Research ArticleA Study on Selection Strategies for Battery Electric VehiclesBased on Sentiments Analysis and the MCDM Model

Xiaosong Ren 1 Sha Sun 1 and Rong Yuan 2

1School of Management Science and Engineering Shanxi University of Finance and Economics Taiyuan 030031 China2College of Management and Economics Chongqing University Chongqing 400044 China

Correspondence should be addressed to Xiaosong Ren renxssxufeeducn

Received 11 March 2021 Revised 3 August 2021 Accepted 9 August 2021 Published 18 August 2021

Academic Editor Maria Angela Butturi

Copyright copy 2021 Xiaosong Ren et alis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Under the goal of carbon peak and carbon neutrality developing battery electric vehicles (BEVs) is an important way to reducecarbon emissions in the transportation sector To popularize BEVs as soon as possible it is necessary to study selection strategiesfor BEVs from the perspective of consumers erefore the Latent Dirichlet Allocation (LDA) model based on fine-grainedsentiment analysis is combined with the multi-criteria decision-making (MCDM) model to assess ten types of BEV alternativesFine-grained sentiment analysis is applied to find the vehicle attributes that consumers care about the most based on the word-of-mouth data e LDA model is suggested to divide topics and construct the indicator system e MCDM model is used to rankvehicles and put forward the corresponding optimization path to increase consumer purchases of BEVs in Chinae results showthat (a) via the LDA model based on fine-grained sentiment analysis attributes that consumers care most about are divided intofive topics dynamics technology safety comfort and cost (b) based on the DEMATEL technique the dimensions in the order ofimportance are as follows safety technology dynamics comfort and cost (c) the price is the most important criteria that affectcustomersrsquo satisfaction by the DANP model and (d) based on the VIKOR model the selection strategies present that Aion S ishighlighted as the best choice and the optimization path is discussed to promote the performance of BEVs to increase customersrsquosatisfactione findings can provide a reference for improving the sustainable development of the automobile industry in Chinae proposed framework serves as the basis for further discussion of BEVs

1 Introduction

With the rapid growth of the number of vehicles the issue ofenergy consumption and greenhouse gas (GHG) emissionsin the transportation sector are attracting increasing at-tention worldwide [1 2] According to the InternationalEnergy Agency the transportation sector accounts for about246 of the worldrsquos energy-related carbon dioxide emis-sions [3] Automobile emissions have been a major source ofemissions in the transportation sector [4ndash6] which aggra-vates the deterioration of the ecological environment andcauses a series of health problems [7ndash9] China owns thelargest automobile market in the world since 2009 [10] andcar ownership has exceeded 200 million by 2020 indicatingthat the issue of energy security and environmental pollutionwill become more prominent [11 12] To alleviate these

problems governments and automobile manufacturers paymore attention to develop cleaner and more efficient al-ternative-fuel vehicles [12 13] which induces the upsurge ofbattery electric vehicles (BEVs) [14ndash17] And BEVs willbecome the mainstream of vehicle sales by 2035 in China[18]

However in the early stage of the development of BEVsonly relying on market forces is not enough for the com-mercialization and popularization of BEVs [19] us theChinese government has formulated a series of incentivepolicies for consumers and BEVmanufacturers [20ndash23] egpurchase subsidies tax exemption free parking and drivingprivileges [24 25] Based on the above incentives from 2011to 2019 the production and sales volume of BEVs increasedfrom 5655 and 5579 to 1020000 and 972000 respectivelyMore importantly as a result of the reduction of subsidies

HindawiMathematical Problems in EngineeringVolume 2021 Article ID 9984343 23 pageshttpsdoiorg10115520219984343

sales of BEVs in July 2019 dropped significantly comparedwith June 2019 with a drop of 599 [26] is means thatthe advantages of policy support offered by BEVs are notenough to persuade consumers [27] Previous studies havehighlighted that the low market share of BEVs is related toconsumersrsquo perceived uncertainty [28 29] whichmeans thatexploring the selection strategies of consumers will helpenlarge the market share of BEVs [30 31]

Existing studies on vehicle selection and comparison aremainly based on life cycle assessment (LCA) [32] statisticalmethods [33] and multicriteria decision-making method(MCDM) [34 35] but the influence of consumer sentimentson vehicle selection strategies is ignored erefore thisstudy attempts to use the Latent Dirichlet Allocation (LDA)model based on the fine-grained sentiment analysis that isto analyze the sentiment polarity of online reviews to revealthe customerrsquos sentiment towards the attributes of BEVsrsquomodels and to extract the attributes that consumers caremost about as evaluation criteria e topic analysis is ap-plied to distinguish dimensions is method considers theindex from the consumerrsquos point of view and effectivelyavoids the problem of artificial selection Based on thisquantitative cause-effect relationships among vehicle attri-butes are included as an important point in BEVsrsquo assess-ment It makes up for the lack of considering the correlationbetween attributes in the existing research Taking ten do-mestic BEVs models in China as research samples a detailedand practical optimization path is put forward which solvesthe shortage that the existing research only considersevaluation and selection

e main tasks of this study include the following as-pects (1) Using consumersrsquo word-of-mouth data fromAutohome website and LDA model based on the fine-grained sentiment analysis a new evaluation index system ofBEVs selection based on customer perspective is con-structed (2) Decision Making Trial and Evaluation Labo-ratory (DEMATEL) technique is utilized in this study toidentify the interaction between criteria within each di-mension Analytic network process (ANP) is then employedto determine the weights of criteria and dimensions (3)Modified Vlse Kriterijuska Optimizacija I KomoromisnoResenje (VIKOR) is applied to calculate the indicator gapsand comprehensive evaluation scores for ten types of BEVsin China Based on the above research framework thecustomer selection strategies and the manufacturer opti-mization paths are proposed e specific analysis process isshown in Figure 1

e remainder of this study is organized as followsSection 2 gives a literature review Section 3 describes themethods Section 4 presents the results Section 5 conducts adiscussion Section 6 presents the conclusions

2 Literature Review

is section briefly reviews the literature from three aspectsapplication of machine-learning methods in the field ofelectric vehicles (EVs) application of MCDM models invehicle selection and the factors influencing consumersrsquochoice of BEVs

21 Application of Machine-Learning Methods in the Field ofEVs With the rise of big data analysis and machinelearning many scholars have applied it in the field of EVsBas et al [36] applied supervised machine-learning tech-niques to identify key elements influencing EVsrsquo adoptionand classify potential EVs purchasers De Clercq et al [37]made use of two machine-learning techniques ie LDAmodel and multi-label classification algorithm to extractsubject words from patent texts and classify patents intomultiple cooperative patent categories Yang et al [38]evaluated and optimized relevant policies of Chinarsquos newenergy vehicle industry based on text mining technologyIn the study of Naumanen et al [39] LDA model was usedto identify emerging research topics according to papersand patents related to heavy duty BEVs Aguilar-Domi-nguez et al [40] used a machine-learning method toevaluate the availability of five vehicles participating in thevehicle-to-home services Basso et al [41] applied theprobabilistic Bayesian machine-learning model to solve theroute selection problem and found the best route for EVsby predicting the energy consumption within the limiteddriving distance Ma et al [42] adopted big data and text-mining technologies to analyze online behavior of Chineseconsumers and to identify the factors affecting consumersrsquopreferences Different from previous research on onlinecomment mining emotion analysis is not only the miningand analysis of consumersrsquo online comments but also theextraction and interpretation of emotions expressed intexts [43] e purpose of sentiment analysis is to inves-tigate the emotion polarity of online texts and divide themas positive neutral and negative [44] However only a fewpapers in the literature apply sentiment analysis to theanalysis of consumersrsquo recognition of vehicle Via big dataplatform Deep Learning techniques were employed byJena [44] to explore and classify consumersrsquo sentimenttowards EVs in India

22 Application of MCDM Models in Vehicle SelectionAccording to previous studies on MCDM MCDM modelscould be classified into the following two categoriese firstcategory is applied to calculate the weights of alternativessuch as the Simultaneous Evaluation of Criteria and Al-ternatives (SECA) [34] Analytical Hierarchy Process (AHP)[35] ANP [45] and Stepwise Weight Assessment RatioAnalysis (SWARA) [46] In the second category the rankingof alternatives is based on comprehensive scores such asMeasurement of Alternatives and Ranking according toCompromise Solution (MARCOS) [34] Complex Propor-tional Assessment (COPRAS) [46] VIKOR [47] Techniquefor Order of Preference by Similarity to Ideal Solution(TOPSIS) [48] ELimination and Choice Expressing theREality (ELECTRE) [49] Preference Ranking OrganizationMethod for Enrichment Evaluation (PROMETHEE) [50]and so on In addition the combination of these methodshas also attracted the attention of academic circles To en-hance the robustness scholars have combined the singlemethod and formed the newly developed hybrid MCDMmodels

2 Mathematical Problems in Engineering

In recent years based on the hybrid MCDM models aseries of scientific and systematic theoretical explorationshave emerged especially in vehicle evaluation and selectionFor example Li et al [47] adopted an MCDM combiningAHP and VIKOR to rank and optimize four types of ve-hicles including EVs gas vehicles methanol vehicles andethanol vehicles to provide references for decision-makersin the new energy automotive industry Using intuitionisticfuzzy set and TOPSIS methods Onat et al [48] evaluated

and ranked the performance scores of alternative vehicleswhich indicated that hybrid electric vehicles were the mostproper choice Das et al [51] developed a fuzzy AHP-EVAMIX hybrid model for evaluating and comparing theperformance of electric vehicles in Asia Tzeng et al [52]developed a hybrid approach including AHP TOPSIS andVIKOR for alternative fuel buses like fuel cell electricity andmethanol e final ranking shows that the hybrid electricbus is the most suitable choice Liang et al [53] presented a

1 e evaluation indicator system is constructed by analyzing online opinions containing the most dissatisfied and the most satisfied emotions of users2 e determination and recognition of the correlation between indicators that influence consumerschoice3 Sort and select BEVs from the perspective of consumers perception of vehicle attributes

QuestionsA Study on Selection Strategy for Battery Electric Vehicles Based on Sentiments Analysis and MCDM Model

Need

Resolve

Data collection

Crawl theword-of-mouth

data inAutohome

website

Emotional polarity analysis Topic analysis Building evaluationindicator system Selection strategies

Building anemotional lexicon

Clustering

Emotional score Keywords

LDA model

Dimensions

Criteria

Analyze the interrelationshipamong dimensions criteria

Calculate the weights ofdimensionscriteria

Obtain the integrated scores ofBEVs and the gaps of indicators

Topic1DynamicsTopic2technologyTopic3SafetyTopic4ComfortTopic5Cost

Topic

Text pre-processing

Sentiments Analysis

Doc2vec model

Skip-gram model

Clustering

DEMATEL technique

MCDM model

DANP method

Modified VIKOR

Based on

e selection strategies of ten BEV alternatives

Discussion

Optimized path for BEVs

Obtain

Indicators

LDA model

Figure 1 Analysis procedure of the study

Mathematical Problems in Engineering 3

fuzzy MCDM assessment model to evaluate and comparealternative-fuel vehicles and the research results show thatbiodiesel vehicles are the best choice

23 e Factors Influencing Consumersrsquo Choice of BEVsVehicle purchasing is closely related to consumersrsquo accep-tance Up to now many studies have explored the possibledrivers or barriers that influence consumersrsquo choice of BEVs[54] Liu et al [55] found that the customer experience is themain decisive factor in buying BEVs Kim et al [56] con-cluded that customers with good driving experiences andknowledge are more likely to buy BEVs in Korea Li et al[57] found in their study that family factors such as scaleincome and location can influence consumersrsquo choice ofBEVs She et al [58] demonstrated that older experiencedand environmentally conscious consumers were more in-terested in buying a BEV in Tianjin Besides safety reli-ability and range were the three key obstacles to BEV salesDong et al [59] recognized that urban consumersrsquo pur-chasing decisions would be changed by psychological factorssuch as subjective norms feelings and emotions personalnorms and perceived behavioural control Li et al [60]argued that fast charging time and battery warranty canpromote consumersrsquo adoption of BEVs Das et al [51]evaluated the performance of EVs based on nine attributessuch as the price battery capacity torque charging timeoverall weight seating capacity driving range top speedand acceleration Nazari et al [31] stressed that the elimi-nation of concerns such as technical uncertainty limitedvehicle styling and charging time will increase the utiliza-tion rate of BEVs In addition to price and battery tech-nology Ma et al [42] suggests that the design of the exteriorand interior has a strong appeal to consumers Kukova et al[61] pointed out that internal space operating reliability andbraking are also important attributes affecting whetherconsumers choose BEVs or not Li et al [24] believed that theimplementation of financial incentives such as purchasesubsidies and tax exemption played an indispensable role inpromoting Chinese consumers to adopt BEVs In the studyof Cheng et al [4] reduction in battery charging time andmaintenance cost were the first two major measures tomotivate consumers to purchase BEVs Although existingstudies have considered the influence of demographictechnological and psychological factors on consumer pur-chasing behavior researchers have shown that consumersrsquodecision to purchase BEVs is largely determined by thevehiclersquos performance characteristics [62 63] erefore inthis study only the influence of vehicle attributes on BEVsselection is considered and other factors are not considered

To sum up although previous studies on vehicle se-lection have made some progress there are still the followinglimitations (1) Some studies have not clearly explained howto select indicators In addition some studies have pointedout that indicators are obtained through literature reviewbut this process is susceptible to subjective factors (2) eindicators are interdependent but many studies have notclearly identified the cause-effect relationship (3) e lit-erature does not provide beneficial guidance for consumers

to choose BEVs in China ere is no mention of the op-timization paths of specific models erefore this studyadopts the LDA model based on fine-grained sentimentanalysis to obtain indicators e MCDM model is used toidentify interrelationships and to propose selection strate-gies and optimization paths for BEVs in China from thepoint of view of consumers

3 Methodology

is research proposes a hybrid model combining fine-grained sentiment analysis and MCDM model to form anovel framework to study consumersrsquo selection strategies forBEVs Specifically the LDA model based on fine-grainedsentiment analysis is applied to identify dimensions andcriteria based on the word-of-mouth data of BEVs inAutohome website DEMATEL technique is used to con-struct an influential network relationship map (INRM)DEMATEL-based Analytic Network Process (DANP) isused to confirm the impact weight of each evaluation in-dicator based on ANP [64] Finally VIKOR is not only usedto evaluate and obtain the selection strategies but also to findthe gaps in each evaluation indicator and make optimalpaths to improve consumersrsquo adoption

31 Sentiment Analysis

311 Text Preprocessing In this study the Scrapy frame-work developed in Python is used to crawl the word-of-mouth data However invalid data in the crawled text willaffect the effectiveness of data output If these invalid dataare introduced into subsequent models it can have a sig-nificant impact on the results of the analysis erefore textpreprocessing should be carried out after obtaining theword-of-mouth data of the Autohome website In this paperthe process of text preprocessing is divided into several stepsincluding data splitting data cleaning text segmentation byjieba clauses removing stop words adding self-definedautomobile dictionary and data transformation

312 Doc2vec Model After preprocessing the data the textdata are divided into a test set and a training set based on acertain ratio en we build a Doc2vec model based on thetraining set data train the test set data with the Doc2vecmodel and finally build a support vector machine classifierto calculate the accuracy of the test set

313 Skip-Gram Model Emotional granularity can be di-vided into two types ie positive and negative emotionalpolarity however to increase the accuracy of the regressionmodel the emotional granularity is further refined ieemotional polarity is divided into five types (very satisfiedsatisfied fair dissatisfied and very dissatisfied) e textpreprocessed data are trained 100 times using word2vec toobtain a Skip-gram model (a method for learning high-dimensional word representations that capture rich se-mantic relationships between words) with a word vectordimension of 200 dimensions e results of the text

4 Mathematical Problems in Engineering

preprocessing are used to obtain the emotion words withhigh word frequency and then the Skip-gram model is usedto obtain words that are similar to the emotion words toobtain a more comprehensive emotion vocabulary

314 Clustering e clustering algorithm divides theemotional lexicon of word-of-mouth into five categoriesbased on the difference between the customersrsquo expectationsand the actual perception of the BEVs

315 LDA Model Topic models are algorithms for dis-covering key topics in a large and unstructured collection oftext [65] LDA is one of the topic models which is applied toautomatically discover topics in the text that consumers aremost satisfied and least satisfied with e core computa-tional problem for topic models is to use the collected text toinfer the hidden topic structure [66] us this studyidentifies the consumer concerns by using the LDA modelto discover key topics from the collected text

32 MCDM Model

321 DEMATEL Technique DEMATEL technique is asystematic factor analysis method to detect the cause-effectrelationships between complicated indicators using graphtheory and matrix tools [67] originally proposed by theBattelle Research Centre in 1972 [68] DEMATEL has beenused to solve complicated real-world problems by buildingan INRM [69] such as optimal online travel agencies [70]regional innovation capacity [71] and sustainable onlineconsumption [72] us the steps of this technique aresummarized as follows

Step 1 Finding the average direct effect matrixe mutual direct effect among criteria is evaluated by

the knowledge-based experts e scales ranged from 0 to 4where ldquo0rdquo means ldquoabsolutely no effectrdquo and ldquo4rdquo means ldquoveryhigh effectrdquo ldquo1rdquo ldquo2rdquo and ldquo3rdquo mean ldquolow effectrdquo ldquomiddleeffectrdquo and ldquohigh effectrdquo respectively By a pairwise com-parison we can obtain these groups of direct matrices byscores where ij represents the influence from criterion i tocriterion j After that we can calculate an average directeffect matrixG (as seen in equation (1)) where each criterionis the average of the corresponding criteria in the expertsrsquodirect matrices [73]

G

g11c g

1jc g

1nc

⋮ ⋮ ⋮

gi1c g

ijc g

inD

⋮ ⋮ ⋮

gn1c g

njc g

nnc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(1)

Step 1 Setting up the normalized direct-influence matrix Xe matrix X can be acquired by using equations (2) and

(3)

X S times G Sgt 0 (2)

where

S min i j1

maxi 1113936nj1 g

ijc

⎧⎨

⎩ 1

maxj 1113936ni1 g

ijc

⎫⎬

⎭ i j isin 1 2 n

(3)

Step 1 Computing the total influence matrix Tce total influence matrix Tc can be derived from

equation (4) where matrix I denotes a unit matrix

Tc X + X2

+ X3

+ middot middot middot + Xθ

X I + X + X2

+ middot middot middot + Xθminus1

1113872 1113873(I minus X)(I minus X)minus1

X I minus Xθ

1113872 1113873(I minus X)minus1

X(I minus X)minus1

(4)

where X [xijc ]ntimesn 0le [x

ijc ]le 1 0lt 1113936

nj1 x

ijc le 1 and

0lt 1113936ni1 x

ijc le 1 and at least the sum of one row or column

(but not all) equals one limθ⟶infinXθ [0]ntimesn

Step 4 Building the INRM and analyzing the resultse sum of rows and the sum of columns of total in-

fluencematrix Tc can be respectively represented by vector rand vector s according to equations (5)ndash(6) where ri in-dicates the total influence of criterion i on others the sidenotes the total influences received by criterion j from othercriteria When i j and i j isin 1 2 n the vector (ri+ si)expresses the importance of criterion i in the questionLikewise the vector (ri minus si) identifies the degree of causalityamong indicators Simultaneously if (ri minus si) is positive thecriterion i influences other criteria On the contrary if(ri minus si) is negative the criterion i is affected by others Fi-nally draw the INRM in which the vertical axis represents(ri+ si) and the horizontal axis represents (ri minus si) [74]

Tc tijc1113960 1113961

ntimesn i j isin 1 2 n

r 1113944n

j1tijc

⎡⎢⎢⎣ ⎤⎥⎥⎦

ntimes1

tic1113960 1113961

ntimes1 r1 ri rn( 1113857prime(5)

s 1113944n

i1tijc

⎡⎣ ⎤⎦

1timesn

tic1113960 11139611timesn

s1 si sn( 1113857prime (6)

e total influence matrices has two forms one is Tc [tijc]ntimesn (equation (7)) where n represents the number of thecriteria and the other is TD [tij D]mtimesm (equation (8))where m represents the number of dimensions

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTc =

Dj

Dm

Dm

D1

D1

Tc11 Tc

1j Tc1n

Tcn1 Tcc

nj Tcnn

Tci1 Tcc

ij Tcin

c1n1 cj1 cjnj cm1 cmnm

(7)

Mathematical Problems in Engineering 5

322 DANP Method e ANP method was first proposedby Saaty [64] to deal with the interdependence and feedbackproblems among indicators [75 76] It originates from theAHP but eliminates the deficiency of AHP which assumesthat the indicators are independent of each other [77]However the weighted super-matrix in the ANP methodlacks rationality because it assumes that each cluster has thesame weight [78] erefore the DANP is an appropriatemethod to obtain the influential weights by improving thenormalization process and addressing the interrelationshipsamong indicators [45] It has been used in many differentfields such as low-carbon energy planning [79] materialselection [80] and renewable energy selection [81]us theprocess of this technique involves the following steps

Step 1 Calculating the normalized total-influential matrixTnor D

According to equations (8) and (9) matrix Tnor D isframed through normalizing the total-influential matrix TDFirst the sum of each row in matrix TD can be expressed astiD 1113936

mj1 t

ijD wherem represents the number of dimensions

en the normalized total-influential matrix Tnor D iscalculated by dividing the elements in each row by the sumof the row so that Tnor D [tij DtiD]mtimesm Meanwhile thesum of each row in matrix Tnor D equals one so that1113936

mj1 t

norij

D 1

TD

t11D t

1jD t

1mD

ti1D t

ijD t

imD

tm1D t

mjD t

mmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⟶ 1113944m

j1tijD t

iD (8)

TnorD

t11D

t1D

t1jD

t1D

t1mD

t1D

⋮ ⋮ ⋮

ti1D

tiD

tijD

tiD

timD

tiD

⋮ ⋮ ⋮

tm1D

tmD

tmjD

tmD

tmmD

tmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

Step 2 Exporting the normalized matrix Tnor c by dimen-sions and clusters

A new matrix Tnor c is acquired by normalizing Tc withthe total degrees of effect and influence of the dimensionsand clusters

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTcnor =

Dj

Dm

Dm

D1

D1

Tcnor11 Tc

nor1j Tcnor1n

Tcnorn1 Tcc

nornj Tcnornn

Tcnori1 Tcc

norij Tcnorin

c1n1 cj1 cjnj cm1 cmnm

(10)

Step 3 Determining the unweighted super-matrix Wce unweighted super-matrix Wc is obtained by trans-

posing the normalized matrix Tnor c

cmnm

cini

c1n1

cm1

ci1

c11c11

DiWc = (Tcnor)prime=

Dj

Dm

Dm

D1

D1

W11 Wi1 Wn1

W1n Win Wnn

W1j Wij Wnj

c1n1 cj1 cjnji cm1 cmnm

(11)

Step 4 Constructing the weighted super-matrix W lowast ce normalized total-influential matrix Tnor D is ob-

tained by equation (9) and the unweighted super-matrixWcis obtained by equation (11) us using equation (12) aweighted super-matrix W lowast c which improves the tradi-tional ANP by using equal weights to make it appropriate forthe real world can be obtained by the product of Tnor c andWc ie W lowast cTnor D lowast Wc is demonstrates that theinfluential level values are the basis of normalization todetermine a weighted super-matrix

Wlowastc T

norD Wc

tnor11D times W

11c t

nori1D times W

i1c t

norm1D times W

m1c

⋮ ⋮

tnor1j

D times W1jc t

norij

D times Wijc t

normj

D times Wmjc

⋮ ⋮ ⋮

tnor1m

D times W1mc t

norim

D times Wimc t

normm

D times Wmmc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)

6 Mathematical Problems in Engineering

Step 5 Calculating the influential weights of the criteriae influential weights w (w1 wj wn) can be

obtained according to the weighted super-matrixW lowast c andmultiplied several times until it converges a stable super-matrix so that limα⟶infin (W lowast c)α where α is a positiveinteger number

323 VIKOR Method e VIKOR method was first pro-posed by Opricovic to optimize the multiple criteria ofcomplicated systems in 1998 [82] It is applied to rank andselect from a set of alternatives given the conflicting criteriaVIKOR is also the compromise ranking method based on theconcept of the Positive-ideal (or the aspired level) solutionand Negative-ideal (or the worst level) solution [83] So theorder of results can be compared by ldquoproximityrdquo to the ldquoidealrdquoalternative [82 84 85] In this study the modified VIKORmethod can be used to increase customersrsquo satisfaction inalternatives that are influenced by the interaction of variousfactors All of the steps for VIKOR are presented as follows

Step 1 Determining the positive-ideal solution negative-ideal solution and the gap

According to the concepts of VIKOR flowast j is the positive-ideal point of assessment criteria which indicates the bestvalue (aspiration level) In contrast fminus j is a negative-idealpoint which means the worst value In this study the bestvalue is set as flowast j 10 [64] Likewise the worst value is set asfminus j 0 with scores of criteria ranging from 0 (dissatisfied) to10 (satisfied) is is different from the traditional VIKORin which the positive-ideal solution is set as the maximum ofall schemes ie flowast jmax fkj|k 1 2 K and thenegative-ideal solution is set as the minimum of all schemesie fminus jmin fkj|k 1 2 K en we can obtain thegap ratio as is described in equation (13)

zkj flowastj minus fkj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

flowastj minus f

minusj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

(13)

Step 2 Calculating the average gap Ek and maximal gap Qkfor prioritizing improvement

e general form of the Lp-metric function is introduced asfollows

Lp

k 1113944n

j1wj times zkj1113960 1113961

p⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭

1p

1lepleinfin k 1 2 k

(14)

where wj is the influential weight generated from the DANPL

p1k and L

pinfink are separately expressed as Ek and Qk which

can be calculated by equations (15) and (16)

Ek Lp1k 1113944

n

j1wj times zkj (15)

Qk Lp⟶infink max j zkj|j 1 2 n1113966 1113967 (16)

e compromise solution minkLp k indicates that thesynthesized gap should be minimized e average gap isemphasized when p is equal to one However Qk means themaximum gap of overall criteria in alternative k When p isinfinite the maximal gaps should be improved by thepriority

Step 3 Exporting comprehensively evaluated values of thealternatives

Uk λEk minus E

lowast( 1113857

Eminus

minus Elowast

( 1113857+(1 minus λ)

Qk minus Qlowast

( 1113857

Qminus

minus Qlowast

( 1113857 (17)

For equation (15) the best gap E lowast k and the worst valueEminus k is expressed as E lowast kmink Ek and Eminus kmaxk Ekrespectively For equation (16) the best gap Q lowast k and theworst value Qminus k is separately expressed as Q lowast kmink Qkand Qminus kmaxk Qk Furthermore in optimal conditionsE lowast k 0 Q lowast k 0 and at worse Eminus k 1 Qminus k 1 enequation (17) can be simplified as follows

Uk λEk +(1 minus λ)Qk (18)

e range of values for λ is zero to one λgt 05 means theanalysis emphasizes the average gap more λlt 05 indicatesthe analysis is more concerned about the maximum gap forpriority improvement In general we can set λ 05

4 Empirical Results

Based on the above models ten types of BEVs are carried outas shown in Table 1 e empirical results of the analyticalprocess are as follows

41 Building Evaluation Indicator System e online word-of-mouth of BEVs is from the Autohome website (seehttpswwwautohomecomcn) which is a relatively well-known auto website in China Word-of-mouth is the key forusers to express their views On the Autohome website theword-of-mouth data reflect vehicle attributes that users aremost concerned about Because the website has strict re-quirements on the opinions all the text information isaccurate and of high quality

411 Emotional Polarity Analysis First emotional lexiconis extracted from the text that has been preprocessed Table 2shows the emotional words with high word frequency It canbe found that the word frequency of the emotional wordssuch as right fine smooth not bad not so dusty highcomfortable stable and so on is very high which indicatedthat consumersrsquo overall cognition of BEVs is relativelyconcentrated

On the Autohome website the overall image of a BEV isoften measured in terms of the customersrsquo satisfaction withthe BEV en the emotional terms of customersrsquo satis-faction are obtained through cluster analysis as is shown inTable 3e overall satisfaction of customers with the BEV ismeasured by the emotion words that indicate emotion306 of the total number of word-of-mouth have

Mathematical Problems in Engineering 7

In recent years based on the hybrid MCDM models aseries of scientific and systematic theoretical explorationshave emerged especially in vehicle evaluation and selectionFor example Li et al [47] adopted an MCDM combiningAHP and VIKOR to rank and optimize four types of ve-hicles including EVs gas vehicles methanol vehicles andethanol vehicles to provide references for decision-makersin the new energy automotive industry Using intuitionisticfuzzy set and TOPSIS methods Onat et al [48] evaluated

and ranked the performance scores of alternative vehicleswhich indicated that hybrid electric vehicles were the mostproper choice Das et al [51] developed a fuzzy AHP-EVAMIX hybrid model for evaluating and comparing theperformance of electric vehicles in Asia Tzeng et al [52]developed a hybrid approach including AHP TOPSIS andVIKOR for alternative fuel buses like fuel cell electricity andmethanol e final ranking shows that the hybrid electricbus is the most suitable choice Liang et al [53] presented a

1 e evaluation indicator system is constructed by analyzing online opinions containing the most dissatisfied and the most satisfied emotions of users2 e determination and recognition of the correlation between indicators that influence consumerschoice3 Sort and select BEVs from the perspective of consumers perception of vehicle attributes

QuestionsA Study on Selection Strategy for Battery Electric Vehicles Based on Sentiments Analysis and MCDM Model

Need

Resolve

Data collection

Crawl theword-of-mouth

data inAutohome

website

Emotional polarity analysis Topic analysis Building evaluationindicator system Selection strategies

Building anemotional lexicon

Clustering

Emotional score Keywords

LDA model

Dimensions

Criteria

Analyze the interrelationshipamong dimensions criteria

Calculate the weights ofdimensionscriteria

Obtain the integrated scores ofBEVs and the gaps of indicators

Topic1DynamicsTopic2technologyTopic3SafetyTopic4ComfortTopic5Cost

Topic

Text pre-processing

Sentiments Analysis

Doc2vec model

Skip-gram model

Clustering

DEMATEL technique

MCDM model

DANP method

Modified VIKOR

Based on

e selection strategies of ten BEV alternatives

Discussion

Optimized path for BEVs

Obtain

Indicators

LDA model

Figure 1 Analysis procedure of the study

Mathematical Problems in Engineering 3

fuzzy MCDM assessment model to evaluate and comparealternative-fuel vehicles and the research results show thatbiodiesel vehicles are the best choice

23 e Factors Influencing Consumersrsquo Choice of BEVsVehicle purchasing is closely related to consumersrsquo accep-tance Up to now many studies have explored the possibledrivers or barriers that influence consumersrsquo choice of BEVs[54] Liu et al [55] found that the customer experience is themain decisive factor in buying BEVs Kim et al [56] con-cluded that customers with good driving experiences andknowledge are more likely to buy BEVs in Korea Li et al[57] found in their study that family factors such as scaleincome and location can influence consumersrsquo choice ofBEVs She et al [58] demonstrated that older experiencedand environmentally conscious consumers were more in-terested in buying a BEV in Tianjin Besides safety reli-ability and range were the three key obstacles to BEV salesDong et al [59] recognized that urban consumersrsquo pur-chasing decisions would be changed by psychological factorssuch as subjective norms feelings and emotions personalnorms and perceived behavioural control Li et al [60]argued that fast charging time and battery warranty canpromote consumersrsquo adoption of BEVs Das et al [51]evaluated the performance of EVs based on nine attributessuch as the price battery capacity torque charging timeoverall weight seating capacity driving range top speedand acceleration Nazari et al [31] stressed that the elimi-nation of concerns such as technical uncertainty limitedvehicle styling and charging time will increase the utiliza-tion rate of BEVs In addition to price and battery tech-nology Ma et al [42] suggests that the design of the exteriorand interior has a strong appeal to consumers Kukova et al[61] pointed out that internal space operating reliability andbraking are also important attributes affecting whetherconsumers choose BEVs or not Li et al [24] believed that theimplementation of financial incentives such as purchasesubsidies and tax exemption played an indispensable role inpromoting Chinese consumers to adopt BEVs In the studyof Cheng et al [4] reduction in battery charging time andmaintenance cost were the first two major measures tomotivate consumers to purchase BEVs Although existingstudies have considered the influence of demographictechnological and psychological factors on consumer pur-chasing behavior researchers have shown that consumersrsquodecision to purchase BEVs is largely determined by thevehiclersquos performance characteristics [62 63] erefore inthis study only the influence of vehicle attributes on BEVsselection is considered and other factors are not considered

To sum up although previous studies on vehicle se-lection have made some progress there are still the followinglimitations (1) Some studies have not clearly explained howto select indicators In addition some studies have pointedout that indicators are obtained through literature reviewbut this process is susceptible to subjective factors (2) eindicators are interdependent but many studies have notclearly identified the cause-effect relationship (3) e lit-erature does not provide beneficial guidance for consumers

to choose BEVs in China ere is no mention of the op-timization paths of specific models erefore this studyadopts the LDA model based on fine-grained sentimentanalysis to obtain indicators e MCDM model is used toidentify interrelationships and to propose selection strate-gies and optimization paths for BEVs in China from thepoint of view of consumers

3 Methodology

is research proposes a hybrid model combining fine-grained sentiment analysis and MCDM model to form anovel framework to study consumersrsquo selection strategies forBEVs Specifically the LDA model based on fine-grainedsentiment analysis is applied to identify dimensions andcriteria based on the word-of-mouth data of BEVs inAutohome website DEMATEL technique is used to con-struct an influential network relationship map (INRM)DEMATEL-based Analytic Network Process (DANP) isused to confirm the impact weight of each evaluation in-dicator based on ANP [64] Finally VIKOR is not only usedto evaluate and obtain the selection strategies but also to findthe gaps in each evaluation indicator and make optimalpaths to improve consumersrsquo adoption

31 Sentiment Analysis

311 Text Preprocessing In this study the Scrapy frame-work developed in Python is used to crawl the word-of-mouth data However invalid data in the crawled text willaffect the effectiveness of data output If these invalid dataare introduced into subsequent models it can have a sig-nificant impact on the results of the analysis erefore textpreprocessing should be carried out after obtaining theword-of-mouth data of the Autohome website In this paperthe process of text preprocessing is divided into several stepsincluding data splitting data cleaning text segmentation byjieba clauses removing stop words adding self-definedautomobile dictionary and data transformation

312 Doc2vec Model After preprocessing the data the textdata are divided into a test set and a training set based on acertain ratio en we build a Doc2vec model based on thetraining set data train the test set data with the Doc2vecmodel and finally build a support vector machine classifierto calculate the accuracy of the test set

313 Skip-Gram Model Emotional granularity can be di-vided into two types ie positive and negative emotionalpolarity however to increase the accuracy of the regressionmodel the emotional granularity is further refined ieemotional polarity is divided into five types (very satisfiedsatisfied fair dissatisfied and very dissatisfied) e textpreprocessed data are trained 100 times using word2vec toobtain a Skip-gram model (a method for learning high-dimensional word representations that capture rich se-mantic relationships between words) with a word vectordimension of 200 dimensions e results of the text

4 Mathematical Problems in Engineering

preprocessing are used to obtain the emotion words withhigh word frequency and then the Skip-gram model is usedto obtain words that are similar to the emotion words toobtain a more comprehensive emotion vocabulary

314 Clustering e clustering algorithm divides theemotional lexicon of word-of-mouth into five categoriesbased on the difference between the customersrsquo expectationsand the actual perception of the BEVs

315 LDA Model Topic models are algorithms for dis-covering key topics in a large and unstructured collection oftext [65] LDA is one of the topic models which is applied toautomatically discover topics in the text that consumers aremost satisfied and least satisfied with e core computa-tional problem for topic models is to use the collected text toinfer the hidden topic structure [66] us this studyidentifies the consumer concerns by using the LDA modelto discover key topics from the collected text

32 MCDM Model

321 DEMATEL Technique DEMATEL technique is asystematic factor analysis method to detect the cause-effectrelationships between complicated indicators using graphtheory and matrix tools [67] originally proposed by theBattelle Research Centre in 1972 [68] DEMATEL has beenused to solve complicated real-world problems by buildingan INRM [69] such as optimal online travel agencies [70]regional innovation capacity [71] and sustainable onlineconsumption [72] us the steps of this technique aresummarized as follows

Step 1 Finding the average direct effect matrixe mutual direct effect among criteria is evaluated by

the knowledge-based experts e scales ranged from 0 to 4where ldquo0rdquo means ldquoabsolutely no effectrdquo and ldquo4rdquo means ldquoveryhigh effectrdquo ldquo1rdquo ldquo2rdquo and ldquo3rdquo mean ldquolow effectrdquo ldquomiddleeffectrdquo and ldquohigh effectrdquo respectively By a pairwise com-parison we can obtain these groups of direct matrices byscores where ij represents the influence from criterion i tocriterion j After that we can calculate an average directeffect matrixG (as seen in equation (1)) where each criterionis the average of the corresponding criteria in the expertsrsquodirect matrices [73]

G

g11c g

1jc g

1nc

⋮ ⋮ ⋮

gi1c g

ijc g

inD

⋮ ⋮ ⋮

gn1c g

njc g

nnc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(1)

Step 1 Setting up the normalized direct-influence matrix Xe matrix X can be acquired by using equations (2) and

(3)

X S times G Sgt 0 (2)

where

S min i j1

maxi 1113936nj1 g

ijc

⎧⎨

⎩ 1

maxj 1113936ni1 g

ijc

⎫⎬

⎭ i j isin 1 2 n

(3)

Step 1 Computing the total influence matrix Tce total influence matrix Tc can be derived from

equation (4) where matrix I denotes a unit matrix

Tc X + X2

+ X3

+ middot middot middot + Xθ

X I + X + X2

+ middot middot middot + Xθminus1

1113872 1113873(I minus X)(I minus X)minus1

X I minus Xθ

1113872 1113873(I minus X)minus1

X(I minus X)minus1

(4)

where X [xijc ]ntimesn 0le [x

ijc ]le 1 0lt 1113936

nj1 x

ijc le 1 and

0lt 1113936ni1 x

ijc le 1 and at least the sum of one row or column

(but not all) equals one limθ⟶infinXθ [0]ntimesn

Step 4 Building the INRM and analyzing the resultse sum of rows and the sum of columns of total in-

fluencematrix Tc can be respectively represented by vector rand vector s according to equations (5)ndash(6) where ri in-dicates the total influence of criterion i on others the sidenotes the total influences received by criterion j from othercriteria When i j and i j isin 1 2 n the vector (ri+ si)expresses the importance of criterion i in the questionLikewise the vector (ri minus si) identifies the degree of causalityamong indicators Simultaneously if (ri minus si) is positive thecriterion i influences other criteria On the contrary if(ri minus si) is negative the criterion i is affected by others Fi-nally draw the INRM in which the vertical axis represents(ri+ si) and the horizontal axis represents (ri minus si) [74]

Tc tijc1113960 1113961

ntimesn i j isin 1 2 n

r 1113944n

j1tijc

⎡⎢⎢⎣ ⎤⎥⎥⎦

ntimes1

tic1113960 1113961

ntimes1 r1 ri rn( 1113857prime(5)

s 1113944n

i1tijc

⎡⎣ ⎤⎦

1timesn

tic1113960 11139611timesn

s1 si sn( 1113857prime (6)

e total influence matrices has two forms one is Tc [tijc]ntimesn (equation (7)) where n represents the number of thecriteria and the other is TD [tij D]mtimesm (equation (8))where m represents the number of dimensions

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTc =

Dj

Dm

Dm

D1

D1

Tc11 Tc

1j Tc1n

Tcn1 Tcc

nj Tcnn

Tci1 Tcc

ij Tcin

c1n1 cj1 cjnj cm1 cmnm

(7)

Mathematical Problems in Engineering 5

322 DANP Method e ANP method was first proposedby Saaty [64] to deal with the interdependence and feedbackproblems among indicators [75 76] It originates from theAHP but eliminates the deficiency of AHP which assumesthat the indicators are independent of each other [77]However the weighted super-matrix in the ANP methodlacks rationality because it assumes that each cluster has thesame weight [78] erefore the DANP is an appropriatemethod to obtain the influential weights by improving thenormalization process and addressing the interrelationshipsamong indicators [45] It has been used in many differentfields such as low-carbon energy planning [79] materialselection [80] and renewable energy selection [81]us theprocess of this technique involves the following steps

Step 1 Calculating the normalized total-influential matrixTnor D

According to equations (8) and (9) matrix Tnor D isframed through normalizing the total-influential matrix TDFirst the sum of each row in matrix TD can be expressed astiD 1113936

mj1 t

ijD wherem represents the number of dimensions

en the normalized total-influential matrix Tnor D iscalculated by dividing the elements in each row by the sumof the row so that Tnor D [tij DtiD]mtimesm Meanwhile thesum of each row in matrix Tnor D equals one so that1113936

mj1 t

norij

D 1

TD

t11D t

1jD t

1mD

ti1D t

ijD t

imD

tm1D t

mjD t

mmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⟶ 1113944m

j1tijD t

iD (8)

TnorD

t11D

t1D

t1jD

t1D

t1mD

t1D

⋮ ⋮ ⋮

ti1D

tiD

tijD

tiD

timD

tiD

⋮ ⋮ ⋮

tm1D

tmD

tmjD

tmD

tmmD

tmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

Step 2 Exporting the normalized matrix Tnor c by dimen-sions and clusters

A new matrix Tnor c is acquired by normalizing Tc withthe total degrees of effect and influence of the dimensionsand clusters

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTcnor =

Dj

Dm

Dm

D1

D1

Tcnor11 Tc

nor1j Tcnor1n

Tcnorn1 Tcc

nornj Tcnornn

Tcnori1 Tcc

norij Tcnorin

c1n1 cj1 cjnj cm1 cmnm

(10)

Step 3 Determining the unweighted super-matrix Wce unweighted super-matrix Wc is obtained by trans-

posing the normalized matrix Tnor c

cmnm

cini

c1n1

cm1

ci1

c11c11

DiWc = (Tcnor)prime=

Dj

Dm

Dm

D1

D1

W11 Wi1 Wn1

W1n Win Wnn

W1j Wij Wnj

c1n1 cj1 cjnji cm1 cmnm

(11)

Step 4 Constructing the weighted super-matrix W lowast ce normalized total-influential matrix Tnor D is ob-

tained by equation (9) and the unweighted super-matrixWcis obtained by equation (11) us using equation (12) aweighted super-matrix W lowast c which improves the tradi-tional ANP by using equal weights to make it appropriate forthe real world can be obtained by the product of Tnor c andWc ie W lowast cTnor D lowast Wc is demonstrates that theinfluential level values are the basis of normalization todetermine a weighted super-matrix

Wlowastc T

norD Wc

tnor11D times W

11c t

nori1D times W

i1c t

norm1D times W

m1c

⋮ ⋮

tnor1j

D times W1jc t

norij

D times Wijc t

normj

D times Wmjc

⋮ ⋮ ⋮

tnor1m

D times W1mc t

norim

D times Wimc t

normm

D times Wmmc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)

6 Mathematical Problems in Engineering

Step 5 Calculating the influential weights of the criteriae influential weights w (w1 wj wn) can be

obtained according to the weighted super-matrixW lowast c andmultiplied several times until it converges a stable super-matrix so that limα⟶infin (W lowast c)α where α is a positiveinteger number

323 VIKOR Method e VIKOR method was first pro-posed by Opricovic to optimize the multiple criteria ofcomplicated systems in 1998 [82] It is applied to rank andselect from a set of alternatives given the conflicting criteriaVIKOR is also the compromise ranking method based on theconcept of the Positive-ideal (or the aspired level) solutionand Negative-ideal (or the worst level) solution [83] So theorder of results can be compared by ldquoproximityrdquo to the ldquoidealrdquoalternative [82 84 85] In this study the modified VIKORmethod can be used to increase customersrsquo satisfaction inalternatives that are influenced by the interaction of variousfactors All of the steps for VIKOR are presented as follows

Step 1 Determining the positive-ideal solution negative-ideal solution and the gap

According to the concepts of VIKOR flowast j is the positive-ideal point of assessment criteria which indicates the bestvalue (aspiration level) In contrast fminus j is a negative-idealpoint which means the worst value In this study the bestvalue is set as flowast j 10 [64] Likewise the worst value is set asfminus j 0 with scores of criteria ranging from 0 (dissatisfied) to10 (satisfied) is is different from the traditional VIKORin which the positive-ideal solution is set as the maximum ofall schemes ie flowast jmax fkj|k 1 2 K and thenegative-ideal solution is set as the minimum of all schemesie fminus jmin fkj|k 1 2 K en we can obtain thegap ratio as is described in equation (13)

zkj flowastj minus fkj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

flowastj minus f

minusj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

(13)

Step 2 Calculating the average gap Ek and maximal gap Qkfor prioritizing improvement

e general form of the Lp-metric function is introduced asfollows

Lp

k 1113944n

j1wj times zkj1113960 1113961

p⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭

1p

1lepleinfin k 1 2 k

(14)

where wj is the influential weight generated from the DANPL

p1k and L

pinfink are separately expressed as Ek and Qk which

can be calculated by equations (15) and (16)

Ek Lp1k 1113944

n

j1wj times zkj (15)

Qk Lp⟶infink max j zkj|j 1 2 n1113966 1113967 (16)

e compromise solution minkLp k indicates that thesynthesized gap should be minimized e average gap isemphasized when p is equal to one However Qk means themaximum gap of overall criteria in alternative k When p isinfinite the maximal gaps should be improved by thepriority

Step 3 Exporting comprehensively evaluated values of thealternatives

Uk λEk minus E

lowast( 1113857

Eminus

minus Elowast

( 1113857+(1 minus λ)

Qk minus Qlowast

( 1113857

Qminus

minus Qlowast

( 1113857 (17)

For equation (15) the best gap E lowast k and the worst valueEminus k is expressed as E lowast kmink Ek and Eminus kmaxk Ekrespectively For equation (16) the best gap Q lowast k and theworst value Qminus k is separately expressed as Q lowast kmink Qkand Qminus kmaxk Qk Furthermore in optimal conditionsE lowast k 0 Q lowast k 0 and at worse Eminus k 1 Qminus k 1 enequation (17) can be simplified as follows

Uk λEk +(1 minus λ)Qk (18)

e range of values for λ is zero to one λgt 05 means theanalysis emphasizes the average gap more λlt 05 indicatesthe analysis is more concerned about the maximum gap forpriority improvement In general we can set λ 05

4 Empirical Results

Based on the above models ten types of BEVs are carried outas shown in Table 1 e empirical results of the analyticalprocess are as follows

41 Building Evaluation Indicator System e online word-of-mouth of BEVs is from the Autohome website (seehttpswwwautohomecomcn) which is a relatively well-known auto website in China Word-of-mouth is the key forusers to express their views On the Autohome website theword-of-mouth data reflect vehicle attributes that users aremost concerned about Because the website has strict re-quirements on the opinions all the text information isaccurate and of high quality

411 Emotional Polarity Analysis First emotional lexiconis extracted from the text that has been preprocessed Table 2shows the emotional words with high word frequency It canbe found that the word frequency of the emotional wordssuch as right fine smooth not bad not so dusty highcomfortable stable and so on is very high which indicatedthat consumersrsquo overall cognition of BEVs is relativelyconcentrated

On the Autohome website the overall image of a BEV isoften measured in terms of the customersrsquo satisfaction withthe BEV en the emotional terms of customersrsquo satis-faction are obtained through cluster analysis as is shown inTable 3e overall satisfaction of customers with the BEV ismeasured by the emotion words that indicate emotion306 of the total number of word-of-mouth have

Mathematical Problems in Engineering 7

fuzzy MCDM assessment model to evaluate and comparealternative-fuel vehicles and the research results show thatbiodiesel vehicles are the best choice

23 e Factors Influencing Consumersrsquo Choice of BEVsVehicle purchasing is closely related to consumersrsquo accep-tance Up to now many studies have explored the possibledrivers or barriers that influence consumersrsquo choice of BEVs[54] Liu et al [55] found that the customer experience is themain decisive factor in buying BEVs Kim et al [56] con-cluded that customers with good driving experiences andknowledge are more likely to buy BEVs in Korea Li et al[57] found in their study that family factors such as scaleincome and location can influence consumersrsquo choice ofBEVs She et al [58] demonstrated that older experiencedand environmentally conscious consumers were more in-terested in buying a BEV in Tianjin Besides safety reli-ability and range were the three key obstacles to BEV salesDong et al [59] recognized that urban consumersrsquo pur-chasing decisions would be changed by psychological factorssuch as subjective norms feelings and emotions personalnorms and perceived behavioural control Li et al [60]argued that fast charging time and battery warranty canpromote consumersrsquo adoption of BEVs Das et al [51]evaluated the performance of EVs based on nine attributessuch as the price battery capacity torque charging timeoverall weight seating capacity driving range top speedand acceleration Nazari et al [31] stressed that the elimi-nation of concerns such as technical uncertainty limitedvehicle styling and charging time will increase the utiliza-tion rate of BEVs In addition to price and battery tech-nology Ma et al [42] suggests that the design of the exteriorand interior has a strong appeal to consumers Kukova et al[61] pointed out that internal space operating reliability andbraking are also important attributes affecting whetherconsumers choose BEVs or not Li et al [24] believed that theimplementation of financial incentives such as purchasesubsidies and tax exemption played an indispensable role inpromoting Chinese consumers to adopt BEVs In the studyof Cheng et al [4] reduction in battery charging time andmaintenance cost were the first two major measures tomotivate consumers to purchase BEVs Although existingstudies have considered the influence of demographictechnological and psychological factors on consumer pur-chasing behavior researchers have shown that consumersrsquodecision to purchase BEVs is largely determined by thevehiclersquos performance characteristics [62 63] erefore inthis study only the influence of vehicle attributes on BEVsselection is considered and other factors are not considered

To sum up although previous studies on vehicle se-lection have made some progress there are still the followinglimitations (1) Some studies have not clearly explained howto select indicators In addition some studies have pointedout that indicators are obtained through literature reviewbut this process is susceptible to subjective factors (2) eindicators are interdependent but many studies have notclearly identified the cause-effect relationship (3) e lit-erature does not provide beneficial guidance for consumers

to choose BEVs in China ere is no mention of the op-timization paths of specific models erefore this studyadopts the LDA model based on fine-grained sentimentanalysis to obtain indicators e MCDM model is used toidentify interrelationships and to propose selection strate-gies and optimization paths for BEVs in China from thepoint of view of consumers

3 Methodology

is research proposes a hybrid model combining fine-grained sentiment analysis and MCDM model to form anovel framework to study consumersrsquo selection strategies forBEVs Specifically the LDA model based on fine-grainedsentiment analysis is applied to identify dimensions andcriteria based on the word-of-mouth data of BEVs inAutohome website DEMATEL technique is used to con-struct an influential network relationship map (INRM)DEMATEL-based Analytic Network Process (DANP) isused to confirm the impact weight of each evaluation in-dicator based on ANP [64] Finally VIKOR is not only usedto evaluate and obtain the selection strategies but also to findthe gaps in each evaluation indicator and make optimalpaths to improve consumersrsquo adoption

31 Sentiment Analysis

311 Text Preprocessing In this study the Scrapy frame-work developed in Python is used to crawl the word-of-mouth data However invalid data in the crawled text willaffect the effectiveness of data output If these invalid dataare introduced into subsequent models it can have a sig-nificant impact on the results of the analysis erefore textpreprocessing should be carried out after obtaining theword-of-mouth data of the Autohome website In this paperthe process of text preprocessing is divided into several stepsincluding data splitting data cleaning text segmentation byjieba clauses removing stop words adding self-definedautomobile dictionary and data transformation

312 Doc2vec Model After preprocessing the data the textdata are divided into a test set and a training set based on acertain ratio en we build a Doc2vec model based on thetraining set data train the test set data with the Doc2vecmodel and finally build a support vector machine classifierto calculate the accuracy of the test set

313 Skip-Gram Model Emotional granularity can be di-vided into two types ie positive and negative emotionalpolarity however to increase the accuracy of the regressionmodel the emotional granularity is further refined ieemotional polarity is divided into five types (very satisfiedsatisfied fair dissatisfied and very dissatisfied) e textpreprocessed data are trained 100 times using word2vec toobtain a Skip-gram model (a method for learning high-dimensional word representations that capture rich se-mantic relationships between words) with a word vectordimension of 200 dimensions e results of the text

4 Mathematical Problems in Engineering

preprocessing are used to obtain the emotion words withhigh word frequency and then the Skip-gram model is usedto obtain words that are similar to the emotion words toobtain a more comprehensive emotion vocabulary

314 Clustering e clustering algorithm divides theemotional lexicon of word-of-mouth into five categoriesbased on the difference between the customersrsquo expectationsand the actual perception of the BEVs

315 LDA Model Topic models are algorithms for dis-covering key topics in a large and unstructured collection oftext [65] LDA is one of the topic models which is applied toautomatically discover topics in the text that consumers aremost satisfied and least satisfied with e core computa-tional problem for topic models is to use the collected text toinfer the hidden topic structure [66] us this studyidentifies the consumer concerns by using the LDA modelto discover key topics from the collected text

32 MCDM Model

321 DEMATEL Technique DEMATEL technique is asystematic factor analysis method to detect the cause-effectrelationships between complicated indicators using graphtheory and matrix tools [67] originally proposed by theBattelle Research Centre in 1972 [68] DEMATEL has beenused to solve complicated real-world problems by buildingan INRM [69] such as optimal online travel agencies [70]regional innovation capacity [71] and sustainable onlineconsumption [72] us the steps of this technique aresummarized as follows

Step 1 Finding the average direct effect matrixe mutual direct effect among criteria is evaluated by

the knowledge-based experts e scales ranged from 0 to 4where ldquo0rdquo means ldquoabsolutely no effectrdquo and ldquo4rdquo means ldquoveryhigh effectrdquo ldquo1rdquo ldquo2rdquo and ldquo3rdquo mean ldquolow effectrdquo ldquomiddleeffectrdquo and ldquohigh effectrdquo respectively By a pairwise com-parison we can obtain these groups of direct matrices byscores where ij represents the influence from criterion i tocriterion j After that we can calculate an average directeffect matrixG (as seen in equation (1)) where each criterionis the average of the corresponding criteria in the expertsrsquodirect matrices [73]

G

g11c g

1jc g

1nc

⋮ ⋮ ⋮

gi1c g

ijc g

inD

⋮ ⋮ ⋮

gn1c g

njc g

nnc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(1)

Step 1 Setting up the normalized direct-influence matrix Xe matrix X can be acquired by using equations (2) and

(3)

X S times G Sgt 0 (2)

where

S min i j1

maxi 1113936nj1 g

ijc

⎧⎨

⎩ 1

maxj 1113936ni1 g

ijc

⎫⎬

⎭ i j isin 1 2 n

(3)

Step 1 Computing the total influence matrix Tce total influence matrix Tc can be derived from

equation (4) where matrix I denotes a unit matrix

Tc X + X2

+ X3

+ middot middot middot + Xθ

X I + X + X2

+ middot middot middot + Xθminus1

1113872 1113873(I minus X)(I minus X)minus1

X I minus Xθ

1113872 1113873(I minus X)minus1

X(I minus X)minus1

(4)

where X [xijc ]ntimesn 0le [x

ijc ]le 1 0lt 1113936

nj1 x

ijc le 1 and

0lt 1113936ni1 x

ijc le 1 and at least the sum of one row or column

(but not all) equals one limθ⟶infinXθ [0]ntimesn

Step 4 Building the INRM and analyzing the resultse sum of rows and the sum of columns of total in-

fluencematrix Tc can be respectively represented by vector rand vector s according to equations (5)ndash(6) where ri in-dicates the total influence of criterion i on others the sidenotes the total influences received by criterion j from othercriteria When i j and i j isin 1 2 n the vector (ri+ si)expresses the importance of criterion i in the questionLikewise the vector (ri minus si) identifies the degree of causalityamong indicators Simultaneously if (ri minus si) is positive thecriterion i influences other criteria On the contrary if(ri minus si) is negative the criterion i is affected by others Fi-nally draw the INRM in which the vertical axis represents(ri+ si) and the horizontal axis represents (ri minus si) [74]

Tc tijc1113960 1113961

ntimesn i j isin 1 2 n

r 1113944n

j1tijc

⎡⎢⎢⎣ ⎤⎥⎥⎦

ntimes1

tic1113960 1113961

ntimes1 r1 ri rn( 1113857prime(5)

s 1113944n

i1tijc

⎡⎣ ⎤⎦

1timesn

tic1113960 11139611timesn

s1 si sn( 1113857prime (6)

e total influence matrices has two forms one is Tc [tijc]ntimesn (equation (7)) where n represents the number of thecriteria and the other is TD [tij D]mtimesm (equation (8))where m represents the number of dimensions

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTc =

Dj

Dm

Dm

D1

D1

Tc11 Tc

1j Tc1n

Tcn1 Tcc

nj Tcnn

Tci1 Tcc

ij Tcin

c1n1 cj1 cjnj cm1 cmnm

(7)

Mathematical Problems in Engineering 5

322 DANP Method e ANP method was first proposedby Saaty [64] to deal with the interdependence and feedbackproblems among indicators [75 76] It originates from theAHP but eliminates the deficiency of AHP which assumesthat the indicators are independent of each other [77]However the weighted super-matrix in the ANP methodlacks rationality because it assumes that each cluster has thesame weight [78] erefore the DANP is an appropriatemethod to obtain the influential weights by improving thenormalization process and addressing the interrelationshipsamong indicators [45] It has been used in many differentfields such as low-carbon energy planning [79] materialselection [80] and renewable energy selection [81]us theprocess of this technique involves the following steps

Step 1 Calculating the normalized total-influential matrixTnor D

According to equations (8) and (9) matrix Tnor D isframed through normalizing the total-influential matrix TDFirst the sum of each row in matrix TD can be expressed astiD 1113936

mj1 t

ijD wherem represents the number of dimensions

en the normalized total-influential matrix Tnor D iscalculated by dividing the elements in each row by the sumof the row so that Tnor D [tij DtiD]mtimesm Meanwhile thesum of each row in matrix Tnor D equals one so that1113936

mj1 t

norij

D 1

TD

t11D t

1jD t

1mD

ti1D t

ijD t

imD

tm1D t

mjD t

mmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⟶ 1113944m

j1tijD t

iD (8)

TnorD

t11D

t1D

t1jD

t1D

t1mD

t1D

⋮ ⋮ ⋮

ti1D

tiD

tijD

tiD

timD

tiD

⋮ ⋮ ⋮

tm1D

tmD

tmjD

tmD

tmmD

tmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

Step 2 Exporting the normalized matrix Tnor c by dimen-sions and clusters

A new matrix Tnor c is acquired by normalizing Tc withthe total degrees of effect and influence of the dimensionsand clusters

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTcnor =

Dj

Dm

Dm

D1

D1

Tcnor11 Tc

nor1j Tcnor1n

Tcnorn1 Tcc

nornj Tcnornn

Tcnori1 Tcc

norij Tcnorin

c1n1 cj1 cjnj cm1 cmnm

(10)

Step 3 Determining the unweighted super-matrix Wce unweighted super-matrix Wc is obtained by trans-

posing the normalized matrix Tnor c

cmnm

cini

c1n1

cm1

ci1

c11c11

DiWc = (Tcnor)prime=

Dj

Dm

Dm

D1

D1

W11 Wi1 Wn1

W1n Win Wnn

W1j Wij Wnj

c1n1 cj1 cjnji cm1 cmnm

(11)

Step 4 Constructing the weighted super-matrix W lowast ce normalized total-influential matrix Tnor D is ob-

tained by equation (9) and the unweighted super-matrixWcis obtained by equation (11) us using equation (12) aweighted super-matrix W lowast c which improves the tradi-tional ANP by using equal weights to make it appropriate forthe real world can be obtained by the product of Tnor c andWc ie W lowast cTnor D lowast Wc is demonstrates that theinfluential level values are the basis of normalization todetermine a weighted super-matrix

Wlowastc T

norD Wc

tnor11D times W

11c t

nori1D times W

i1c t

norm1D times W

m1c

⋮ ⋮

tnor1j

D times W1jc t

norij

D times Wijc t

normj

D times Wmjc

⋮ ⋮ ⋮

tnor1m

D times W1mc t

norim

D times Wimc t

normm

D times Wmmc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)

6 Mathematical Problems in Engineering

Step 5 Calculating the influential weights of the criteriae influential weights w (w1 wj wn) can be

obtained according to the weighted super-matrixW lowast c andmultiplied several times until it converges a stable super-matrix so that limα⟶infin (W lowast c)α where α is a positiveinteger number

323 VIKOR Method e VIKOR method was first pro-posed by Opricovic to optimize the multiple criteria ofcomplicated systems in 1998 [82] It is applied to rank andselect from a set of alternatives given the conflicting criteriaVIKOR is also the compromise ranking method based on theconcept of the Positive-ideal (or the aspired level) solutionand Negative-ideal (or the worst level) solution [83] So theorder of results can be compared by ldquoproximityrdquo to the ldquoidealrdquoalternative [82 84 85] In this study the modified VIKORmethod can be used to increase customersrsquo satisfaction inalternatives that are influenced by the interaction of variousfactors All of the steps for VIKOR are presented as follows

Step 1 Determining the positive-ideal solution negative-ideal solution and the gap

According to the concepts of VIKOR flowast j is the positive-ideal point of assessment criteria which indicates the bestvalue (aspiration level) In contrast fminus j is a negative-idealpoint which means the worst value In this study the bestvalue is set as flowast j 10 [64] Likewise the worst value is set asfminus j 0 with scores of criteria ranging from 0 (dissatisfied) to10 (satisfied) is is different from the traditional VIKORin which the positive-ideal solution is set as the maximum ofall schemes ie flowast jmax fkj|k 1 2 K and thenegative-ideal solution is set as the minimum of all schemesie fminus jmin fkj|k 1 2 K en we can obtain thegap ratio as is described in equation (13)

zkj flowastj minus fkj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

flowastj minus f

minusj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

(13)

Step 2 Calculating the average gap Ek and maximal gap Qkfor prioritizing improvement

e general form of the Lp-metric function is introduced asfollows

Lp

k 1113944n

j1wj times zkj1113960 1113961

p⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭

1p

1lepleinfin k 1 2 k

(14)

where wj is the influential weight generated from the DANPL

p1k and L

pinfink are separately expressed as Ek and Qk which

can be calculated by equations (15) and (16)

Ek Lp1k 1113944

n

j1wj times zkj (15)

Qk Lp⟶infink max j zkj|j 1 2 n1113966 1113967 (16)

e compromise solution minkLp k indicates that thesynthesized gap should be minimized e average gap isemphasized when p is equal to one However Qk means themaximum gap of overall criteria in alternative k When p isinfinite the maximal gaps should be improved by thepriority

Step 3 Exporting comprehensively evaluated values of thealternatives

Uk λEk minus E

lowast( 1113857

Eminus

minus Elowast

( 1113857+(1 minus λ)

Qk minus Qlowast

( 1113857

Qminus

minus Qlowast

( 1113857 (17)

For equation (15) the best gap E lowast k and the worst valueEminus k is expressed as E lowast kmink Ek and Eminus kmaxk Ekrespectively For equation (16) the best gap Q lowast k and theworst value Qminus k is separately expressed as Q lowast kmink Qkand Qminus kmaxk Qk Furthermore in optimal conditionsE lowast k 0 Q lowast k 0 and at worse Eminus k 1 Qminus k 1 enequation (17) can be simplified as follows

Uk λEk +(1 minus λ)Qk (18)

e range of values for λ is zero to one λgt 05 means theanalysis emphasizes the average gap more λlt 05 indicatesthe analysis is more concerned about the maximum gap forpriority improvement In general we can set λ 05

4 Empirical Results

Based on the above models ten types of BEVs are carried outas shown in Table 1 e empirical results of the analyticalprocess are as follows

41 Building Evaluation Indicator System e online word-of-mouth of BEVs is from the Autohome website (seehttpswwwautohomecomcn) which is a relatively well-known auto website in China Word-of-mouth is the key forusers to express their views On the Autohome website theword-of-mouth data reflect vehicle attributes that users aremost concerned about Because the website has strict re-quirements on the opinions all the text information isaccurate and of high quality

411 Emotional Polarity Analysis First emotional lexiconis extracted from the text that has been preprocessed Table 2shows the emotional words with high word frequency It canbe found that the word frequency of the emotional wordssuch as right fine smooth not bad not so dusty highcomfortable stable and so on is very high which indicatedthat consumersrsquo overall cognition of BEVs is relativelyconcentrated

On the Autohome website the overall image of a BEV isoften measured in terms of the customersrsquo satisfaction withthe BEV en the emotional terms of customersrsquo satis-faction are obtained through cluster analysis as is shown inTable 3e overall satisfaction of customers with the BEV ismeasured by the emotion words that indicate emotion306 of the total number of word-of-mouth have

Mathematical Problems in Engineering 7

preprocessing are used to obtain the emotion words withhigh word frequency and then the Skip-gram model is usedto obtain words that are similar to the emotion words toobtain a more comprehensive emotion vocabulary

314 Clustering e clustering algorithm divides theemotional lexicon of word-of-mouth into five categoriesbased on the difference between the customersrsquo expectationsand the actual perception of the BEVs

315 LDA Model Topic models are algorithms for dis-covering key topics in a large and unstructured collection oftext [65] LDA is one of the topic models which is applied toautomatically discover topics in the text that consumers aremost satisfied and least satisfied with e core computa-tional problem for topic models is to use the collected text toinfer the hidden topic structure [66] us this studyidentifies the consumer concerns by using the LDA modelto discover key topics from the collected text

32 MCDM Model

321 DEMATEL Technique DEMATEL technique is asystematic factor analysis method to detect the cause-effectrelationships between complicated indicators using graphtheory and matrix tools [67] originally proposed by theBattelle Research Centre in 1972 [68] DEMATEL has beenused to solve complicated real-world problems by buildingan INRM [69] such as optimal online travel agencies [70]regional innovation capacity [71] and sustainable onlineconsumption [72] us the steps of this technique aresummarized as follows

Step 1 Finding the average direct effect matrixe mutual direct effect among criteria is evaluated by

the knowledge-based experts e scales ranged from 0 to 4where ldquo0rdquo means ldquoabsolutely no effectrdquo and ldquo4rdquo means ldquoveryhigh effectrdquo ldquo1rdquo ldquo2rdquo and ldquo3rdquo mean ldquolow effectrdquo ldquomiddleeffectrdquo and ldquohigh effectrdquo respectively By a pairwise com-parison we can obtain these groups of direct matrices byscores where ij represents the influence from criterion i tocriterion j After that we can calculate an average directeffect matrixG (as seen in equation (1)) where each criterionis the average of the corresponding criteria in the expertsrsquodirect matrices [73]

G

g11c g

1jc g

1nc

⋮ ⋮ ⋮

gi1c g

ijc g

inD

⋮ ⋮ ⋮

gn1c g

njc g

nnc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(1)

Step 1 Setting up the normalized direct-influence matrix Xe matrix X can be acquired by using equations (2) and

(3)

X S times G Sgt 0 (2)

where

S min i j1

maxi 1113936nj1 g

ijc

⎧⎨

⎩ 1

maxj 1113936ni1 g

ijc

⎫⎬

⎭ i j isin 1 2 n

(3)

Step 1 Computing the total influence matrix Tce total influence matrix Tc can be derived from

equation (4) where matrix I denotes a unit matrix

Tc X + X2

+ X3

+ middot middot middot + Xθ

X I + X + X2

+ middot middot middot + Xθminus1

1113872 1113873(I minus X)(I minus X)minus1

X I minus Xθ

1113872 1113873(I minus X)minus1

X(I minus X)minus1

(4)

where X [xijc ]ntimesn 0le [x

ijc ]le 1 0lt 1113936

nj1 x

ijc le 1 and

0lt 1113936ni1 x

ijc le 1 and at least the sum of one row or column

(but not all) equals one limθ⟶infinXθ [0]ntimesn

Step 4 Building the INRM and analyzing the resultse sum of rows and the sum of columns of total in-

fluencematrix Tc can be respectively represented by vector rand vector s according to equations (5)ndash(6) where ri in-dicates the total influence of criterion i on others the sidenotes the total influences received by criterion j from othercriteria When i j and i j isin 1 2 n the vector (ri+ si)expresses the importance of criterion i in the questionLikewise the vector (ri minus si) identifies the degree of causalityamong indicators Simultaneously if (ri minus si) is positive thecriterion i influences other criteria On the contrary if(ri minus si) is negative the criterion i is affected by others Fi-nally draw the INRM in which the vertical axis represents(ri+ si) and the horizontal axis represents (ri minus si) [74]

Tc tijc1113960 1113961

ntimesn i j isin 1 2 n

r 1113944n

j1tijc

⎡⎢⎢⎣ ⎤⎥⎥⎦

ntimes1

tic1113960 1113961

ntimes1 r1 ri rn( 1113857prime(5)

s 1113944n

i1tijc

⎡⎣ ⎤⎦

1timesn

tic1113960 11139611timesn

s1 si sn( 1113857prime (6)

e total influence matrices has two forms one is Tc [tijc]ntimesn (equation (7)) where n represents the number of thecriteria and the other is TD [tij D]mtimesm (equation (8))where m represents the number of dimensions

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTc =

Dj

Dm

Dm

D1

D1

Tc11 Tc

1j Tc1n

Tcn1 Tcc

nj Tcnn

Tci1 Tcc

ij Tcin

c1n1 cj1 cjnj cm1 cmnm

(7)

Mathematical Problems in Engineering 5

322 DANP Method e ANP method was first proposedby Saaty [64] to deal with the interdependence and feedbackproblems among indicators [75 76] It originates from theAHP but eliminates the deficiency of AHP which assumesthat the indicators are independent of each other [77]However the weighted super-matrix in the ANP methodlacks rationality because it assumes that each cluster has thesame weight [78] erefore the DANP is an appropriatemethod to obtain the influential weights by improving thenormalization process and addressing the interrelationshipsamong indicators [45] It has been used in many differentfields such as low-carbon energy planning [79] materialselection [80] and renewable energy selection [81]us theprocess of this technique involves the following steps

Step 1 Calculating the normalized total-influential matrixTnor D

According to equations (8) and (9) matrix Tnor D isframed through normalizing the total-influential matrix TDFirst the sum of each row in matrix TD can be expressed astiD 1113936

mj1 t

ijD wherem represents the number of dimensions

en the normalized total-influential matrix Tnor D iscalculated by dividing the elements in each row by the sumof the row so that Tnor D [tij DtiD]mtimesm Meanwhile thesum of each row in matrix Tnor D equals one so that1113936

mj1 t

norij

D 1

TD

t11D t

1jD t

1mD

ti1D t

ijD t

imD

tm1D t

mjD t

mmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⟶ 1113944m

j1tijD t

iD (8)

TnorD

t11D

t1D

t1jD

t1D

t1mD

t1D

⋮ ⋮ ⋮

ti1D

tiD

tijD

tiD

timD

tiD

⋮ ⋮ ⋮

tm1D

tmD

tmjD

tmD

tmmD

tmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

Step 2 Exporting the normalized matrix Tnor c by dimen-sions and clusters

A new matrix Tnor c is acquired by normalizing Tc withthe total degrees of effect and influence of the dimensionsand clusters

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTcnor =

Dj

Dm

Dm

D1

D1

Tcnor11 Tc

nor1j Tcnor1n

Tcnorn1 Tcc

nornj Tcnornn

Tcnori1 Tcc

norij Tcnorin

c1n1 cj1 cjnj cm1 cmnm

(10)

Step 3 Determining the unweighted super-matrix Wce unweighted super-matrix Wc is obtained by trans-

posing the normalized matrix Tnor c

cmnm

cini

c1n1

cm1

ci1

c11c11

DiWc = (Tcnor)prime=

Dj

Dm

Dm

D1

D1

W11 Wi1 Wn1

W1n Win Wnn

W1j Wij Wnj

c1n1 cj1 cjnji cm1 cmnm

(11)

Step 4 Constructing the weighted super-matrix W lowast ce normalized total-influential matrix Tnor D is ob-

tained by equation (9) and the unweighted super-matrixWcis obtained by equation (11) us using equation (12) aweighted super-matrix W lowast c which improves the tradi-tional ANP by using equal weights to make it appropriate forthe real world can be obtained by the product of Tnor c andWc ie W lowast cTnor D lowast Wc is demonstrates that theinfluential level values are the basis of normalization todetermine a weighted super-matrix

Wlowastc T

norD Wc

tnor11D times W

11c t

nori1D times W

i1c t

norm1D times W

m1c

⋮ ⋮

tnor1j

D times W1jc t

norij

D times Wijc t

normj

D times Wmjc

⋮ ⋮ ⋮

tnor1m

D times W1mc t

norim

D times Wimc t

normm

D times Wmmc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)

6 Mathematical Problems in Engineering

Step 5 Calculating the influential weights of the criteriae influential weights w (w1 wj wn) can be

obtained according to the weighted super-matrixW lowast c andmultiplied several times until it converges a stable super-matrix so that limα⟶infin (W lowast c)α where α is a positiveinteger number

323 VIKOR Method e VIKOR method was first pro-posed by Opricovic to optimize the multiple criteria ofcomplicated systems in 1998 [82] It is applied to rank andselect from a set of alternatives given the conflicting criteriaVIKOR is also the compromise ranking method based on theconcept of the Positive-ideal (or the aspired level) solutionand Negative-ideal (or the worst level) solution [83] So theorder of results can be compared by ldquoproximityrdquo to the ldquoidealrdquoalternative [82 84 85] In this study the modified VIKORmethod can be used to increase customersrsquo satisfaction inalternatives that are influenced by the interaction of variousfactors All of the steps for VIKOR are presented as follows

Step 1 Determining the positive-ideal solution negative-ideal solution and the gap

According to the concepts of VIKOR flowast j is the positive-ideal point of assessment criteria which indicates the bestvalue (aspiration level) In contrast fminus j is a negative-idealpoint which means the worst value In this study the bestvalue is set as flowast j 10 [64] Likewise the worst value is set asfminus j 0 with scores of criteria ranging from 0 (dissatisfied) to10 (satisfied) is is different from the traditional VIKORin which the positive-ideal solution is set as the maximum ofall schemes ie flowast jmax fkj|k 1 2 K and thenegative-ideal solution is set as the minimum of all schemesie fminus jmin fkj|k 1 2 K en we can obtain thegap ratio as is described in equation (13)

zkj flowastj minus fkj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

flowastj minus f

minusj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

(13)

Step 2 Calculating the average gap Ek and maximal gap Qkfor prioritizing improvement

e general form of the Lp-metric function is introduced asfollows

Lp

k 1113944n

j1wj times zkj1113960 1113961

p⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭

1p

1lepleinfin k 1 2 k

(14)

where wj is the influential weight generated from the DANPL

p1k and L

pinfink are separately expressed as Ek and Qk which

can be calculated by equations (15) and (16)

Ek Lp1k 1113944

n

j1wj times zkj (15)

Qk Lp⟶infink max j zkj|j 1 2 n1113966 1113967 (16)

e compromise solution minkLp k indicates that thesynthesized gap should be minimized e average gap isemphasized when p is equal to one However Qk means themaximum gap of overall criteria in alternative k When p isinfinite the maximal gaps should be improved by thepriority

Step 3 Exporting comprehensively evaluated values of thealternatives

Uk λEk minus E

lowast( 1113857

Eminus

minus Elowast

( 1113857+(1 minus λ)

Qk minus Qlowast

( 1113857

Qminus

minus Qlowast

( 1113857 (17)

For equation (15) the best gap E lowast k and the worst valueEminus k is expressed as E lowast kmink Ek and Eminus kmaxk Ekrespectively For equation (16) the best gap Q lowast k and theworst value Qminus k is separately expressed as Q lowast kmink Qkand Qminus kmaxk Qk Furthermore in optimal conditionsE lowast k 0 Q lowast k 0 and at worse Eminus k 1 Qminus k 1 enequation (17) can be simplified as follows

Uk λEk +(1 minus λ)Qk (18)

e range of values for λ is zero to one λgt 05 means theanalysis emphasizes the average gap more λlt 05 indicatesthe analysis is more concerned about the maximum gap forpriority improvement In general we can set λ 05

4 Empirical Results

Based on the above models ten types of BEVs are carried outas shown in Table 1 e empirical results of the analyticalprocess are as follows

41 Building Evaluation Indicator System e online word-of-mouth of BEVs is from the Autohome website (seehttpswwwautohomecomcn) which is a relatively well-known auto website in China Word-of-mouth is the key forusers to express their views On the Autohome website theword-of-mouth data reflect vehicle attributes that users aremost concerned about Because the website has strict re-quirements on the opinions all the text information isaccurate and of high quality

411 Emotional Polarity Analysis First emotional lexiconis extracted from the text that has been preprocessed Table 2shows the emotional words with high word frequency It canbe found that the word frequency of the emotional wordssuch as right fine smooth not bad not so dusty highcomfortable stable and so on is very high which indicatedthat consumersrsquo overall cognition of BEVs is relativelyconcentrated

On the Autohome website the overall image of a BEV isoften measured in terms of the customersrsquo satisfaction withthe BEV en the emotional terms of customersrsquo satis-faction are obtained through cluster analysis as is shown inTable 3e overall satisfaction of customers with the BEV ismeasured by the emotion words that indicate emotion306 of the total number of word-of-mouth have

Mathematical Problems in Engineering 7

322 DANP Method e ANP method was first proposedby Saaty [64] to deal with the interdependence and feedbackproblems among indicators [75 76] It originates from theAHP but eliminates the deficiency of AHP which assumesthat the indicators are independent of each other [77]However the weighted super-matrix in the ANP methodlacks rationality because it assumes that each cluster has thesame weight [78] erefore the DANP is an appropriatemethod to obtain the influential weights by improving thenormalization process and addressing the interrelationshipsamong indicators [45] It has been used in many differentfields such as low-carbon energy planning [79] materialselection [80] and renewable energy selection [81]us theprocess of this technique involves the following steps

Step 1 Calculating the normalized total-influential matrixTnor D

According to equations (8) and (9) matrix Tnor D isframed through normalizing the total-influential matrix TDFirst the sum of each row in matrix TD can be expressed astiD 1113936

mj1 t

ijD wherem represents the number of dimensions

en the normalized total-influential matrix Tnor D iscalculated by dividing the elements in each row by the sumof the row so that Tnor D [tij DtiD]mtimesm Meanwhile thesum of each row in matrix Tnor D equals one so that1113936

mj1 t

norij

D 1

TD

t11D t

1jD t

1mD

ti1D t

ijD t

imD

tm1D t

mjD t

mmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

⟶ 1113944m

j1tijD t

iD (8)

TnorD

t11D

t1D

t1jD

t1D

t1mD

t1D

⋮ ⋮ ⋮

ti1D

tiD

tijD

tiD

timD

tiD

⋮ ⋮ ⋮

tm1D

tmD

tmjD

tmD

tmmD

tmD

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

Step 2 Exporting the normalized matrix Tnor c by dimen-sions and clusters

A new matrix Tnor c is acquired by normalizing Tc withthe total degrees of effect and influence of the dimensionsand clusters

cmnm

cini

c1n11

cm1

ci1

c11c11

DiTcnor =

Dj

Dm

Dm

D1

D1

Tcnor11 Tc

nor1j Tcnor1n

Tcnorn1 Tcc

nornj Tcnornn

Tcnori1 Tcc

norij Tcnorin

c1n1 cj1 cjnj cm1 cmnm

(10)

Step 3 Determining the unweighted super-matrix Wce unweighted super-matrix Wc is obtained by trans-

posing the normalized matrix Tnor c

cmnm

cini

c1n1

cm1

ci1

c11c11

DiWc = (Tcnor)prime=

Dj

Dm

Dm

D1

D1

W11 Wi1 Wn1

W1n Win Wnn

W1j Wij Wnj

c1n1 cj1 cjnji cm1 cmnm

(11)

Step 4 Constructing the weighted super-matrix W lowast ce normalized total-influential matrix Tnor D is ob-

tained by equation (9) and the unweighted super-matrixWcis obtained by equation (11) us using equation (12) aweighted super-matrix W lowast c which improves the tradi-tional ANP by using equal weights to make it appropriate forthe real world can be obtained by the product of Tnor c andWc ie W lowast cTnor D lowast Wc is demonstrates that theinfluential level values are the basis of normalization todetermine a weighted super-matrix

Wlowastc T

norD Wc

tnor11D times W

11c t

nori1D times W

i1c t

norm1D times W

m1c

⋮ ⋮

tnor1j

D times W1jc t

norij

D times Wijc t

normj

D times Wmjc

⋮ ⋮ ⋮

tnor1m

D times W1mc t

norim

D times Wimc t

normm

D times Wmmc

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)

6 Mathematical Problems in Engineering

Step 5 Calculating the influential weights of the criteriae influential weights w (w1 wj wn) can be

obtained according to the weighted super-matrixW lowast c andmultiplied several times until it converges a stable super-matrix so that limα⟶infin (W lowast c)α where α is a positiveinteger number

323 VIKOR Method e VIKOR method was first pro-posed by Opricovic to optimize the multiple criteria ofcomplicated systems in 1998 [82] It is applied to rank andselect from a set of alternatives given the conflicting criteriaVIKOR is also the compromise ranking method based on theconcept of the Positive-ideal (or the aspired level) solutionand Negative-ideal (or the worst level) solution [83] So theorder of results can be compared by ldquoproximityrdquo to the ldquoidealrdquoalternative [82 84 85] In this study the modified VIKORmethod can be used to increase customersrsquo satisfaction inalternatives that are influenced by the interaction of variousfactors All of the steps for VIKOR are presented as follows

Step 1 Determining the positive-ideal solution negative-ideal solution and the gap

According to the concepts of VIKOR flowast j is the positive-ideal point of assessment criteria which indicates the bestvalue (aspiration level) In contrast fminus j is a negative-idealpoint which means the worst value In this study the bestvalue is set as flowast j 10 [64] Likewise the worst value is set asfminus j 0 with scores of criteria ranging from 0 (dissatisfied) to10 (satisfied) is is different from the traditional VIKORin which the positive-ideal solution is set as the maximum ofall schemes ie flowast jmax fkj|k 1 2 K and thenegative-ideal solution is set as the minimum of all schemesie fminus jmin fkj|k 1 2 K en we can obtain thegap ratio as is described in equation (13)

zkj flowastj minus fkj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

flowastj minus f

minusj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

(13)

Step 2 Calculating the average gap Ek and maximal gap Qkfor prioritizing improvement

e general form of the Lp-metric function is introduced asfollows

Lp

k 1113944n

j1wj times zkj1113960 1113961

p⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭

1p

1lepleinfin k 1 2 k

(14)

where wj is the influential weight generated from the DANPL

p1k and L

pinfink are separately expressed as Ek and Qk which

can be calculated by equations (15) and (16)

Ek Lp1k 1113944

n

j1wj times zkj (15)

Qk Lp⟶infink max j zkj|j 1 2 n1113966 1113967 (16)

e compromise solution minkLp k indicates that thesynthesized gap should be minimized e average gap isemphasized when p is equal to one However Qk means themaximum gap of overall criteria in alternative k When p isinfinite the maximal gaps should be improved by thepriority

Step 3 Exporting comprehensively evaluated values of thealternatives

Uk λEk minus E

lowast( 1113857

Eminus

minus Elowast

( 1113857+(1 minus λ)

Qk minus Qlowast

( 1113857

Qminus

minus Qlowast

( 1113857 (17)

For equation (15) the best gap E lowast k and the worst valueEminus k is expressed as E lowast kmink Ek and Eminus kmaxk Ekrespectively For equation (16) the best gap Q lowast k and theworst value Qminus k is separately expressed as Q lowast kmink Qkand Qminus kmaxk Qk Furthermore in optimal conditionsE lowast k 0 Q lowast k 0 and at worse Eminus k 1 Qminus k 1 enequation (17) can be simplified as follows

Uk λEk +(1 minus λ)Qk (18)

e range of values for λ is zero to one λgt 05 means theanalysis emphasizes the average gap more λlt 05 indicatesthe analysis is more concerned about the maximum gap forpriority improvement In general we can set λ 05

4 Empirical Results

Based on the above models ten types of BEVs are carried outas shown in Table 1 e empirical results of the analyticalprocess are as follows

41 Building Evaluation Indicator System e online word-of-mouth of BEVs is from the Autohome website (seehttpswwwautohomecomcn) which is a relatively well-known auto website in China Word-of-mouth is the key forusers to express their views On the Autohome website theword-of-mouth data reflect vehicle attributes that users aremost concerned about Because the website has strict re-quirements on the opinions all the text information isaccurate and of high quality

411 Emotional Polarity Analysis First emotional lexiconis extracted from the text that has been preprocessed Table 2shows the emotional words with high word frequency It canbe found that the word frequency of the emotional wordssuch as right fine smooth not bad not so dusty highcomfortable stable and so on is very high which indicatedthat consumersrsquo overall cognition of BEVs is relativelyconcentrated

On the Autohome website the overall image of a BEV isoften measured in terms of the customersrsquo satisfaction withthe BEV en the emotional terms of customersrsquo satis-faction are obtained through cluster analysis as is shown inTable 3e overall satisfaction of customers with the BEV ismeasured by the emotion words that indicate emotion306 of the total number of word-of-mouth have

Mathematical Problems in Engineering 7

Step 5 Calculating the influential weights of the criteriae influential weights w (w1 wj wn) can be

obtained according to the weighted super-matrixW lowast c andmultiplied several times until it converges a stable super-matrix so that limα⟶infin (W lowast c)α where α is a positiveinteger number

323 VIKOR Method e VIKOR method was first pro-posed by Opricovic to optimize the multiple criteria ofcomplicated systems in 1998 [82] It is applied to rank andselect from a set of alternatives given the conflicting criteriaVIKOR is also the compromise ranking method based on theconcept of the Positive-ideal (or the aspired level) solutionand Negative-ideal (or the worst level) solution [83] So theorder of results can be compared by ldquoproximityrdquo to the ldquoidealrdquoalternative [82 84 85] In this study the modified VIKORmethod can be used to increase customersrsquo satisfaction inalternatives that are influenced by the interaction of variousfactors All of the steps for VIKOR are presented as follows

Step 1 Determining the positive-ideal solution negative-ideal solution and the gap

According to the concepts of VIKOR flowast j is the positive-ideal point of assessment criteria which indicates the bestvalue (aspiration level) In contrast fminus j is a negative-idealpoint which means the worst value In this study the bestvalue is set as flowast j 10 [64] Likewise the worst value is set asfminus j 0 with scores of criteria ranging from 0 (dissatisfied) to10 (satisfied) is is different from the traditional VIKORin which the positive-ideal solution is set as the maximum ofall schemes ie flowast jmax fkj|k 1 2 K and thenegative-ideal solution is set as the minimum of all schemesie fminus jmin fkj|k 1 2 K en we can obtain thegap ratio as is described in equation (13)

zkj flowastj minus fkj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

flowastj minus f

minusj

11138681113868111386811138681113868

111386811138681113868111386811138681113874 1113875

(13)

Step 2 Calculating the average gap Ek and maximal gap Qkfor prioritizing improvement

e general form of the Lp-metric function is introduced asfollows

Lp

k 1113944n

j1wj times zkj1113960 1113961

p⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭

1p

1lepleinfin k 1 2 k

(14)

where wj is the influential weight generated from the DANPL

p1k and L

pinfink are separately expressed as Ek and Qk which

can be calculated by equations (15) and (16)

Ek Lp1k 1113944

n

j1wj times zkj (15)

Qk Lp⟶infink max j zkj|j 1 2 n1113966 1113967 (16)

e compromise solution minkLp k indicates that thesynthesized gap should be minimized e average gap isemphasized when p is equal to one However Qk means themaximum gap of overall criteria in alternative k When p isinfinite the maximal gaps should be improved by thepriority

Step 3 Exporting comprehensively evaluated values of thealternatives

Uk λEk minus E

lowast( 1113857

Eminus

minus Elowast

( 1113857+(1 minus λ)

Qk minus Qlowast

( 1113857

Qminus

minus Qlowast

( 1113857 (17)

For equation (15) the best gap E lowast k and the worst valueEminus k is expressed as E lowast kmink Ek and Eminus kmaxk Ekrespectively For equation (16) the best gap Q lowast k and theworst value Qminus k is separately expressed as Q lowast kmink Qkand Qminus kmaxk Qk Furthermore in optimal conditionsE lowast k 0 Q lowast k 0 and at worse Eminus k 1 Qminus k 1 enequation (17) can be simplified as follows

Uk λEk +(1 minus λ)Qk (18)

e range of values for λ is zero to one λgt 05 means theanalysis emphasizes the average gap more λlt 05 indicatesthe analysis is more concerned about the maximum gap forpriority improvement In general we can set λ 05

4 Empirical Results

Based on the above models ten types of BEVs are carried outas shown in Table 1 e empirical results of the analyticalprocess are as follows

41 Building Evaluation Indicator System e online word-of-mouth of BEVs is from the Autohome website (seehttpswwwautohomecomcn) which is a relatively well-known auto website in China Word-of-mouth is the key forusers to express their views On the Autohome website theword-of-mouth data reflect vehicle attributes that users aremost concerned about Because the website has strict re-quirements on the opinions all the text information isaccurate and of high quality

411 Emotional Polarity Analysis First emotional lexiconis extracted from the text that has been preprocessed Table 2shows the emotional words with high word frequency It canbe found that the word frequency of the emotional wordssuch as right fine smooth not bad not so dusty highcomfortable stable and so on is very high which indicatedthat consumersrsquo overall cognition of BEVs is relativelyconcentrated

On the Autohome website the overall image of a BEV isoften measured in terms of the customersrsquo satisfaction withthe BEV en the emotional terms of customersrsquo satis-faction are obtained through cluster analysis as is shown inTable 3e overall satisfaction of customers with the BEV ismeasured by the emotion words that indicate emotion306 of the total number of word-of-mouth have

Mathematical Problems in Engineering 7

customersrsquo satisfaction far greater than customersrsquo expec-tations 275 of the total have customersrsquo satisfactiongreater than customersrsquo expectations 15 of the total havecustomersrsquo satisfaction equal to customersrsquo expectations107 of the total have customersrsquo satisfaction less thancustomersrsquo expectations and 162 of the total have cus-tomersrsquo satisfaction far less than customersrsquo expectations

Finally the word-of-mouth containing the above sen-timent words are calculated and scored e actual scoreranges from 1 to 5 points according to the set scoring rules(using the sentiment dictionary method transforming androunding the results) the scores of the word-of-mouthcontaining the sentiment words are obtained and the av-erage score containing each sentiment word is calculated(the ratio of the frequency of the sentiment word to thenumber of word-of-mouth containing the sentiment word)e emotional scores are shown in Table 4

412 Topic Analysis is study identifies the consumerconcerns by using the LDA model to discover key topics fromthe collected text As is shown in Table 5 the LDA modeldivides keywords into five topics including dynamics

technology safety comfort and cost Specifically the keywordsin topic 1 mainly involve maximum power max torque topspeed and acceleration time which reflect the dynamics Intopic 2 the keywords mainly involve driving range chargingtime and electricity consumption which reflect the batterytechnologye keywords of topic 3 are mainly related to threeaspects curb weight braking and operating stability whichrepresent the safety of BEVs In topic 4 the keywords mainlyinvolve four aspects exterior and interior space suspensionand seats reflecting the comfort of BEVs e keywords intopic 5 involve three aspects price incentives and after-salescost which are the embodiment of the cost factor

rough the above analysis five major topics are ob-tained which are taken as the five dimensions in the in-dicator system Seventeen criteria are identified by keywordsegmentation e details of the evaluation dimensions andcriteria of BEVs are shown in Table 6

42 Data Collection Based on the evaluation indicatorsystem two different questionnaires are designed to collectthe information required for sufficient evaluation of tenBEVs e DEMATEL questionnaire on the relationship

Table 1 Details of ten types of BEVs

Alternatives Vehicle types Vehicle manufacturersP1 BAOJUN E100 SGMW CompanyP2 BAIC EU Series BAIC BJEV CompanyP3 Aion S GAC NE CompanyP4 MG EZS SAIC MotorP5 Aeolus E70 Dongfeng Passenger Vehicle CompanyP6 GEOMETRY A Geely Auto CompanyP7 BYD Yuan BYD CompanyP8 BESTUNE B30EV China FAW Group CorporationP9 EMGRAND EV Geely Auto CompanyP10 ORA R1 Great Wall Motor Company Limited

Table 2 e emotional words with high word frequency

Emotional words Frequency Emotional words Frequency Emotional words FrequencyRight 725476 Powerful 524895 Low 302589Fine 707542 Not too bad 524123 Expensive 212587Smooth 694575 Not very good 487256 Pretty good 128456Not bad 675821 Uneconomic 462358 Excellent 114753Not so dusty 658426 Loud 458697 Terrible 102475High 654895 So-so 458241 Poor 102452Comfortable 641257 Beautiful 458214 Reasonable 85426Stable 607002 Super good 436248 Bad 83856Passable 597426 Spacious 385462 Insensitive 68542Noisy 584712 High-end 368456 Awful 62147Cheap 548968 Accurate 325869 Small 56235

Table 3 Emotional terms of customersrsquo satisfaction

Customersrsquo satisfaction Emotional wordsPractical feelings ≫ expectations Super good pretty good excellent superb high etcPractical feelingsgt expectations Not bad right fine not so dusty cheap etcPractical feelings expectations Passable not too bad etcPractical feelingslt expectations Not very good so-so etcPractical feelings ≫ltlt expectations Loud poor bad low terrible awful expensive noisy etc

8 Mathematical Problems in Engineering

between dimensions and criteria is emailed to 8 experts whoare proficient in the automobile industry and have severalyears of experience in the BEVs field Moreover expertsrsquoprofessional and practical experience can strongly supportpersonal interviews and questionnaires Experts can use afive-point scale ranging from 0ndash4 to grade mutual influencesbased on pairwise comparisons [45] en we collect theexpertsrsquo results and compute the average scores of all criteriato form the initial direct-effect matrix as is shown in Table 7e statistical significance confidence of scores by experts is

9513 (greater than 95 ie the gap error is only 487orlt 5) which indicates consistent responses

e modified VIKOR questionnaire is distributed tocustomers who have bought BEVs or intend to buy BEVs toscore the criteria on an eleven-point scale ranging from0ndash10 Of the 550 questionnaires distributed 540 validquestionnaires are obtained e internal consistency ofcustomer ratings is tested using Cronbachrsquos alpha [107] enull hypothesis of the scores associated with each BEV isdefined as no difference between customer ratings e

Table 4 Average score of word-of-mouth with sentiment words

Customersrsquo satisfaction Emotional wordsSuper good pretty good excellent superb comfortable high etc 5Not bad right fine not so dusty etc 4Passable not too bad etc 3Not very good so-so etc 2Loud poor bad low terrible awful etc 1

Table 5 e word-of-mouth data analysis results

Topics KeywordsTopic 1 dynamics Power accelerate full of power torque top speed powerful climbingTopic 2technology Charge quick charge economy battery capacity range lithium battery electricity consumption

Topic 3 safety Braking stability control vehicle weight sensitivity steering accurate start smoothly

Topic 4 comfort Space seat rear seats seat backs suspension damping fashion high-end face value taillight design wheeldashboard workmanship large screen recorder high beams

Topic 5 cost Price virtual-high subsidies incentives policies restricted licence repair maintenance after-sale service supportingfacilities market ratios cost-efficient quality assurance

Table 6 Descriptions of the evaluation dimensions and criteria of BEVs

Dimensions Criteria Descriptions Source

Dynamics (A)

Maximum power (A1) Maximum power output that the BEVs can achieve [53 86]Max torque (A2) e higher the torque is the better the vehicle acceleration [51]Top speed (A3) Top speed that can be driven on good road [27 30 87]Acceleration time

(A4) Acceleration time refers to the acceleration time of 100 kilometers [27 30 88 89]

Technology(B)

Driving range (B1) In the standards of the new European driving Cycle the maximum mileage theEVs can run without a recharge [90ndash93]

Electricityconsumption (B2) Electricity consumption for 100 kilometers [27]

Charge time (B3) e time required to charge the battery to 80 of capacity using high-powerdirect current (DC) charging [90 91 94]

Safety (C)

Curb weight (C1) Empty weight is one of the most important factors to check the safety of a vehicle [95]Braking properties

(C2) e shorter the braking distance is the higher the safety [96 97]

Operating stability(C3) It is a combination of mobility and stability [61]

Comfort (D)

Car space (D1) It includes the size of the driver control space luggage space and passengercompartment [98]

Suspension (D2) Vibration characteristics ensure normal and comfortable driving [99]Car seat (D3) It includes electrically adjustable soft and comfortable material etc [87]

Exterior and interior(D4)

Exterior involves many aspects such as appearance and color interior involvesreasonable color matching complete and intelligent equipment etc [61 100]

Cost (E)Price (E1) Whether the price is within the acceptable range [101ndash103]

Incentives (E2) It includes purchase tax subsidies etc [104ndash106]After-sales cost (E3) It includes vehicle repairs maintenance and other expenses [4 12]

Mathematical Problems in Engineering 9

alternative hypothesis assumes that the scores are differente Cronbachrsquos alpha values related to these ten hypothesesare 0894 0882 0930 0855 0838 0823 0825 08020750 and 0835 respectively e null hypothesis is sig-nificant It is hoped that this study can help the governmentdepartments and automobile enterprisesrsquo decision-makers toeffectively improve customersrsquo purchasing decisions therebyenhancing the BEVrsquos development and competitiveness

43 Estimating the Relationships among Dimensions andCriteria e DEMATEL technique is applied to obtain thecause-effect relationships and to construct the INRM amongdimensions and criteria According to the responses from 7experts the direct effect 17times17 matrix G [aij] is con-structed in Table 7 e normalized direct-influence matrixX in Table 8 is derived by equations (1)ndash(3) e total in-fluence matrix Tc of the criteria and matrix TD of the di-mensions are separately listed in Tables 9ndash10 Furthermorethe (ri+ si) and (ri minus si) values of indicators can be obtainedfrom the total influence matrix as is shown in Table 11 eimplication of (ri+ si) presents the intensity of the influencethat the ith criterion plays in the problem and (ri minus si)presents the size of the ith criterionrsquos direct impact on othersWhen (ri minus si) is positive ith criterion influences other cri-teria On the contrary if (ri minus si) is negative ith criterion isinfluenced by other criteria e INRM is drawn with (ri+ si)as the horizontal axis and (ri minus si) as the vertical axisreflecting the cause-and-effect relationships between di-mensions and criteria as shown in Figure 2

As shown in Table 11 the five dimensions can be pri-oritised as EgtAgtBgtCgtD based on the (ri+ si) valueswhere the cost (E) has the highest impact intensity with avalue of 0402 e comfort (D) is the most vulnerable in itsinfluence with a value of 0154 e influential relationship(ri minus si) indicates that safety (C) technology (B) dynamics(A) and comfort (D) have the highest direct influence on theother dimensions in relationship studies with the values of0084 0062 0058 and 0052 On the contrary cost (E) hasthe lowest negative value of minus0256 which is easily affectedby other dimensions Hence based on the causal relation-ships derived in Figure 2 the safety (C) affects technology(B) dynamics (A) and comfort (D) whereas cost (E) isinfluenced by all other dimensions

e network relationship between criteria under eachdimension can be shown in Figure 2 e (ri+ si) and (ri minus si)are shown in Table 11 Overall from the (ri+ si) valuesmaximum power (A1) is considered the most importantcriterion of all criteria with the value of 0397 From the(ri minus si) values max torque (A2) driving range (B1) curbweight (C1) suspension (D2) and incentives (E2) have agreater influence on the other criteria in the individualdimensions According to Figure 2 specifically in the dy-namics (A) dimension max torque (0110) and maximumpower (0085) have the strongest influence on max speed(minus0056) and acceleration time (minus0019) is indicates thatmax torque should be improved first to prompt customerpurchase intention In the technology (B) dimension thedriving range (0019) directly influences the remaining

criteria including electricity consumption (minus0009) andcharge time (minus0011) us it is important to achieve alonger driving range to ease consumersrsquo range anxietythereby affecting the degree of purchase Automobile en-terprises should increase technological investment and givepriority for expanding driving Under the same circum-stances consumers usually choose new energy vehicles witha high range [54] In terms of safety (C) dimension curbweight (0194) exerts a direct effect on the other criteriaincluding braking properties (minus0093) and operating stability(minus0101) erefore lightweight technology should be sup-ported and promoted to reduce curb weight and thus im-prove the safety of BEVs In the comfort (D) dimension thecriterion of suspension (0048) and car seat (0017) stronglyinfluence exterior and interior (minus0016) and car space(minus0050) us optimizing the suspension to improve op-erating comfort is the most influential way to encourage acustomer to purchase new vehicles In the cost (E) di-mension incentives (0072) exert a direct effect on theremaining criteria including price (minus0033) and after-salescost (minus0039) erefore incentives play the most importantrole in consumersrsquo decisions e general improvementpriorities can be sequenced as E2 E1 and E3

44 Determining the InfluenceWeights After identifying therelationships among indicators through the DEMATELtechnique the influence weights of those indicators can beobtained by the DANP method Initially the unweightedsuper-matrixWc in Table 12 can be derived by equation (11)e weighted super-matrixW lowast c in Table 13 can be obtainedusing equation (12) Finally the weight of each criterion isacquired by limiting the power of the weighted super-matrix

According to the influence weights shown in Table 14 interms of dimensions the cost (0400) is ranked as the mostimportant weight while the comfort (0062) is ranked as theleast important one Based on the influence weights (globalweights) associated with seventeen criteria empirical resultsreveal that price (E1) incentives (E2) and after-sales cost(E3) are ranked as the top criteria Concretely price gains thehighest point of 0191 followed by incentives (0106) andafter-sales cost (0103) Moreover the influence weights ofsuspension (0010) car seat (0010) exterior and interior(0009) are relatively low which means that these criteriahave the least impact

45 Obtaining the Selection Strategies and Optimal Pathis study aims to propose the most effective strategies toimprove customersrsquo decisions for BEVs in China In thissection the Modified VIKOR method is employed toevaluate ten models based on the opinion of customers erange of fik is defined from 0 to 10 where 0 is the worst leveland 10 is the desired level in the evaluation By introducingthe relative weight versus each criterion the average gap Ekandmaximal gapQk is derivedUk is also derived by setting v

as 05 us each BEV could be evaluated and ranked andthe results are shown in Table 15 e key to solving theproblem can be identified according to this integrated indexfrom the dimension perspective or the criteria perspective

10 Mathematical Problems in Engineering

Tabl

e7

Initial

direct-effect

matrixG

A1

A2

A3

A4

B1B2

B3C1

C2C3

D1

D2

D3

D4

E1E2

E3To

tal

A1

0000

0286

3429

1857

3286

1857

2429

0000

2143

1286

0000

0143

0000

0000

2429

0429

1571

21143

A2

0714

0000

1000

3429

1143

0286

0000

0000

2714

1143

0000

0429

0000

0000

2286

0429

2429

16000

A3

1286

1286

0000

0571

1286

2857

0429

0000

1714

1429

0000

0143

0000

0000

1714

3286

0143

16143

A4

1143

0429

0286

0000

0429

1143

0286

0000

1429

1000

0000

0286

0000

0000

2571

0571

2429

12000

B11286

0000

2857

2571

0000

3714

0857

0143

0000

0000

0000

0000

0000

0000

3571

3714

2857

21571

B20714

0143

1571

0429

3571

0000

0714

0000

0143

0000

0000

0000

0000

0000

3429

3714

2714

17143

B30429

0000

0000

0286

0429

0714

0000

0000

0000

0000

0000

0000

0000

0000

3429

2857

1857

10000

C11286

1286

1286

1857

2571

1857

0000

0000

2857

2714

0286

0000

0143

1143

1714

3429

0286

22714

C21143

1571

0714

1429

0000

0143

0000

0000

0000

2857

0143

0571

0000

0286

3571

0143

2286

14857

C31000

0857

0571

0571

0000

0000

0000

0000

2571

0000

0143

0429

0000

0286

3571

0143

1857

12000

D1

0000

0000

0000

0000

0000

0000

0000

0571

0857

0143

0000

0143

2286

1571

0429

0000

0143

6143

D2

0000

0000

1429

1286

0000

0286

0000

0714

2714

3429

0571

0000

0571

0714

2429

0143

1571

15857

D3

0000

0000

0000

0000

0000

0143

0000

0857

0000

0000

2429

0286

0000

1286

0857

0000

0714

6571

D4

0429

0000

0143

0286

0714

0143

0000

0286

0000

0000

2429

0143

0571

0000

0571

0000

0286

6000

E11857

0714

1429

1286

1286

0714

0000

0143

0429

0429

1571

0143

0143

0000

0000

0857

0143

11143

E20000

0000

0000

0000

0571

0000

0143

0286

0000

0000

0000

0000

0000

0000

2714

0000

1143

4857

E30000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0571

0143

0000

0714

Total

11286

6571

14714

15857

15286

13857

4857

3000

17571

14429

7571

2714

3714

5286

35857

19857

22429

Note1

(n

(n

minus1)

)1113936

n i1

1113936n j

1(|t

p ijminus

tpminus1

ij|t

p ij)

times100

487lt5

iesig

nificantc

onfid

ence

is9513

where

p7deno

testhenu

mberof

experts

tp ijistheaverageinflu

ence

oficriterion

onjandndeno

tes

numberof

criteriahere

n17

andn

timesnmatrix

Mathematical Problems in Engineering 11

e order of priority for achieving the desired level can bedetermined by the weights of the performance values fromhigh to low and the gap values from high to low

As indicated in Table 15 Aion S (P3) produced by GACNE company presents the smallest gap (0276) and thereforeranks first followed by the BAIC EU Series (P2 0296) MGEZS (P4 0297) BESTUNE B30EV (P8 0304) GEOMETRY

A (P6 0305) EMGRAND EV (P9 0340) Aeolus E70 (P50341) BYD Yuan (P7 0344) ORA R1 (P10 0471) andBAOJUN E100 (P1 0694) in this regard Integration of thescores of Aion S (P3) further demonstrates that the gap forthe dynamics (A) dimension is 0191 and that for the sus-pension (D2) criterion is 0367 constituting the largest gapswhich Aion S should improve as a priority e integration

Table 8 e normalized direct-influence matrix X

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3A1 0000 0008 0096 0052 0092 0052 0068 0000 0060 0036 0000 0004 0000 0000 0068 0012 0044A2 0020 0000 0028 0096 0032 0008 0000 0000 0076 0032 0000 0012 0000 0000 0064 0012 0068A3 0036 0036 0000 0016 0036 0080 0012 0000 0048 0040 0000 0004 0000 0000 0048 0092 0004A4 0032 0012 0008 0000 0012 0032 0008 0000 0040 0028 0000 0008 0000 0000 0072 0016 0068B1 0036 0000 0080 0072 0000 0104 0024 0004 0000 0000 0000 0000 0000 0000 0100 0104 0080B2 0020 0004 0044 0012 0100 0000 0020 0000 0004 0000 0000 0000 0000 0000 0096 0104 0076B3 0012 0000 0000 0008 0012 0020 0000 0000 0000 0000 0000 0000 0000 0000 0096 0080 0052C1 0036 0036 0036 0052 0072 0052 0000 0000 0080 0076 0008 0000 0004 0032 0048 0096 0008C2 0032 0044 0020 0040 0000 0004 0000 0000 0000 0080 0004 0016 0000 0008 0100 0004 0064C3 0028 0024 0016 0016 0000 0000 0000 0000 0072 0000 0004 0012 0000 0008 0100 0004 0052D1 0000 0000 0000 0000 0000 0000 0000 0016 0024 0004 0000 0004 0064 0044 0012 0000 0004D2 0000 0000 0040 0036 0000 0008 0000 0020 0076 0096 0016 0000 0016 0020 0068 0004 0044D3 0000 0000 0000 0000 0000 0004 0000 0024 0000 0000 0068 0008 0000 0036 0024 0000 0020D4 0012 0000 0004 0008 0020 0004 0000 0008 0000 0000 0068 0004 0016 0000 0016 0000 0008E1 0052 0020 0040 0036 0036 0020 0000 0004 0012 0012 0044 0004 0004 0000 0000 0024 0004E2 0000 0000 0000 0000 0016 0000 0004 0008 0000 0000 0000 0000 0000 0000 0076 0000 0032E3 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0016 0004 0000

Table 9 e total influence matrix of criteria

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3 riA1 0024 0021 0120 0076 0114 0082 0076 0002 0078 0053 0006 0008 0001 0002 0128 0055 0082 0241A2 0037 0011 0045 0112 0045 0025 0006 0001 0092 0049 0006 0016 0001 0002 0103 0030 0094 0204A3 0051 0044 0021 0036 0058 0094 0020 0002 0063 0052 0005 0007 0001 0001 0096 0116 0036 0152A4 0043 0019 0023 0013 0026 0042 0013 0001 0050 0038 0005 0010 0001 0001 0099 0030 0085 0098B1 0055 0010 0101 0088 0031 0125 0033 0006 0016 0013 0007 0002 0001 0001 0149 0138 0110 0189B2 0036 0011 0064 0030 0116 0023 0027 0002 0014 0008 0006 0002 0001 0001 0134 0131 0099 0166B3 0020 0003 0010 0016 0023 0027 0003 0001 0005 0004 0005 0001 0001 0000 0113 0090 0062 0053C1 0059 0049 0063 0079 0096 0076 0010 0003 0102 0094 0017 0005 0006 0035 0113 0127 0050 0199C2 0047 0052 0036 0057 0015 0016 0005 0001 0021 0091 0011 0019 0002 0010 0131 0017 0084 0113C3 0041 0032 0030 0031 0012 0010 0004 0001 0083 0014 0011 0015 0002 0010 0124 0015 0068 0099D1 0004 0003 0004 0004 0004 0003 0001 0018 0028 0009 0009 0005 0065 0048 0021 0004 0010 0127D2 0017 0012 0053 0051 0013 0020 0003 0022 0094 0111 0024 0004 0018 0024 0105 0019 0066 0071D3 0004 0002 0004 0004 0005 0007 0001 0026 0006 0004 0073 0009 0006 0040 0031 0005 0024 0127D4 0016 0002 0010 0013 0025 0010 0002 0010 0006 0004 0071 0005 0021 0004 0026 0007 0014 0101E1 0062 0026 0055 0050 0051 0036 0007 0006 0026 0022 0046 0006 0007 0003 0031 0042 0024 0097E2 0006 0003 0006 0006 0021 0005 0005 0009 0003 0003 0004 0001 0001 0001 0082 0007 0036 0125E3 0001 0000 0001 0001 0001 0001 0000 0000 0000 0000 0001 0000 0000 0000 0017 0005 0001 0022si 0156 0094 0208 0236 0169 0175 0064 0005 0207 0200 0177 0024 0110 0116 0130 0054 0061

Table 10 e total influence matrix of dimension

Dimension A B C D E riA 0043 0050 0040 0005 0079 0217B 0037 0045 0008 0002 0114 0206C 0048 0027 0046 0012 0081 0214D 0013 0008 0028 0027 0028 0103E 0018 0014 0008 0006 0027 0073si 0159 0144 0129 0051 0329

12 Mathematical Problems in Engineering

of the scores of the BAIC EU Series (P2) in the DANP showsthat the gap of the safety (C) dimension is 0267 and that ofthe alliance with operating stability (C3) criterion is 0383constituting the largest gaps which the BAIC EU Seriesshould improve as a priority e integration of the scores ofBAOJUN E100 (P1) in the DANP shows that the gap of thecomfort (D) dimension is 0467 and that of the charge time

(B3) criterion is 1000 constituting the largest gaps whichBAOJUN E100 should improve as a priority e integrationof the scores of MG EZS (P4) in the DANP shows that thegap of the vehicle comfort (D) dimension is 0276 and that ofthe suspension (D2) criterion is 0367 constituting thelargest gaps which MG EZS should improve as a prioritye integration of the scores of Aeolus E70 (P5) in the

Table 11 Sum of influences givenreceived on dimensionscriteria

Criteria ri si ri+ si ri minus siDynamics (A) 0217 0159 0376 0058Maximum power (A1) 0241 0156 0397 0085Max torque (A2) 0204 0094 0298 0110Max speed (A3) 0152 0208 0361 minus0056Acceleration time (A4) 0098 0236 0334 minus0139Technology (B) 0206 0144 0351 0062Driving range (B1) 0189 0169 0358 0019Electricity consumption (B2) 0166 0175 0341 minus0009Charge time (B3) 0053 0064 0116 minus0011Safety (C) 0214 0129 0343 0084Curb weight (C1) 0199 0005 0205 0194Braking properties (C2) 0113 0207 0320 minus0093Operating stability (C3) 0099 0200 0299 minus0101Comfort (D) 0103 0051 0154 0052Car space (D1) 0127 0177 0304 minus0050Suspension (D2) 0071 0024 0095 0048Car seat (D3) 0127 0110 0238 0017Exterior and interior (D4) 0101 0116 0217 minus0016Cost (E) 0073 0329 0402 minus0256Price (E1) 0097 0130 0227 minus0033Incentives (E2) 0125 0054 0179 0072After-sales cost (E3) 0022 0061 0083 minus0039

ri-si

ri+si

005

010

020

-005

-010

030025020 035

C2

C3

C1

015

026018

0010

-0005

0015

0005

ri-si

ri+si010 034

B1

B3B2

042

-0010

0020

02 025 03 035 04504

006

015

-024

012

AB

-018

-012

-006

CD

E

ri+si

ri-si

-0030

000 01-0015

02

0045

0030

015 025

0015

D1

03

D3

D4

D2

ri+si

ri-si

0060

-0045

0060025

-005040035030

-015

015

010 A1A2

A4

A3

ri-si

ri+si005

-010

010

005

-005

ri+si

ri-si

015005 025020E1E3

E2

010

Figure 2 e INRM of each dimension and criterion

Mathematical Problems in Engineering 13

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

between dimensions and criteria is emailed to 8 experts whoare proficient in the automobile industry and have severalyears of experience in the BEVs field Moreover expertsrsquoprofessional and practical experience can strongly supportpersonal interviews and questionnaires Experts can use afive-point scale ranging from 0ndash4 to grade mutual influencesbased on pairwise comparisons [45] en we collect theexpertsrsquo results and compute the average scores of all criteriato form the initial direct-effect matrix as is shown in Table 7e statistical significance confidence of scores by experts is

9513 (greater than 95 ie the gap error is only 487orlt 5) which indicates consistent responses

e modified VIKOR questionnaire is distributed tocustomers who have bought BEVs or intend to buy BEVs toscore the criteria on an eleven-point scale ranging from0ndash10 Of the 550 questionnaires distributed 540 validquestionnaires are obtained e internal consistency ofcustomer ratings is tested using Cronbachrsquos alpha [107] enull hypothesis of the scores associated with each BEV isdefined as no difference between customer ratings e

Table 4 Average score of word-of-mouth with sentiment words

Customersrsquo satisfaction Emotional wordsSuper good pretty good excellent superb comfortable high etc 5Not bad right fine not so dusty etc 4Passable not too bad etc 3Not very good so-so etc 2Loud poor bad low terrible awful etc 1

Table 5 e word-of-mouth data analysis results

Topics KeywordsTopic 1 dynamics Power accelerate full of power torque top speed powerful climbingTopic 2technology Charge quick charge economy battery capacity range lithium battery electricity consumption

Topic 3 safety Braking stability control vehicle weight sensitivity steering accurate start smoothly

Topic 4 comfort Space seat rear seats seat backs suspension damping fashion high-end face value taillight design wheeldashboard workmanship large screen recorder high beams

Topic 5 cost Price virtual-high subsidies incentives policies restricted licence repair maintenance after-sale service supportingfacilities market ratios cost-efficient quality assurance

Table 6 Descriptions of the evaluation dimensions and criteria of BEVs

Dimensions Criteria Descriptions Source

Dynamics (A)

Maximum power (A1) Maximum power output that the BEVs can achieve [53 86]Max torque (A2) e higher the torque is the better the vehicle acceleration [51]Top speed (A3) Top speed that can be driven on good road [27 30 87]Acceleration time

(A4) Acceleration time refers to the acceleration time of 100 kilometers [27 30 88 89]

Technology(B)

Driving range (B1) In the standards of the new European driving Cycle the maximum mileage theEVs can run without a recharge [90ndash93]

Electricityconsumption (B2) Electricity consumption for 100 kilometers [27]

Charge time (B3) e time required to charge the battery to 80 of capacity using high-powerdirect current (DC) charging [90 91 94]

Safety (C)

Curb weight (C1) Empty weight is one of the most important factors to check the safety of a vehicle [95]Braking properties

(C2) e shorter the braking distance is the higher the safety [96 97]

Operating stability(C3) It is a combination of mobility and stability [61]

Comfort (D)

Car space (D1) It includes the size of the driver control space luggage space and passengercompartment [98]

Suspension (D2) Vibration characteristics ensure normal and comfortable driving [99]Car seat (D3) It includes electrically adjustable soft and comfortable material etc [87]

Exterior and interior(D4)

Exterior involves many aspects such as appearance and color interior involvesreasonable color matching complete and intelligent equipment etc [61 100]

Cost (E)Price (E1) Whether the price is within the acceptable range [101ndash103]

Incentives (E2) It includes purchase tax subsidies etc [104ndash106]After-sales cost (E3) It includes vehicle repairs maintenance and other expenses [4 12]

Mathematical Problems in Engineering 9

alternative hypothesis assumes that the scores are differente Cronbachrsquos alpha values related to these ten hypothesesare 0894 0882 0930 0855 0838 0823 0825 08020750 and 0835 respectively e null hypothesis is sig-nificant It is hoped that this study can help the governmentdepartments and automobile enterprisesrsquo decision-makers toeffectively improve customersrsquo purchasing decisions therebyenhancing the BEVrsquos development and competitiveness

43 Estimating the Relationships among Dimensions andCriteria e DEMATEL technique is applied to obtain thecause-effect relationships and to construct the INRM amongdimensions and criteria According to the responses from 7experts the direct effect 17times17 matrix G [aij] is con-structed in Table 7 e normalized direct-influence matrixX in Table 8 is derived by equations (1)ndash(3) e total in-fluence matrix Tc of the criteria and matrix TD of the di-mensions are separately listed in Tables 9ndash10 Furthermorethe (ri+ si) and (ri minus si) values of indicators can be obtainedfrom the total influence matrix as is shown in Table 11 eimplication of (ri+ si) presents the intensity of the influencethat the ith criterion plays in the problem and (ri minus si)presents the size of the ith criterionrsquos direct impact on othersWhen (ri minus si) is positive ith criterion influences other cri-teria On the contrary if (ri minus si) is negative ith criterion isinfluenced by other criteria e INRM is drawn with (ri+ si)as the horizontal axis and (ri minus si) as the vertical axisreflecting the cause-and-effect relationships between di-mensions and criteria as shown in Figure 2

As shown in Table 11 the five dimensions can be pri-oritised as EgtAgtBgtCgtD based on the (ri+ si) valueswhere the cost (E) has the highest impact intensity with avalue of 0402 e comfort (D) is the most vulnerable in itsinfluence with a value of 0154 e influential relationship(ri minus si) indicates that safety (C) technology (B) dynamics(A) and comfort (D) have the highest direct influence on theother dimensions in relationship studies with the values of0084 0062 0058 and 0052 On the contrary cost (E) hasthe lowest negative value of minus0256 which is easily affectedby other dimensions Hence based on the causal relation-ships derived in Figure 2 the safety (C) affects technology(B) dynamics (A) and comfort (D) whereas cost (E) isinfluenced by all other dimensions

e network relationship between criteria under eachdimension can be shown in Figure 2 e (ri+ si) and (ri minus si)are shown in Table 11 Overall from the (ri+ si) valuesmaximum power (A1) is considered the most importantcriterion of all criteria with the value of 0397 From the(ri minus si) values max torque (A2) driving range (B1) curbweight (C1) suspension (D2) and incentives (E2) have agreater influence on the other criteria in the individualdimensions According to Figure 2 specifically in the dy-namics (A) dimension max torque (0110) and maximumpower (0085) have the strongest influence on max speed(minus0056) and acceleration time (minus0019) is indicates thatmax torque should be improved first to prompt customerpurchase intention In the technology (B) dimension thedriving range (0019) directly influences the remaining

criteria including electricity consumption (minus0009) andcharge time (minus0011) us it is important to achieve alonger driving range to ease consumersrsquo range anxietythereby affecting the degree of purchase Automobile en-terprises should increase technological investment and givepriority for expanding driving Under the same circum-stances consumers usually choose new energy vehicles witha high range [54] In terms of safety (C) dimension curbweight (0194) exerts a direct effect on the other criteriaincluding braking properties (minus0093) and operating stability(minus0101) erefore lightweight technology should be sup-ported and promoted to reduce curb weight and thus im-prove the safety of BEVs In the comfort (D) dimension thecriterion of suspension (0048) and car seat (0017) stronglyinfluence exterior and interior (minus0016) and car space(minus0050) us optimizing the suspension to improve op-erating comfort is the most influential way to encourage acustomer to purchase new vehicles In the cost (E) di-mension incentives (0072) exert a direct effect on theremaining criteria including price (minus0033) and after-salescost (minus0039) erefore incentives play the most importantrole in consumersrsquo decisions e general improvementpriorities can be sequenced as E2 E1 and E3

44 Determining the InfluenceWeights After identifying therelationships among indicators through the DEMATELtechnique the influence weights of those indicators can beobtained by the DANP method Initially the unweightedsuper-matrixWc in Table 12 can be derived by equation (11)e weighted super-matrixW lowast c in Table 13 can be obtainedusing equation (12) Finally the weight of each criterion isacquired by limiting the power of the weighted super-matrix

According to the influence weights shown in Table 14 interms of dimensions the cost (0400) is ranked as the mostimportant weight while the comfort (0062) is ranked as theleast important one Based on the influence weights (globalweights) associated with seventeen criteria empirical resultsreveal that price (E1) incentives (E2) and after-sales cost(E3) are ranked as the top criteria Concretely price gains thehighest point of 0191 followed by incentives (0106) andafter-sales cost (0103) Moreover the influence weights ofsuspension (0010) car seat (0010) exterior and interior(0009) are relatively low which means that these criteriahave the least impact

45 Obtaining the Selection Strategies and Optimal Pathis study aims to propose the most effective strategies toimprove customersrsquo decisions for BEVs in China In thissection the Modified VIKOR method is employed toevaluate ten models based on the opinion of customers erange of fik is defined from 0 to 10 where 0 is the worst leveland 10 is the desired level in the evaluation By introducingthe relative weight versus each criterion the average gap Ekandmaximal gapQk is derivedUk is also derived by setting v

as 05 us each BEV could be evaluated and ranked andthe results are shown in Table 15 e key to solving theproblem can be identified according to this integrated indexfrom the dimension perspective or the criteria perspective

10 Mathematical Problems in Engineering

Tabl

e7

Initial

direct-effect

matrixG

A1

A2

A3

A4

B1B2

B3C1

C2C3

D1

D2

D3

D4

E1E2

E3To

tal

A1

0000

0286

3429

1857

3286

1857

2429

0000

2143

1286

0000

0143

0000

0000

2429

0429

1571

21143

A2

0714

0000

1000

3429

1143

0286

0000

0000

2714

1143

0000

0429

0000

0000

2286

0429

2429

16000

A3

1286

1286

0000

0571

1286

2857

0429

0000

1714

1429

0000

0143

0000

0000

1714

3286

0143

16143

A4

1143

0429

0286

0000

0429

1143

0286

0000

1429

1000

0000

0286

0000

0000

2571

0571

2429

12000

B11286

0000

2857

2571

0000

3714

0857

0143

0000

0000

0000

0000

0000

0000

3571

3714

2857

21571

B20714

0143

1571

0429

3571

0000

0714

0000

0143

0000

0000

0000

0000

0000

3429

3714

2714

17143

B30429

0000

0000

0286

0429

0714

0000

0000

0000

0000

0000

0000

0000

0000

3429

2857

1857

10000

C11286

1286

1286

1857

2571

1857

0000

0000

2857

2714

0286

0000

0143

1143

1714

3429

0286

22714

C21143

1571

0714

1429

0000

0143

0000

0000

0000

2857

0143

0571

0000

0286

3571

0143

2286

14857

C31000

0857

0571

0571

0000

0000

0000

0000

2571

0000

0143

0429

0000

0286

3571

0143

1857

12000

D1

0000

0000

0000

0000

0000

0000

0000

0571

0857

0143

0000

0143

2286

1571

0429

0000

0143

6143

D2

0000

0000

1429

1286

0000

0286

0000

0714

2714

3429

0571

0000

0571

0714

2429

0143

1571

15857

D3

0000

0000

0000

0000

0000

0143

0000

0857

0000

0000

2429

0286

0000

1286

0857

0000

0714

6571

D4

0429

0000

0143

0286

0714

0143

0000

0286

0000

0000

2429

0143

0571

0000

0571

0000

0286

6000

E11857

0714

1429

1286

1286

0714

0000

0143

0429

0429

1571

0143

0143

0000

0000

0857

0143

11143

E20000

0000

0000

0000

0571

0000

0143

0286

0000

0000

0000

0000

0000

0000

2714

0000

1143

4857

E30000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0571

0143

0000

0714

Total

11286

6571

14714

15857

15286

13857

4857

3000

17571

14429

7571

2714

3714

5286

35857

19857

22429

Note1

(n

(n

minus1)

)1113936

n i1

1113936n j

1(|t

p ijminus

tpminus1

ij|t

p ij)

times100

487lt5

iesig

nificantc

onfid

ence

is9513

where

p7deno

testhenu

mberof

experts

tp ijistheaverageinflu

ence

oficriterion

onjandndeno

tes

numberof

criteriahere

n17

andn

timesnmatrix

Mathematical Problems in Engineering 11

e order of priority for achieving the desired level can bedetermined by the weights of the performance values fromhigh to low and the gap values from high to low

As indicated in Table 15 Aion S (P3) produced by GACNE company presents the smallest gap (0276) and thereforeranks first followed by the BAIC EU Series (P2 0296) MGEZS (P4 0297) BESTUNE B30EV (P8 0304) GEOMETRY

A (P6 0305) EMGRAND EV (P9 0340) Aeolus E70 (P50341) BYD Yuan (P7 0344) ORA R1 (P10 0471) andBAOJUN E100 (P1 0694) in this regard Integration of thescores of Aion S (P3) further demonstrates that the gap forthe dynamics (A) dimension is 0191 and that for the sus-pension (D2) criterion is 0367 constituting the largest gapswhich Aion S should improve as a priority e integration

Table 8 e normalized direct-influence matrix X

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3A1 0000 0008 0096 0052 0092 0052 0068 0000 0060 0036 0000 0004 0000 0000 0068 0012 0044A2 0020 0000 0028 0096 0032 0008 0000 0000 0076 0032 0000 0012 0000 0000 0064 0012 0068A3 0036 0036 0000 0016 0036 0080 0012 0000 0048 0040 0000 0004 0000 0000 0048 0092 0004A4 0032 0012 0008 0000 0012 0032 0008 0000 0040 0028 0000 0008 0000 0000 0072 0016 0068B1 0036 0000 0080 0072 0000 0104 0024 0004 0000 0000 0000 0000 0000 0000 0100 0104 0080B2 0020 0004 0044 0012 0100 0000 0020 0000 0004 0000 0000 0000 0000 0000 0096 0104 0076B3 0012 0000 0000 0008 0012 0020 0000 0000 0000 0000 0000 0000 0000 0000 0096 0080 0052C1 0036 0036 0036 0052 0072 0052 0000 0000 0080 0076 0008 0000 0004 0032 0048 0096 0008C2 0032 0044 0020 0040 0000 0004 0000 0000 0000 0080 0004 0016 0000 0008 0100 0004 0064C3 0028 0024 0016 0016 0000 0000 0000 0000 0072 0000 0004 0012 0000 0008 0100 0004 0052D1 0000 0000 0000 0000 0000 0000 0000 0016 0024 0004 0000 0004 0064 0044 0012 0000 0004D2 0000 0000 0040 0036 0000 0008 0000 0020 0076 0096 0016 0000 0016 0020 0068 0004 0044D3 0000 0000 0000 0000 0000 0004 0000 0024 0000 0000 0068 0008 0000 0036 0024 0000 0020D4 0012 0000 0004 0008 0020 0004 0000 0008 0000 0000 0068 0004 0016 0000 0016 0000 0008E1 0052 0020 0040 0036 0036 0020 0000 0004 0012 0012 0044 0004 0004 0000 0000 0024 0004E2 0000 0000 0000 0000 0016 0000 0004 0008 0000 0000 0000 0000 0000 0000 0076 0000 0032E3 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0016 0004 0000

Table 9 e total influence matrix of criteria

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3 riA1 0024 0021 0120 0076 0114 0082 0076 0002 0078 0053 0006 0008 0001 0002 0128 0055 0082 0241A2 0037 0011 0045 0112 0045 0025 0006 0001 0092 0049 0006 0016 0001 0002 0103 0030 0094 0204A3 0051 0044 0021 0036 0058 0094 0020 0002 0063 0052 0005 0007 0001 0001 0096 0116 0036 0152A4 0043 0019 0023 0013 0026 0042 0013 0001 0050 0038 0005 0010 0001 0001 0099 0030 0085 0098B1 0055 0010 0101 0088 0031 0125 0033 0006 0016 0013 0007 0002 0001 0001 0149 0138 0110 0189B2 0036 0011 0064 0030 0116 0023 0027 0002 0014 0008 0006 0002 0001 0001 0134 0131 0099 0166B3 0020 0003 0010 0016 0023 0027 0003 0001 0005 0004 0005 0001 0001 0000 0113 0090 0062 0053C1 0059 0049 0063 0079 0096 0076 0010 0003 0102 0094 0017 0005 0006 0035 0113 0127 0050 0199C2 0047 0052 0036 0057 0015 0016 0005 0001 0021 0091 0011 0019 0002 0010 0131 0017 0084 0113C3 0041 0032 0030 0031 0012 0010 0004 0001 0083 0014 0011 0015 0002 0010 0124 0015 0068 0099D1 0004 0003 0004 0004 0004 0003 0001 0018 0028 0009 0009 0005 0065 0048 0021 0004 0010 0127D2 0017 0012 0053 0051 0013 0020 0003 0022 0094 0111 0024 0004 0018 0024 0105 0019 0066 0071D3 0004 0002 0004 0004 0005 0007 0001 0026 0006 0004 0073 0009 0006 0040 0031 0005 0024 0127D4 0016 0002 0010 0013 0025 0010 0002 0010 0006 0004 0071 0005 0021 0004 0026 0007 0014 0101E1 0062 0026 0055 0050 0051 0036 0007 0006 0026 0022 0046 0006 0007 0003 0031 0042 0024 0097E2 0006 0003 0006 0006 0021 0005 0005 0009 0003 0003 0004 0001 0001 0001 0082 0007 0036 0125E3 0001 0000 0001 0001 0001 0001 0000 0000 0000 0000 0001 0000 0000 0000 0017 0005 0001 0022si 0156 0094 0208 0236 0169 0175 0064 0005 0207 0200 0177 0024 0110 0116 0130 0054 0061

Table 10 e total influence matrix of dimension

Dimension A B C D E riA 0043 0050 0040 0005 0079 0217B 0037 0045 0008 0002 0114 0206C 0048 0027 0046 0012 0081 0214D 0013 0008 0028 0027 0028 0103E 0018 0014 0008 0006 0027 0073si 0159 0144 0129 0051 0329

12 Mathematical Problems in Engineering

of the scores of the BAIC EU Series (P2) in the DANP showsthat the gap of the safety (C) dimension is 0267 and that ofthe alliance with operating stability (C3) criterion is 0383constituting the largest gaps which the BAIC EU Seriesshould improve as a priority e integration of the scores ofBAOJUN E100 (P1) in the DANP shows that the gap of thecomfort (D) dimension is 0467 and that of the charge time

(B3) criterion is 1000 constituting the largest gaps whichBAOJUN E100 should improve as a priority e integrationof the scores of MG EZS (P4) in the DANP shows that thegap of the vehicle comfort (D) dimension is 0276 and that ofthe suspension (D2) criterion is 0367 constituting thelargest gaps which MG EZS should improve as a prioritye integration of the scores of Aeolus E70 (P5) in the

Table 11 Sum of influences givenreceived on dimensionscriteria

Criteria ri si ri+ si ri minus siDynamics (A) 0217 0159 0376 0058Maximum power (A1) 0241 0156 0397 0085Max torque (A2) 0204 0094 0298 0110Max speed (A3) 0152 0208 0361 minus0056Acceleration time (A4) 0098 0236 0334 minus0139Technology (B) 0206 0144 0351 0062Driving range (B1) 0189 0169 0358 0019Electricity consumption (B2) 0166 0175 0341 minus0009Charge time (B3) 0053 0064 0116 minus0011Safety (C) 0214 0129 0343 0084Curb weight (C1) 0199 0005 0205 0194Braking properties (C2) 0113 0207 0320 minus0093Operating stability (C3) 0099 0200 0299 minus0101Comfort (D) 0103 0051 0154 0052Car space (D1) 0127 0177 0304 minus0050Suspension (D2) 0071 0024 0095 0048Car seat (D3) 0127 0110 0238 0017Exterior and interior (D4) 0101 0116 0217 minus0016Cost (E) 0073 0329 0402 minus0256Price (E1) 0097 0130 0227 minus0033Incentives (E2) 0125 0054 0179 0072After-sales cost (E3) 0022 0061 0083 minus0039

ri-si

ri+si

005

010

020

-005

-010

030025020 035

C2

C3

C1

015

026018

0010

-0005

0015

0005

ri-si

ri+si010 034

B1

B3B2

042

-0010

0020

02 025 03 035 04504

006

015

-024

012

AB

-018

-012

-006

CD

E

ri+si

ri-si

-0030

000 01-0015

02

0045

0030

015 025

0015

D1

03

D3

D4

D2

ri+si

ri-si

0060

-0045

0060025

-005040035030

-015

015

010 A1A2

A4

A3

ri-si

ri+si005

-010

010

005

-005

ri+si

ri-si

015005 025020E1E3

E2

010

Figure 2 e INRM of each dimension and criterion

Mathematical Problems in Engineering 13

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

alternative hypothesis assumes that the scores are differente Cronbachrsquos alpha values related to these ten hypothesesare 0894 0882 0930 0855 0838 0823 0825 08020750 and 0835 respectively e null hypothesis is sig-nificant It is hoped that this study can help the governmentdepartments and automobile enterprisesrsquo decision-makers toeffectively improve customersrsquo purchasing decisions therebyenhancing the BEVrsquos development and competitiveness

43 Estimating the Relationships among Dimensions andCriteria e DEMATEL technique is applied to obtain thecause-effect relationships and to construct the INRM amongdimensions and criteria According to the responses from 7experts the direct effect 17times17 matrix G [aij] is con-structed in Table 7 e normalized direct-influence matrixX in Table 8 is derived by equations (1)ndash(3) e total in-fluence matrix Tc of the criteria and matrix TD of the di-mensions are separately listed in Tables 9ndash10 Furthermorethe (ri+ si) and (ri minus si) values of indicators can be obtainedfrom the total influence matrix as is shown in Table 11 eimplication of (ri+ si) presents the intensity of the influencethat the ith criterion plays in the problem and (ri minus si)presents the size of the ith criterionrsquos direct impact on othersWhen (ri minus si) is positive ith criterion influences other cri-teria On the contrary if (ri minus si) is negative ith criterion isinfluenced by other criteria e INRM is drawn with (ri+ si)as the horizontal axis and (ri minus si) as the vertical axisreflecting the cause-and-effect relationships between di-mensions and criteria as shown in Figure 2

As shown in Table 11 the five dimensions can be pri-oritised as EgtAgtBgtCgtD based on the (ri+ si) valueswhere the cost (E) has the highest impact intensity with avalue of 0402 e comfort (D) is the most vulnerable in itsinfluence with a value of 0154 e influential relationship(ri minus si) indicates that safety (C) technology (B) dynamics(A) and comfort (D) have the highest direct influence on theother dimensions in relationship studies with the values of0084 0062 0058 and 0052 On the contrary cost (E) hasthe lowest negative value of minus0256 which is easily affectedby other dimensions Hence based on the causal relation-ships derived in Figure 2 the safety (C) affects technology(B) dynamics (A) and comfort (D) whereas cost (E) isinfluenced by all other dimensions

e network relationship between criteria under eachdimension can be shown in Figure 2 e (ri+ si) and (ri minus si)are shown in Table 11 Overall from the (ri+ si) valuesmaximum power (A1) is considered the most importantcriterion of all criteria with the value of 0397 From the(ri minus si) values max torque (A2) driving range (B1) curbweight (C1) suspension (D2) and incentives (E2) have agreater influence on the other criteria in the individualdimensions According to Figure 2 specifically in the dy-namics (A) dimension max torque (0110) and maximumpower (0085) have the strongest influence on max speed(minus0056) and acceleration time (minus0019) is indicates thatmax torque should be improved first to prompt customerpurchase intention In the technology (B) dimension thedriving range (0019) directly influences the remaining

criteria including electricity consumption (minus0009) andcharge time (minus0011) us it is important to achieve alonger driving range to ease consumersrsquo range anxietythereby affecting the degree of purchase Automobile en-terprises should increase technological investment and givepriority for expanding driving Under the same circum-stances consumers usually choose new energy vehicles witha high range [54] In terms of safety (C) dimension curbweight (0194) exerts a direct effect on the other criteriaincluding braking properties (minus0093) and operating stability(minus0101) erefore lightweight technology should be sup-ported and promoted to reduce curb weight and thus im-prove the safety of BEVs In the comfort (D) dimension thecriterion of suspension (0048) and car seat (0017) stronglyinfluence exterior and interior (minus0016) and car space(minus0050) us optimizing the suspension to improve op-erating comfort is the most influential way to encourage acustomer to purchase new vehicles In the cost (E) di-mension incentives (0072) exert a direct effect on theremaining criteria including price (minus0033) and after-salescost (minus0039) erefore incentives play the most importantrole in consumersrsquo decisions e general improvementpriorities can be sequenced as E2 E1 and E3

44 Determining the InfluenceWeights After identifying therelationships among indicators through the DEMATELtechnique the influence weights of those indicators can beobtained by the DANP method Initially the unweightedsuper-matrixWc in Table 12 can be derived by equation (11)e weighted super-matrixW lowast c in Table 13 can be obtainedusing equation (12) Finally the weight of each criterion isacquired by limiting the power of the weighted super-matrix

According to the influence weights shown in Table 14 interms of dimensions the cost (0400) is ranked as the mostimportant weight while the comfort (0062) is ranked as theleast important one Based on the influence weights (globalweights) associated with seventeen criteria empirical resultsreveal that price (E1) incentives (E2) and after-sales cost(E3) are ranked as the top criteria Concretely price gains thehighest point of 0191 followed by incentives (0106) andafter-sales cost (0103) Moreover the influence weights ofsuspension (0010) car seat (0010) exterior and interior(0009) are relatively low which means that these criteriahave the least impact

45 Obtaining the Selection Strategies and Optimal Pathis study aims to propose the most effective strategies toimprove customersrsquo decisions for BEVs in China In thissection the Modified VIKOR method is employed toevaluate ten models based on the opinion of customers erange of fik is defined from 0 to 10 where 0 is the worst leveland 10 is the desired level in the evaluation By introducingthe relative weight versus each criterion the average gap Ekandmaximal gapQk is derivedUk is also derived by setting v

as 05 us each BEV could be evaluated and ranked andthe results are shown in Table 15 e key to solving theproblem can be identified according to this integrated indexfrom the dimension perspective or the criteria perspective

10 Mathematical Problems in Engineering

Tabl

e7

Initial

direct-effect

matrixG

A1

A2

A3

A4

B1B2

B3C1

C2C3

D1

D2

D3

D4

E1E2

E3To

tal

A1

0000

0286

3429

1857

3286

1857

2429

0000

2143

1286

0000

0143

0000

0000

2429

0429

1571

21143

A2

0714

0000

1000

3429

1143

0286

0000

0000

2714

1143

0000

0429

0000

0000

2286

0429

2429

16000

A3

1286

1286

0000

0571

1286

2857

0429

0000

1714

1429

0000

0143

0000

0000

1714

3286

0143

16143

A4

1143

0429

0286

0000

0429

1143

0286

0000

1429

1000

0000

0286

0000

0000

2571

0571

2429

12000

B11286

0000

2857

2571

0000

3714

0857

0143

0000

0000

0000

0000

0000

0000

3571

3714

2857

21571

B20714

0143

1571

0429

3571

0000

0714

0000

0143

0000

0000

0000

0000

0000

3429

3714

2714

17143

B30429

0000

0000

0286

0429

0714

0000

0000

0000

0000

0000

0000

0000

0000

3429

2857

1857

10000

C11286

1286

1286

1857

2571

1857

0000

0000

2857

2714

0286

0000

0143

1143

1714

3429

0286

22714

C21143

1571

0714

1429

0000

0143

0000

0000

0000

2857

0143

0571

0000

0286

3571

0143

2286

14857

C31000

0857

0571

0571

0000

0000

0000

0000

2571

0000

0143

0429

0000

0286

3571

0143

1857

12000

D1

0000

0000

0000

0000

0000

0000

0000

0571

0857

0143

0000

0143

2286

1571

0429

0000

0143

6143

D2

0000

0000

1429

1286

0000

0286

0000

0714

2714

3429

0571

0000

0571

0714

2429

0143

1571

15857

D3

0000

0000

0000

0000

0000

0143

0000

0857

0000

0000

2429

0286

0000

1286

0857

0000

0714

6571

D4

0429

0000

0143

0286

0714

0143

0000

0286

0000

0000

2429

0143

0571

0000

0571

0000

0286

6000

E11857

0714

1429

1286

1286

0714

0000

0143

0429

0429

1571

0143

0143

0000

0000

0857

0143

11143

E20000

0000

0000

0000

0571

0000

0143

0286

0000

0000

0000

0000

0000

0000

2714

0000

1143

4857

E30000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0571

0143

0000

0714

Total

11286

6571

14714

15857

15286

13857

4857

3000

17571

14429

7571

2714

3714

5286

35857

19857

22429

Note1

(n

(n

minus1)

)1113936

n i1

1113936n j

1(|t

p ijminus

tpminus1

ij|t

p ij)

times100

487lt5

iesig

nificantc

onfid

ence

is9513

where

p7deno

testhenu

mberof

experts

tp ijistheaverageinflu

ence

oficriterion

onjandndeno

tes

numberof

criteriahere

n17

andn

timesnmatrix

Mathematical Problems in Engineering 11

e order of priority for achieving the desired level can bedetermined by the weights of the performance values fromhigh to low and the gap values from high to low

As indicated in Table 15 Aion S (P3) produced by GACNE company presents the smallest gap (0276) and thereforeranks first followed by the BAIC EU Series (P2 0296) MGEZS (P4 0297) BESTUNE B30EV (P8 0304) GEOMETRY

A (P6 0305) EMGRAND EV (P9 0340) Aeolus E70 (P50341) BYD Yuan (P7 0344) ORA R1 (P10 0471) andBAOJUN E100 (P1 0694) in this regard Integration of thescores of Aion S (P3) further demonstrates that the gap forthe dynamics (A) dimension is 0191 and that for the sus-pension (D2) criterion is 0367 constituting the largest gapswhich Aion S should improve as a priority e integration

Table 8 e normalized direct-influence matrix X

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3A1 0000 0008 0096 0052 0092 0052 0068 0000 0060 0036 0000 0004 0000 0000 0068 0012 0044A2 0020 0000 0028 0096 0032 0008 0000 0000 0076 0032 0000 0012 0000 0000 0064 0012 0068A3 0036 0036 0000 0016 0036 0080 0012 0000 0048 0040 0000 0004 0000 0000 0048 0092 0004A4 0032 0012 0008 0000 0012 0032 0008 0000 0040 0028 0000 0008 0000 0000 0072 0016 0068B1 0036 0000 0080 0072 0000 0104 0024 0004 0000 0000 0000 0000 0000 0000 0100 0104 0080B2 0020 0004 0044 0012 0100 0000 0020 0000 0004 0000 0000 0000 0000 0000 0096 0104 0076B3 0012 0000 0000 0008 0012 0020 0000 0000 0000 0000 0000 0000 0000 0000 0096 0080 0052C1 0036 0036 0036 0052 0072 0052 0000 0000 0080 0076 0008 0000 0004 0032 0048 0096 0008C2 0032 0044 0020 0040 0000 0004 0000 0000 0000 0080 0004 0016 0000 0008 0100 0004 0064C3 0028 0024 0016 0016 0000 0000 0000 0000 0072 0000 0004 0012 0000 0008 0100 0004 0052D1 0000 0000 0000 0000 0000 0000 0000 0016 0024 0004 0000 0004 0064 0044 0012 0000 0004D2 0000 0000 0040 0036 0000 0008 0000 0020 0076 0096 0016 0000 0016 0020 0068 0004 0044D3 0000 0000 0000 0000 0000 0004 0000 0024 0000 0000 0068 0008 0000 0036 0024 0000 0020D4 0012 0000 0004 0008 0020 0004 0000 0008 0000 0000 0068 0004 0016 0000 0016 0000 0008E1 0052 0020 0040 0036 0036 0020 0000 0004 0012 0012 0044 0004 0004 0000 0000 0024 0004E2 0000 0000 0000 0000 0016 0000 0004 0008 0000 0000 0000 0000 0000 0000 0076 0000 0032E3 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0016 0004 0000

Table 9 e total influence matrix of criteria

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3 riA1 0024 0021 0120 0076 0114 0082 0076 0002 0078 0053 0006 0008 0001 0002 0128 0055 0082 0241A2 0037 0011 0045 0112 0045 0025 0006 0001 0092 0049 0006 0016 0001 0002 0103 0030 0094 0204A3 0051 0044 0021 0036 0058 0094 0020 0002 0063 0052 0005 0007 0001 0001 0096 0116 0036 0152A4 0043 0019 0023 0013 0026 0042 0013 0001 0050 0038 0005 0010 0001 0001 0099 0030 0085 0098B1 0055 0010 0101 0088 0031 0125 0033 0006 0016 0013 0007 0002 0001 0001 0149 0138 0110 0189B2 0036 0011 0064 0030 0116 0023 0027 0002 0014 0008 0006 0002 0001 0001 0134 0131 0099 0166B3 0020 0003 0010 0016 0023 0027 0003 0001 0005 0004 0005 0001 0001 0000 0113 0090 0062 0053C1 0059 0049 0063 0079 0096 0076 0010 0003 0102 0094 0017 0005 0006 0035 0113 0127 0050 0199C2 0047 0052 0036 0057 0015 0016 0005 0001 0021 0091 0011 0019 0002 0010 0131 0017 0084 0113C3 0041 0032 0030 0031 0012 0010 0004 0001 0083 0014 0011 0015 0002 0010 0124 0015 0068 0099D1 0004 0003 0004 0004 0004 0003 0001 0018 0028 0009 0009 0005 0065 0048 0021 0004 0010 0127D2 0017 0012 0053 0051 0013 0020 0003 0022 0094 0111 0024 0004 0018 0024 0105 0019 0066 0071D3 0004 0002 0004 0004 0005 0007 0001 0026 0006 0004 0073 0009 0006 0040 0031 0005 0024 0127D4 0016 0002 0010 0013 0025 0010 0002 0010 0006 0004 0071 0005 0021 0004 0026 0007 0014 0101E1 0062 0026 0055 0050 0051 0036 0007 0006 0026 0022 0046 0006 0007 0003 0031 0042 0024 0097E2 0006 0003 0006 0006 0021 0005 0005 0009 0003 0003 0004 0001 0001 0001 0082 0007 0036 0125E3 0001 0000 0001 0001 0001 0001 0000 0000 0000 0000 0001 0000 0000 0000 0017 0005 0001 0022si 0156 0094 0208 0236 0169 0175 0064 0005 0207 0200 0177 0024 0110 0116 0130 0054 0061

Table 10 e total influence matrix of dimension

Dimension A B C D E riA 0043 0050 0040 0005 0079 0217B 0037 0045 0008 0002 0114 0206C 0048 0027 0046 0012 0081 0214D 0013 0008 0028 0027 0028 0103E 0018 0014 0008 0006 0027 0073si 0159 0144 0129 0051 0329

12 Mathematical Problems in Engineering

of the scores of the BAIC EU Series (P2) in the DANP showsthat the gap of the safety (C) dimension is 0267 and that ofthe alliance with operating stability (C3) criterion is 0383constituting the largest gaps which the BAIC EU Seriesshould improve as a priority e integration of the scores ofBAOJUN E100 (P1) in the DANP shows that the gap of thecomfort (D) dimension is 0467 and that of the charge time

(B3) criterion is 1000 constituting the largest gaps whichBAOJUN E100 should improve as a priority e integrationof the scores of MG EZS (P4) in the DANP shows that thegap of the vehicle comfort (D) dimension is 0276 and that ofthe suspension (D2) criterion is 0367 constituting thelargest gaps which MG EZS should improve as a prioritye integration of the scores of Aeolus E70 (P5) in the

Table 11 Sum of influences givenreceived on dimensionscriteria

Criteria ri si ri+ si ri minus siDynamics (A) 0217 0159 0376 0058Maximum power (A1) 0241 0156 0397 0085Max torque (A2) 0204 0094 0298 0110Max speed (A3) 0152 0208 0361 minus0056Acceleration time (A4) 0098 0236 0334 minus0139Technology (B) 0206 0144 0351 0062Driving range (B1) 0189 0169 0358 0019Electricity consumption (B2) 0166 0175 0341 minus0009Charge time (B3) 0053 0064 0116 minus0011Safety (C) 0214 0129 0343 0084Curb weight (C1) 0199 0005 0205 0194Braking properties (C2) 0113 0207 0320 minus0093Operating stability (C3) 0099 0200 0299 minus0101Comfort (D) 0103 0051 0154 0052Car space (D1) 0127 0177 0304 minus0050Suspension (D2) 0071 0024 0095 0048Car seat (D3) 0127 0110 0238 0017Exterior and interior (D4) 0101 0116 0217 minus0016Cost (E) 0073 0329 0402 minus0256Price (E1) 0097 0130 0227 minus0033Incentives (E2) 0125 0054 0179 0072After-sales cost (E3) 0022 0061 0083 minus0039

ri-si

ri+si

005

010

020

-005

-010

030025020 035

C2

C3

C1

015

026018

0010

-0005

0015

0005

ri-si

ri+si010 034

B1

B3B2

042

-0010

0020

02 025 03 035 04504

006

015

-024

012

AB

-018

-012

-006

CD

E

ri+si

ri-si

-0030

000 01-0015

02

0045

0030

015 025

0015

D1

03

D3

D4

D2

ri+si

ri-si

0060

-0045

0060025

-005040035030

-015

015

010 A1A2

A4

A3

ri-si

ri+si005

-010

010

005

-005

ri+si

ri-si

015005 025020E1E3

E2

010

Figure 2 e INRM of each dimension and criterion

Mathematical Problems in Engineering 13

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

Tabl

e7

Initial

direct-effect

matrixG

A1

A2

A3

A4

B1B2

B3C1

C2C3

D1

D2

D3

D4

E1E2

E3To

tal

A1

0000

0286

3429

1857

3286

1857

2429

0000

2143

1286

0000

0143

0000

0000

2429

0429

1571

21143

A2

0714

0000

1000

3429

1143

0286

0000

0000

2714

1143

0000

0429

0000

0000

2286

0429

2429

16000

A3

1286

1286

0000

0571

1286

2857

0429

0000

1714

1429

0000

0143

0000

0000

1714

3286

0143

16143

A4

1143

0429

0286

0000

0429

1143

0286

0000

1429

1000

0000

0286

0000

0000

2571

0571

2429

12000

B11286

0000

2857

2571

0000

3714

0857

0143

0000

0000

0000

0000

0000

0000

3571

3714

2857

21571

B20714

0143

1571

0429

3571

0000

0714

0000

0143

0000

0000

0000

0000

0000

3429

3714

2714

17143

B30429

0000

0000

0286

0429

0714

0000

0000

0000

0000

0000

0000

0000

0000

3429

2857

1857

10000

C11286

1286

1286

1857

2571

1857

0000

0000

2857

2714

0286

0000

0143

1143

1714

3429

0286

22714

C21143

1571

0714

1429

0000

0143

0000

0000

0000

2857

0143

0571

0000

0286

3571

0143

2286

14857

C31000

0857

0571

0571

0000

0000

0000

0000

2571

0000

0143

0429

0000

0286

3571

0143

1857

12000

D1

0000

0000

0000

0000

0000

0000

0000

0571

0857

0143

0000

0143

2286

1571

0429

0000

0143

6143

D2

0000

0000

1429

1286

0000

0286

0000

0714

2714

3429

0571

0000

0571

0714

2429

0143

1571

15857

D3

0000

0000

0000

0000

0000

0143

0000

0857

0000

0000

2429

0286

0000

1286

0857

0000

0714

6571

D4

0429

0000

0143

0286

0714

0143

0000

0286

0000

0000

2429

0143

0571

0000

0571

0000

0286

6000

E11857

0714

1429

1286

1286

0714

0000

0143

0429

0429

1571

0143

0143

0000

0000

0857

0143

11143

E20000

0000

0000

0000

0571

0000

0143

0286

0000

0000

0000

0000

0000

0000

2714

0000

1143

4857

E30000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0571

0143

0000

0714

Total

11286

6571

14714

15857

15286

13857

4857

3000

17571

14429

7571

2714

3714

5286

35857

19857

22429

Note1

(n

(n

minus1)

)1113936

n i1

1113936n j

1(|t

p ijminus

tpminus1

ij|t

p ij)

times100

487lt5

iesig

nificantc

onfid

ence

is9513

where

p7deno

testhenu

mberof

experts

tp ijistheaverageinflu

ence

oficriterion

onjandndeno

tes

numberof

criteriahere

n17

andn

timesnmatrix

Mathematical Problems in Engineering 11

e order of priority for achieving the desired level can bedetermined by the weights of the performance values fromhigh to low and the gap values from high to low

As indicated in Table 15 Aion S (P3) produced by GACNE company presents the smallest gap (0276) and thereforeranks first followed by the BAIC EU Series (P2 0296) MGEZS (P4 0297) BESTUNE B30EV (P8 0304) GEOMETRY

A (P6 0305) EMGRAND EV (P9 0340) Aeolus E70 (P50341) BYD Yuan (P7 0344) ORA R1 (P10 0471) andBAOJUN E100 (P1 0694) in this regard Integration of thescores of Aion S (P3) further demonstrates that the gap forthe dynamics (A) dimension is 0191 and that for the sus-pension (D2) criterion is 0367 constituting the largest gapswhich Aion S should improve as a priority e integration

Table 8 e normalized direct-influence matrix X

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3A1 0000 0008 0096 0052 0092 0052 0068 0000 0060 0036 0000 0004 0000 0000 0068 0012 0044A2 0020 0000 0028 0096 0032 0008 0000 0000 0076 0032 0000 0012 0000 0000 0064 0012 0068A3 0036 0036 0000 0016 0036 0080 0012 0000 0048 0040 0000 0004 0000 0000 0048 0092 0004A4 0032 0012 0008 0000 0012 0032 0008 0000 0040 0028 0000 0008 0000 0000 0072 0016 0068B1 0036 0000 0080 0072 0000 0104 0024 0004 0000 0000 0000 0000 0000 0000 0100 0104 0080B2 0020 0004 0044 0012 0100 0000 0020 0000 0004 0000 0000 0000 0000 0000 0096 0104 0076B3 0012 0000 0000 0008 0012 0020 0000 0000 0000 0000 0000 0000 0000 0000 0096 0080 0052C1 0036 0036 0036 0052 0072 0052 0000 0000 0080 0076 0008 0000 0004 0032 0048 0096 0008C2 0032 0044 0020 0040 0000 0004 0000 0000 0000 0080 0004 0016 0000 0008 0100 0004 0064C3 0028 0024 0016 0016 0000 0000 0000 0000 0072 0000 0004 0012 0000 0008 0100 0004 0052D1 0000 0000 0000 0000 0000 0000 0000 0016 0024 0004 0000 0004 0064 0044 0012 0000 0004D2 0000 0000 0040 0036 0000 0008 0000 0020 0076 0096 0016 0000 0016 0020 0068 0004 0044D3 0000 0000 0000 0000 0000 0004 0000 0024 0000 0000 0068 0008 0000 0036 0024 0000 0020D4 0012 0000 0004 0008 0020 0004 0000 0008 0000 0000 0068 0004 0016 0000 0016 0000 0008E1 0052 0020 0040 0036 0036 0020 0000 0004 0012 0012 0044 0004 0004 0000 0000 0024 0004E2 0000 0000 0000 0000 0016 0000 0004 0008 0000 0000 0000 0000 0000 0000 0076 0000 0032E3 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0016 0004 0000

Table 9 e total influence matrix of criteria

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3 riA1 0024 0021 0120 0076 0114 0082 0076 0002 0078 0053 0006 0008 0001 0002 0128 0055 0082 0241A2 0037 0011 0045 0112 0045 0025 0006 0001 0092 0049 0006 0016 0001 0002 0103 0030 0094 0204A3 0051 0044 0021 0036 0058 0094 0020 0002 0063 0052 0005 0007 0001 0001 0096 0116 0036 0152A4 0043 0019 0023 0013 0026 0042 0013 0001 0050 0038 0005 0010 0001 0001 0099 0030 0085 0098B1 0055 0010 0101 0088 0031 0125 0033 0006 0016 0013 0007 0002 0001 0001 0149 0138 0110 0189B2 0036 0011 0064 0030 0116 0023 0027 0002 0014 0008 0006 0002 0001 0001 0134 0131 0099 0166B3 0020 0003 0010 0016 0023 0027 0003 0001 0005 0004 0005 0001 0001 0000 0113 0090 0062 0053C1 0059 0049 0063 0079 0096 0076 0010 0003 0102 0094 0017 0005 0006 0035 0113 0127 0050 0199C2 0047 0052 0036 0057 0015 0016 0005 0001 0021 0091 0011 0019 0002 0010 0131 0017 0084 0113C3 0041 0032 0030 0031 0012 0010 0004 0001 0083 0014 0011 0015 0002 0010 0124 0015 0068 0099D1 0004 0003 0004 0004 0004 0003 0001 0018 0028 0009 0009 0005 0065 0048 0021 0004 0010 0127D2 0017 0012 0053 0051 0013 0020 0003 0022 0094 0111 0024 0004 0018 0024 0105 0019 0066 0071D3 0004 0002 0004 0004 0005 0007 0001 0026 0006 0004 0073 0009 0006 0040 0031 0005 0024 0127D4 0016 0002 0010 0013 0025 0010 0002 0010 0006 0004 0071 0005 0021 0004 0026 0007 0014 0101E1 0062 0026 0055 0050 0051 0036 0007 0006 0026 0022 0046 0006 0007 0003 0031 0042 0024 0097E2 0006 0003 0006 0006 0021 0005 0005 0009 0003 0003 0004 0001 0001 0001 0082 0007 0036 0125E3 0001 0000 0001 0001 0001 0001 0000 0000 0000 0000 0001 0000 0000 0000 0017 0005 0001 0022si 0156 0094 0208 0236 0169 0175 0064 0005 0207 0200 0177 0024 0110 0116 0130 0054 0061

Table 10 e total influence matrix of dimension

Dimension A B C D E riA 0043 0050 0040 0005 0079 0217B 0037 0045 0008 0002 0114 0206C 0048 0027 0046 0012 0081 0214D 0013 0008 0028 0027 0028 0103E 0018 0014 0008 0006 0027 0073si 0159 0144 0129 0051 0329

12 Mathematical Problems in Engineering

of the scores of the BAIC EU Series (P2) in the DANP showsthat the gap of the safety (C) dimension is 0267 and that ofthe alliance with operating stability (C3) criterion is 0383constituting the largest gaps which the BAIC EU Seriesshould improve as a priority e integration of the scores ofBAOJUN E100 (P1) in the DANP shows that the gap of thecomfort (D) dimension is 0467 and that of the charge time

(B3) criterion is 1000 constituting the largest gaps whichBAOJUN E100 should improve as a priority e integrationof the scores of MG EZS (P4) in the DANP shows that thegap of the vehicle comfort (D) dimension is 0276 and that ofthe suspension (D2) criterion is 0367 constituting thelargest gaps which MG EZS should improve as a prioritye integration of the scores of Aeolus E70 (P5) in the

Table 11 Sum of influences givenreceived on dimensionscriteria

Criteria ri si ri+ si ri minus siDynamics (A) 0217 0159 0376 0058Maximum power (A1) 0241 0156 0397 0085Max torque (A2) 0204 0094 0298 0110Max speed (A3) 0152 0208 0361 minus0056Acceleration time (A4) 0098 0236 0334 minus0139Technology (B) 0206 0144 0351 0062Driving range (B1) 0189 0169 0358 0019Electricity consumption (B2) 0166 0175 0341 minus0009Charge time (B3) 0053 0064 0116 minus0011Safety (C) 0214 0129 0343 0084Curb weight (C1) 0199 0005 0205 0194Braking properties (C2) 0113 0207 0320 minus0093Operating stability (C3) 0099 0200 0299 minus0101Comfort (D) 0103 0051 0154 0052Car space (D1) 0127 0177 0304 minus0050Suspension (D2) 0071 0024 0095 0048Car seat (D3) 0127 0110 0238 0017Exterior and interior (D4) 0101 0116 0217 minus0016Cost (E) 0073 0329 0402 minus0256Price (E1) 0097 0130 0227 minus0033Incentives (E2) 0125 0054 0179 0072After-sales cost (E3) 0022 0061 0083 minus0039

ri-si

ri+si

005

010

020

-005

-010

030025020 035

C2

C3

C1

015

026018

0010

-0005

0015

0005

ri-si

ri+si010 034

B1

B3B2

042

-0010

0020

02 025 03 035 04504

006

015

-024

012

AB

-018

-012

-006

CD

E

ri+si

ri-si

-0030

000 01-0015

02

0045

0030

015 025

0015

D1

03

D3

D4

D2

ri+si

ri-si

0060

-0045

0060025

-005040035030

-015

015

010 A1A2

A4

A3

ri-si

ri+si005

-010

010

005

-005

ri+si

ri-si

015005 025020E1E3

E2

010

Figure 2 e INRM of each dimension and criterion

Mathematical Problems in Engineering 13

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

e order of priority for achieving the desired level can bedetermined by the weights of the performance values fromhigh to low and the gap values from high to low

As indicated in Table 15 Aion S (P3) produced by GACNE company presents the smallest gap (0276) and thereforeranks first followed by the BAIC EU Series (P2 0296) MGEZS (P4 0297) BESTUNE B30EV (P8 0304) GEOMETRY

A (P6 0305) EMGRAND EV (P9 0340) Aeolus E70 (P50341) BYD Yuan (P7 0344) ORA R1 (P10 0471) andBAOJUN E100 (P1 0694) in this regard Integration of thescores of Aion S (P3) further demonstrates that the gap forthe dynamics (A) dimension is 0191 and that for the sus-pension (D2) criterion is 0367 constituting the largest gapswhich Aion S should improve as a priority e integration

Table 8 e normalized direct-influence matrix X

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3A1 0000 0008 0096 0052 0092 0052 0068 0000 0060 0036 0000 0004 0000 0000 0068 0012 0044A2 0020 0000 0028 0096 0032 0008 0000 0000 0076 0032 0000 0012 0000 0000 0064 0012 0068A3 0036 0036 0000 0016 0036 0080 0012 0000 0048 0040 0000 0004 0000 0000 0048 0092 0004A4 0032 0012 0008 0000 0012 0032 0008 0000 0040 0028 0000 0008 0000 0000 0072 0016 0068B1 0036 0000 0080 0072 0000 0104 0024 0004 0000 0000 0000 0000 0000 0000 0100 0104 0080B2 0020 0004 0044 0012 0100 0000 0020 0000 0004 0000 0000 0000 0000 0000 0096 0104 0076B3 0012 0000 0000 0008 0012 0020 0000 0000 0000 0000 0000 0000 0000 0000 0096 0080 0052C1 0036 0036 0036 0052 0072 0052 0000 0000 0080 0076 0008 0000 0004 0032 0048 0096 0008C2 0032 0044 0020 0040 0000 0004 0000 0000 0000 0080 0004 0016 0000 0008 0100 0004 0064C3 0028 0024 0016 0016 0000 0000 0000 0000 0072 0000 0004 0012 0000 0008 0100 0004 0052D1 0000 0000 0000 0000 0000 0000 0000 0016 0024 0004 0000 0004 0064 0044 0012 0000 0004D2 0000 0000 0040 0036 0000 0008 0000 0020 0076 0096 0016 0000 0016 0020 0068 0004 0044D3 0000 0000 0000 0000 0000 0004 0000 0024 0000 0000 0068 0008 0000 0036 0024 0000 0020D4 0012 0000 0004 0008 0020 0004 0000 0008 0000 0000 0068 0004 0016 0000 0016 0000 0008E1 0052 0020 0040 0036 0036 0020 0000 0004 0012 0012 0044 0004 0004 0000 0000 0024 0004E2 0000 0000 0000 0000 0016 0000 0004 0008 0000 0000 0000 0000 0000 0000 0076 0000 0032E3 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0000 0016 0004 0000

Table 9 e total influence matrix of criteria

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3 riA1 0024 0021 0120 0076 0114 0082 0076 0002 0078 0053 0006 0008 0001 0002 0128 0055 0082 0241A2 0037 0011 0045 0112 0045 0025 0006 0001 0092 0049 0006 0016 0001 0002 0103 0030 0094 0204A3 0051 0044 0021 0036 0058 0094 0020 0002 0063 0052 0005 0007 0001 0001 0096 0116 0036 0152A4 0043 0019 0023 0013 0026 0042 0013 0001 0050 0038 0005 0010 0001 0001 0099 0030 0085 0098B1 0055 0010 0101 0088 0031 0125 0033 0006 0016 0013 0007 0002 0001 0001 0149 0138 0110 0189B2 0036 0011 0064 0030 0116 0023 0027 0002 0014 0008 0006 0002 0001 0001 0134 0131 0099 0166B3 0020 0003 0010 0016 0023 0027 0003 0001 0005 0004 0005 0001 0001 0000 0113 0090 0062 0053C1 0059 0049 0063 0079 0096 0076 0010 0003 0102 0094 0017 0005 0006 0035 0113 0127 0050 0199C2 0047 0052 0036 0057 0015 0016 0005 0001 0021 0091 0011 0019 0002 0010 0131 0017 0084 0113C3 0041 0032 0030 0031 0012 0010 0004 0001 0083 0014 0011 0015 0002 0010 0124 0015 0068 0099D1 0004 0003 0004 0004 0004 0003 0001 0018 0028 0009 0009 0005 0065 0048 0021 0004 0010 0127D2 0017 0012 0053 0051 0013 0020 0003 0022 0094 0111 0024 0004 0018 0024 0105 0019 0066 0071D3 0004 0002 0004 0004 0005 0007 0001 0026 0006 0004 0073 0009 0006 0040 0031 0005 0024 0127D4 0016 0002 0010 0013 0025 0010 0002 0010 0006 0004 0071 0005 0021 0004 0026 0007 0014 0101E1 0062 0026 0055 0050 0051 0036 0007 0006 0026 0022 0046 0006 0007 0003 0031 0042 0024 0097E2 0006 0003 0006 0006 0021 0005 0005 0009 0003 0003 0004 0001 0001 0001 0082 0007 0036 0125E3 0001 0000 0001 0001 0001 0001 0000 0000 0000 0000 0001 0000 0000 0000 0017 0005 0001 0022si 0156 0094 0208 0236 0169 0175 0064 0005 0207 0200 0177 0024 0110 0116 0130 0054 0061

Table 10 e total influence matrix of dimension

Dimension A B C D E riA 0043 0050 0040 0005 0079 0217B 0037 0045 0008 0002 0114 0206C 0048 0027 0046 0012 0081 0214D 0013 0008 0028 0027 0028 0103E 0018 0014 0008 0006 0027 0073si 0159 0144 0129 0051 0329

12 Mathematical Problems in Engineering

of the scores of the BAIC EU Series (P2) in the DANP showsthat the gap of the safety (C) dimension is 0267 and that ofthe alliance with operating stability (C3) criterion is 0383constituting the largest gaps which the BAIC EU Seriesshould improve as a priority e integration of the scores ofBAOJUN E100 (P1) in the DANP shows that the gap of thecomfort (D) dimension is 0467 and that of the charge time

(B3) criterion is 1000 constituting the largest gaps whichBAOJUN E100 should improve as a priority e integrationof the scores of MG EZS (P4) in the DANP shows that thegap of the vehicle comfort (D) dimension is 0276 and that ofthe suspension (D2) criterion is 0367 constituting thelargest gaps which MG EZS should improve as a prioritye integration of the scores of Aeolus E70 (P5) in the

Table 11 Sum of influences givenreceived on dimensionscriteria

Criteria ri si ri+ si ri minus siDynamics (A) 0217 0159 0376 0058Maximum power (A1) 0241 0156 0397 0085Max torque (A2) 0204 0094 0298 0110Max speed (A3) 0152 0208 0361 minus0056Acceleration time (A4) 0098 0236 0334 minus0139Technology (B) 0206 0144 0351 0062Driving range (B1) 0189 0169 0358 0019Electricity consumption (B2) 0166 0175 0341 minus0009Charge time (B3) 0053 0064 0116 minus0011Safety (C) 0214 0129 0343 0084Curb weight (C1) 0199 0005 0205 0194Braking properties (C2) 0113 0207 0320 minus0093Operating stability (C3) 0099 0200 0299 minus0101Comfort (D) 0103 0051 0154 0052Car space (D1) 0127 0177 0304 minus0050Suspension (D2) 0071 0024 0095 0048Car seat (D3) 0127 0110 0238 0017Exterior and interior (D4) 0101 0116 0217 minus0016Cost (E) 0073 0329 0402 minus0256Price (E1) 0097 0130 0227 minus0033Incentives (E2) 0125 0054 0179 0072After-sales cost (E3) 0022 0061 0083 minus0039

ri-si

ri+si

005

010

020

-005

-010

030025020 035

C2

C3

C1

015

026018

0010

-0005

0015

0005

ri-si

ri+si010 034

B1

B3B2

042

-0010

0020

02 025 03 035 04504

006

015

-024

012

AB

-018

-012

-006

CD

E

ri+si

ri-si

-0030

000 01-0015

02

0045

0030

015 025

0015

D1

03

D3

D4

D2

ri+si

ri-si

0060

-0045

0060025

-005040035030

-015

015

010 A1A2

A4

A3

ri-si

ri+si005

-010

010

005

-005

ri+si

ri-si

015005 025020E1E3

E2

010

Figure 2 e INRM of each dimension and criterion

Mathematical Problems in Engineering 13

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

of the scores of the BAIC EU Series (P2) in the DANP showsthat the gap of the safety (C) dimension is 0267 and that ofthe alliance with operating stability (C3) criterion is 0383constituting the largest gaps which the BAIC EU Seriesshould improve as a priority e integration of the scores ofBAOJUN E100 (P1) in the DANP shows that the gap of thecomfort (D) dimension is 0467 and that of the charge time

(B3) criterion is 1000 constituting the largest gaps whichBAOJUN E100 should improve as a priority e integrationof the scores of MG EZS (P4) in the DANP shows that thegap of the vehicle comfort (D) dimension is 0276 and that ofthe suspension (D2) criterion is 0367 constituting thelargest gaps which MG EZS should improve as a prioritye integration of the scores of Aeolus E70 (P5) in the

Table 11 Sum of influences givenreceived on dimensionscriteria

Criteria ri si ri+ si ri minus siDynamics (A) 0217 0159 0376 0058Maximum power (A1) 0241 0156 0397 0085Max torque (A2) 0204 0094 0298 0110Max speed (A3) 0152 0208 0361 minus0056Acceleration time (A4) 0098 0236 0334 minus0139Technology (B) 0206 0144 0351 0062Driving range (B1) 0189 0169 0358 0019Electricity consumption (B2) 0166 0175 0341 minus0009Charge time (B3) 0053 0064 0116 minus0011Safety (C) 0214 0129 0343 0084Curb weight (C1) 0199 0005 0205 0194Braking properties (C2) 0113 0207 0320 minus0093Operating stability (C3) 0099 0200 0299 minus0101Comfort (D) 0103 0051 0154 0052Car space (D1) 0127 0177 0304 minus0050Suspension (D2) 0071 0024 0095 0048Car seat (D3) 0127 0110 0238 0017Exterior and interior (D4) 0101 0116 0217 minus0016Cost (E) 0073 0329 0402 minus0256Price (E1) 0097 0130 0227 minus0033Incentives (E2) 0125 0054 0179 0072After-sales cost (E3) 0022 0061 0083 minus0039

ri-si

ri+si

005

010

020

-005

-010

030025020 035

C2

C3

C1

015

026018

0010

-0005

0015

0005

ri-si

ri+si010 034

B1

B3B2

042

-0010

0020

02 025 03 035 04504

006

015

-024

012

AB

-018

-012

-006

CD

E

ri+si

ri-si

-0030

000 01-0015

02

0045

0030

015 025

0015

D1

03

D3

D4

D2

ri+si

ri-si

0060

-0045

0060025

-005040035030

-015

015

010 A1A2

A4

A3

ri-si

ri+si005

-010

010

005

-005

ri+si

ri-si

015005 025020E1E3

E2

010

Figure 2 e INRM of each dimension and criterion

Mathematical Problems in Engineering 13

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

Table 12 e unweighted super-matrix Wc

A1 A2 A3 A4 B1 B2 B3 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0101 0182 0337 0442 0218 0256 0412 0245 0305 0272 0129 0274 0392 0322 0294 0321A2 0087 0052 0288 0190 0039 0075 0069 0271 0240 0186 0093 0149 0046 0133 0120 0133A3 0497 0219 0141 0231 0397 0454 0200 0188 0223 0242 0398 0280 0242 0287 0304 0287A4 0314 0547 0234 0137 0346 0215 0320 0296 0233 0299 0380 0297 0320 0258 0281 0259

BB1 0421 0601 0338 0322 0163 0696 0431 0415 0465 0530 0355 0393 0675 0541 0666 0551B2 0300 0325 0548 0519 0660 0141 0511 0450 0385 0402 0562 0558 0268 0381 0171 0365B3 0279 0075 0114 0160 0177 0163 0058 0135 0149 0068 0084 0049 0058 0078 0163 0085

CC1 0013 0009 0015 0012 0172 0091 0144 0012 0013 0333 0095 0713 0520 0105 0597 0135C2 0588 0647 0539 0566 0469 0575 0480 0183 0843 0505 0415 0164 0289 0485 0217 0469C3 0399 0344 0446 0422 0360 0334 0376 0805 0144 0162 0490 0122 0190 0410 0186 0396

D

D1 0380 0230 0346 0289 0609 0656 0712 0267 0293 0070 0343 0570 0702 0738 0691 0737D2 0463 0654 0500 0591 0220 0177 0124 0459 0405 0043 0062 0069 0049 0099 0102 0099D3 0064 0044 0059 0052 0098 0104 0112 0041 0043 0513 0258 0044 0206 0115 0113 0115D4 0093 0073 0095 0069 0072 0062 0053 0233 0260 0374 0337 0317 0043 0048 0094 0049

EE1 0483 0455 0387 0462 0375 0369 0427 0564 0601 0613 0552 0519 0551 0320 0657 0763E2 0209 0131 0468 0141 0349 0360 0339 0074 0070 0107 0102 0087 0143 0432 0055 0213E3 0309 0414 0144 0396 0276 0271 0234 0362 0329 0280 0346 0393 0306 0248 0288 0024

Table 13 e weighted super-matrix W lowast c

A1 A2 A3 A4 B1 B2 B3 C1 C2 C3 D1 D2 D3 D4 E1 E2 E3

A

A1 0020 0036 0067 0088 0039 0046 0074 0053 0055 0068 0034 0016 0034 0048 0080 0073 0079A2 0017 0010 0057 0038 0007 0013 0012 0044 0061 0054 0023 0011 0018 0006 0033 0030 0033A3 0099 0044 0028 0046 0071 0081 0036 0057 0042 0050 0030 0049 0035 0030 0071 0075 0071A4 0063 0109 0047 0027 0062 0039 0057 0071 0067 0052 0037 0047 0037 0039 0064 0070 0064

BB1 0097 0138 0078 0074 0036 0153 0095 0067 0053 0059 0040 0027 0030 0051 0105 0130 0107B2 0069 0075 0126 0119 0145 0031 0112 0053 0057 0049 0030 0042 0042 0020 0074 0033 0071B3 0064 0017 0026 0037 0039 0036 0013 0007 0017 0019 0005 0006 0004 0004 0015 0032 0016

CC1 0002 0002 0003 0002 0006 0003 0005 0003 0003 0003 0091 0026 0195 0142 0011 0063 0014C2 0108 0119 0099 0104 0017 0021 0018 0110 0039 0181 0138 0113 0045 0079 0051 0023 0050C3 0073 0063 0082 0078 0013 0012 0014 0101 0172 0031 0044 0134 0033 0052 0043 0020 0042

D

D1 0008 0005 0007 0006 0007 0007 0008 0015 0015 0016 0018 0089 0148 0182 0058 0055 0058D2 0010 0014 0010 0012 0002 0002 0001 0005 0025 0022 0011 0016 0018 0013 0008 0008 0008D3 0001 0001 0001 0001 0001 0001 0001 0005 0002 0002 0133 0067 0011 0053 0009 0009 0009D4 0002 0002 0002 0001 0001 0001 0001 0031 0013 0014 0097 0087 0082 0011 0004 0007 0004

EE1 0176 0166 0142 0169 0207 0204 0236 0148 0214 0228 0165 0149 0140 0148 0119 0245 0285E2 0076 0048 0171 0052 0193 0199 0187 0166 0028 0027 0029 0027 0024 0039 0161 0021 0079E3 0113 0151 0053 0145 0153 0150 0129 0065 0137 0124 0075 0093 0106 0082 0093 0107 0009

Table 14 Influential weights of criteria based on DANP

DimensionsCriteria Local Weights Global WeightsDynamics (A) 0214 mdashMaximum power (A1) 0289 0062Max torque (A2) 0143 0031Max speed (A3) 0293 0063Acceleration time (A4) 0274 0059Technology (B) 0190 mdashDriving range (B1) 0479 0091Electricity consumption (B2) 0388 0074Charge time (B3) 0133 0025Safety (C) 0134 mdashCurb weight (C1) 0139 0019Braking properties (C2) 0478 0064Operating stability (C3) 0383 0051Comfort (D) 0062 mdashCar space (D1) 0527 0032Suspension (D2) 0156 0010Car seat (D3) 0163 0010Exterior and interior (D4) 0154 0009Cost (E) 0400 mdashPrice (E1) 0477 0191Incentives (E2) 0265 0106After-sales cost (E3) 0258 0103

14 Mathematical Problems in Engineering

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

Tabl

e15

eperformance

evaluatio

nof

theBE

VsusingVIK

OR

Dim

ensio

ns

criteria

Local

weigh

tGlobal

weigh

tP1

P2P3

P4P5

P6P7

P8P9

P10

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

Scores

Gap

A0214

5618

0438

8446

0155

8088

0191

8301

0170

7669

0233

7860

0214

7448

0255

6892

0311

7545

0245

5648

0435

A1

0289

0062

5833

0417

8167

0183

7833

0217

7500

0250

7167

0283

7833

0217

6000

0400

6667

0333

7333

0267

6000

0400

A2

0143

0031

6500

0350

8500

0150

8333

0167

8833

0117

7667

0233

7333

0267

6500

0350

6833

0317

7833

0217

6667

0333

A3

0293

0063

6333

0367

8333

0167

7833

0217

8333

0167

8167

0183

8167

0183

8667

0133

7667

0233

7500

0250

6500

0350

A4

0274

0059

4167

0583

8833

0117

8500

0150

8833

0117

7667

0233

7833

0217

8167

0183

6333

0367

7667

0233

3833

0617

B0190

6285

0371

7742

0226

8159

0184

7442

0256

7425

0258

8124

0188

7306

0269

7856

0214

7986

0201

7635

0237

B10479

0091

6500

0350

8500

0150

8833

0117

7333

0267

7833

0217

8667

0133

7500

0250

8333

0167

8333

0167

7333

0267

B20388

0074

8167

0183

6833

0317

7667

0233

7500

0250

6667

0333

7667

0233

7000

0300

7333

0267

7667

0233

8167

0183

B30133

0025

0000

1000

7667

0233

7167

0283

7667

0233

8167

0183

7500

0250

7500

0250

7667

0233

7667

0233

7167

0283

C0134

6037

0396

7329

0267

8125

0188

7938

0206

7709

0229

7584

0242

7115

0289

8486

0151

6881

0312

6259

0374

C10139

0019

6500

0350

6500

0350

6833

0317

7667

0233

7167

0283

6500

0350

6333

0367

8167

0183

6833

0317

6833

0317

C20478

0064

6333

0367

8500

0150

9000

0100

8500

0150

8167

0183

7833

0217

7167

0283

8833

0117

7333

0267

6167

0383

C30383

0051

5500

0450

6167

0383

7500

0250

7333

0267

7333

0267

7667

0233

7333

0267

8167

0183

6333

0367

6167

0383

D0062

5328

0467

7499

0250

8595

0141

7244

0276

6886

0311

7361

0264

6795

0321

8161

0184

6202

0380

6791

0321

D1

0527

0032

5833

0417

7500

0250

9500

0050

7667

0233

7333

0267

7500

0250

6500

0350

8500

0150

6333

0367

7333

0267

D2

0156

0010

4333

0567

6667

0333

6333

0367

6333

0367

5833

0417

7667

0233

7167

0283

7500

0250

5667

0433

5167

0483

D3

0163

0010

4333

0567

7667

0233

9500

0050

6667

0333

6500

0350

6333

0367

7667

0233

8167

0183

6167

0383

7500

0250

D4

0154

0009

5667

0433

8167

0183

6833

0317

7333

0267

6833

0317

7667

0233

6500

0350

7667

0233

6333

0367

5833

0417

E0400

6466

0353

7947

0205

8115

0188

7554

0245

7105

0289

7154

0285

6916

0308

7410

0259

7714

0229

7052

0295

E10477

0191

6167

0383

8333

0167

8500

0150

8167

0183

6667

0333

6667

0333

7000

0300

6667

0333

7667

0233

7833

0217

E20265

0106

5833

0417

8500

0150

8833

0117

6667

0333

7833

0217

8667

0133

7333

0267

8333

0167

8333

0167

6833

0317

E30258

0103

7667

0233

6667

0333

6667

0333

7333

0267

7167

0283

6500

0350

6333

0367

7833

0217

7167

0283

5833

0417

Total

1000

1000

6123

0694

7904

0296

8148

0276

7725

0297

7354

0341

7560

0305

7123

0344

7575

0304

7525

0340

6740

0471

102

13

75

84

69

Mathematical Problems in Engineering 15

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

DANP showed that the gap of the comfort (D) dimension is0311 and that of the alliance with suspension (D2) criterionis 0417 constituting the largest gaps which Aeolus E70should improve as a priority e integration of the scores ofGEOMETRY A (P6) in the DANP shows that the gap of thecost (E) dimension is 0285 and that of the car seat (D3)criterion is 0367 constituting the largest gaps which GE-OMETRY A should improve as a priority Integration of thescores of BYD Yuan (P7) in the DANP showed that the gapof the comfort (D) dimension is 0276 and that of themaximum power (A1) criterion is 0400 constituting thelargest gaps which BYD Yuan should improve as a prioritye integration of the scores of BESTUNE B30EV (P8) in theDANP shows that the gap of the dynamics (A) dimension is0311 and that of the alliance with acceleration time (A4)criterion is 0367 constituting the largest gaps whichBESTUNE B30EV should improve as a priority e inte-gration of the scores of EMGRAND EV (P9) in the DANPshows that the gap of the vehicle comfort (D) dimension is0380 and that of the suspension (D2) criterion is 0433constituting the largest gaps which EMGRAND EV shouldimprove as a priority e integration of the scores of ORAR1 (P10) in the DANP shows that the gap of the dynamics(A) dimension is 0435 and that of the acceleration time (A4)criterion is 0617 constituting the largest gaps which ORAR1 should improve as a priority Furthermore the gap valuesobtained by the customers reveal that improvement priorityschemes are unique and comprehensive for each separatedimension as well as for the overall range of criteria De-cision-makers in government departments and automobileenterprises can easily understand the gaps where im-provements are prioritised

5 Discussion

e study uses fine-grained sentiment analysis and MCDMmodel to explore the BEVs from the customersrsquo point of viewbased on five dimensions namely safety technology dy-namics comfort and cost e LDA model based on fine-grained sentiment analysis has been applied to obtain di-mensions and criteria based on the word-of-mouth dataeDANP method combining the DEMATEL technique withANP has been used to create an INRM to identify the causalrelationship and compute the influence weights of all

indicators e Modified VIKOR is not only used to rankand determine selection strategies but also to propose theoptimized path for ten BEVs

e empirical results are discussed as follows First usingthe LDA model based on fine-grained sentiment analysisthe comprehensive evaluation indicator system is con-structed including five dimensions and seventeen criteriaAccording to the DEMATEL cause-and-effect model theinterrelationships between each dimension and criterion aredetermined by INRM In Figure 2 the degree of the di-mension effect indicates that improvement priorities shouldbe established in the following order safety technologydynamics comfort and cost e results further illustratethat the decision-makers should improve safety first becauseit has the greatest immediate network effect on the otherdimensions e structural design materials used in colli-sion sites safety equipment and other aspects may causeaccidents [108 109] such as battery explosions and physicaldamages [57] So most people are concerned about safetywhen driving BEVs [58] e findings also imply that a fullunderstanding of the relationships among dimensions canenable automobile companies to grasp the main direction ofthe future revolution thus enhancing consumersrsquo recogni-tion and enthusiasm for BEVs

Second after describing the dimensions the study alsodiscusses the criterion considered in each dimension eyprovide a higher-level mode for the improvement of BEVattributes [110] In terms of the results for the safety (C)dimension consumers are more concerned with the curbweight of BEVs Combining lightweight designs with BEVscan further reduce environmental impacts [52] In additionthe application of lightweight materials in the BEVs canimprove functionality and safety [111] Moreover details onthe causal relationship between technology dynamicscomfort and cost can also be derived from Figure 2 Each ofthe evaluation dimensions and criteria play an essential rolein customersrsquo choices of BEVs erefore relevant depart-ments should evaluate all the dimensions and criteria forpromoting customersrsquo attitudes and willingness e sub-sequent evaluation model can be used for the automobileindustry in China

ird the study uses the DANP to confirm the weight ofthe 17 influential criteria As shown in Table 14 price (E1) isthe largest relative weight of seventeen criteria with the value

Table 16 e results of the two MCDM methods

AlternativesDANP-VIKOR CRITIC-VIKOR

Uk Rank Uk RankP1 0694 10 0726 10P2 0296 2 0295 2P3 0276 1 0284 1P4 0297 3 0300 4P5 0341 7 0340 6P6 0305 5 0305 5P7 0344 8 0345 7P8 0305 4 0299 3P9 0340 6 0353 8P10 0471 9 0478 9

16 Mathematical Problems in Engineering

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

Table 17 Optimal path

Formula Path (sequence of improvement priorities)

Total optimization path

F1 influential network of dimensions of DEMATEL C B A D E

F2 influential network of criteria within individual dimensions

C C1 C2 C3B B1 B2 B3

A A2 A1 A3 A4D D2 D3 D4 D1

E E2 E1 E3

Specific optimization path

F3 BAOJUN E100 (by gap value from high to low)

D A C B ED D2D3 D4 D1A A4 A1 A3 A2C C3 C2 C1B B3 B1 B2E E2 E1 E3

F4 BAIC EU Series (by gap value from high to low)

C D B E ACC3 C1 C2

D D2 D1 D3 D4B B2 B3 B1E E3 E1 E2

A A1 A3 A2 A4

F5 Aion S (by gap value from high to low)

A CE B DA A1 A3 A2 A4C C1 C3 C2E E3 E1 E2B B3 B2 B1

D D2 D4 D1 D3

F6 MG EZS (by gap value from high to low)

D B E C AD D2 D3 D4 D1

B B1 B2 B3E E2 E3 E1C C3 C1 C2

A A1 A3 A2A4

F7 Aeolus E70 (by gap value from high to low)

D E B A CD D2 D3 D4 D1

E E1 E3 E2B B2 B1 B3

A A1 A2A4 A3C C1 C3 C2

F8 GEOMETRY A (by gap value from high to low)

E D C A BE E3 E1 E2

D D3 D1 D2D4C C1 C3 C2

A A2 A1A4 A3B B3 B2 B1

F9 BYD Yuan (by gap value from high to low)

D E C B AD D1D4 D2 D3

E E3 E1 E2C C1 C2 C3B B2 B1B3

A A1 A2 A4 A3

F10 BESTUN EB30EV (by gap value from high to low)

A E B D CA A4 A1 A2 A3

E E1 E3 E2B B2 B3 B1

D D2 D4 D3 D1CC1C3 C2

F11 EMGRAND EV (by gap value from high to low)

D C A E BD D2 D3 D4D1C C3 C1 C2

A A1 A3 A4 A2E E3 E1 E2B B2B3 B1

F12 ORA R1 (by gap value from high to low)

A C D E BA A4 A1 A3 A2

C C3C2 C1D D2 D4 D1 D3

E E3 E2 E1B B3 B1 B2

Mathematical Problems in Engineering 17

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

of 0191 which indicates that the price of BEVs is the mostimportant criterion that affects the customersrsquo decisionGenerally the cost of BEVs is higher than that of InternalCombustion Engine Vehicles (ICEVs) which is mainly due tothe high research and development (RampD) expense andproduction costs especially batteries Hence in addition totechnology improvements automakers can develop dedicatedplatforms and modular production platforms for BEVs toreduce costs as much as possible e lowest priority is ex-terior and interior (0009) Consequently the design of theexterior and interior has the least impact on consumersrsquodecisions e results indicate that the priority of criterionimprovement from top to bottom and the improvement of themost influential criteria would provide the largest effects It ispossible that improving different criteria will strongly in-fluence the results directly and indirectly and improving themost influential criteria provides the most substantial effects

Fourth the comparison results of ten BEVs in Table 15show that Aion S (P3) is the best choice In order to verifythe feasibility and validity of this hybrid method wecombine the criteria importance through inter-criteriacorrelation (CRITIC) method with modified VIKOR forranking the ten BEVs e CRITIC method proposed byDiakoulaki et al [112] is used to calculate attributesrsquoweights by standard deviation As derived in Table 16although the sorting of the BEVs based on different MCDMmethods varies partly Aion S (P3) is still the optimalchoice is means that the proposed method is effective atranking and selecting BEV alternatives

Finally from the perspective of dimension the comfort(D) featuring the largest gap value (0467 0276 03110321 0380) with cars of BAOJUN E100 (P1) MG EZS (P4)Aeolus E70 (P5) BYD Yuan (P7) and EMGRAND EV (P9)should be the priority for improvement if banking managerswish to enhance service innovation e dynamics (A) di-mension constituting the largest gap value (0191 03110435) with cars of Aion S (P3) BESTUNE B30EV (P8) andORA R1 (P10) should improve as a priority In the BAIC EUSeries (P2) the safety (C) dimension features the largest gapwith the value of 0267 which should be a top priority forimprovement to achieve aspiration levels In the alternativeof GEOMETRY A (P6) the cost (E) dimension constitutesthe largest gap with the value of 0285 According to the samerule the priority improvement following sustainable de-velopment context can be sequenced in the criteria for eachBEV Given these critical empirical findings our analysisresults as holistically formulated in Table 17 present theoptimized path for different BEV models Hence relevantdepartments can not only use this method to define gaps butalso enhance customersrsquo purchase willingness in Chinabased on the priorities of influence weights or gap values

6 Conclusions

BEVs are not only the mainstream of the future automobileindustry but also the top priority for implementing nationalenergy policy and achieving sustainable development of theautomobile industry However a large proportion of

consumers still hold a wait-and-see attitude [58 60] ChinarsquosBEVmarket has always been a policy-orientedmodelere isstill much room for improvement in consumersrsquo purchasewillingness is study creates a novel hybrid model com-bining fine-grained sentiment analysis and an MCDMmodelto assess BEV alternatives from the customersrsquo point of viewIn summary the main results of this study are as follows

(1) e comprehensive evaluation indicator systemincluding five dimensions (dynamics technologysafety comfort and cost) and seventeen criteria isobtained by the LDA model based on fine-grainedsentiment analysis

(2) Safety (C) is the most influential dimension followedby technology (B) dynamics (A) comfort (D) andcost (E) Safety improvements can lead to changes inother dimensions us government and providerscan improve BEV attributes more flexibly and ac-curately based on the identification of cause-effectrelationships between the dimensions and the actualsituation of BEVs

(3) e DANP analysis shows that price (E1) incentives(E2) and after-sales cost (E3) are ranked as the topcriteria and they all belong to the cost dimensionMoreover the lowest influential weights involvesuspension (D2) car seat (D3) exterior and interior(D4) and they all belong to the comfort dimensionGovernment and providers can determine the orderof improvement based on the weight

(4) e modified VIKOR results indicated that theselection strategies of ten BEVs are P3gtP2gtP4gt P8gtP6gtP9gt P5gtP7gt P10gtP1 which pre-sented that P3 (Aion S) is the best choice In additionthere is still room for improvement in vehicle at-tributes and the indicator gaps and optimized pathare different for each BEV Taking the BEV namedAion S as an example it has the highest integratedscores but it still falls short of aspiration levelsAccording to the gaps ranging from large to smallthe first step of the optimized path should improvethe dynamics

(5) In this study we find some attributes that have beenneglected in previous studies such as maximumpower max torque and after-sales cost affect cus-tomersrsquo satisfaction With the gradual maturity ofBEV technology the effects of more detailed vehicleattributes should be considered [30] erefore thewidespread promotion of BEVs cannot rely solely onpolicy incentives and the government should takeactive measures to promote automobile companiesto achieve the urgent improvements of BEV attri-butes [113 114]

However there are some limitations in this studywhich need to be solved in future research First due tothe differences in consumption concepts and the influenceof psychological factors consumers can be divided intodifferent levels for further analysis Second based on the

18 Mathematical Problems in Engineering

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

complexity of things and the ambiguities of subjectivejudgments interval numbers and fuzzy numbers can beintroduced in future research on consumersrsquo adoption ofBEVs Finally some forecasting methods can be used topredict the motivation of consumers

Abbreviations

AHP Analytic hierarchy processANP Analytic network processBAIC BJEV Beijing Automotive Industry Corporation

Beijing Electric VehicleBEVs Battery electric vehiclesBYD Build your dreamsCOPRAS Complex proportional assessmentCRITIC Criteria importance through intercriteria

correlationDANP DEMATEL-based analytic network processDC Direct currentDEMATEL Decision-making trial and evaluation

laboratoryELECTRE ELimination and Choice Expressing the

REalityEVs Electric vehiclesFAW First automobile workshopGAC NE Guangzhou Automobile Group Co LtdGHG Greenhouse gasICEVs Internal combustion engine vehiclesINRM Influential network relationship mapLCA Life cycle assessmentLDA Latent Dirichlet AllocationMCDM Multicriteria decision-making methodMARCOS Measurement of Alternatives and Ranking

According to Compromise SolutionPROMETHEE Preference Ranking Organization Method

for Enrichment EvaluationRampD Research and DevelopmentSAIC Shanghai Automotive Industry

CorporationSECA Simultaneous Evaluation of Criteria and

AlternativesSGMW Shanghai General Motors WulingSWARA Stepwise Weight Assessment Ratio

AnalysisTOPSIS Technique for Order of Preference by

Similarity to Ideal SolutionVIKOR Vlse Kriterijuska Optimizacija I

Komoromisno Resenje

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is study was supported by the National Natural ScienceFoundation of China (Grant No 71774105) the MOE(Ministry of Education in China) Youth Foundation Projectof Humanities and Social Sciences (Grant No18YJCZH143) the Philosophy and Social Sciences Researchof Higher Learning Institutions of Shanxi (Grant No2019W064) and Shanxi Province Graduate Education In-novation Project in 2019 (Grant No 2019SY402)

References

[1] T G Poder and J He ldquoWillingness to pay for a cleaner carthe case of car pollution in Quebec and Francerdquo Energyvol 130 pp 48ndash54 2017

[2] A Ardeshiri and T H Rashidi ldquoWillingness to pay for fastcharging station for electric vehicles with limited marketpenetration makingrdquo Energy Policy vol 147 Article ID111822 2020

[3] International Energy Agency (IEA) Data and Statistics In-ternational Energy Agency Paris France 2020 httpswwwieaorgdata-and-statisticscountry=WORLDampfuel=CO220emissionsampindicator=CO2BySector

[4] Y W Cheng J Chen and K Lin ldquoExploring consumerattitudes and public opinions on battery electric vehiclesrdquoJournal of Renewable and Sustainable Energy vol 7 no 4Article ID 043122 2015

[5] J Geng R Long H Chen T Yue W Li and Q LildquoldquoExploring multiple motivations on urban residentsrsquo travelmode choices an empirical study from Jiangsu province inChinardquo Sustainability vol 9 no 1 p 136 2017

[6] Z Miao T Balezentis S Shao and D Chang ldquoEnergy useindustrial soot and vehicle exhaust pollution-Chinarsquos re-gional air pollution recognition performance decompositionand governancerdquo Energy Economics vol 83 pp 501ndash5142019

[7] A Haines A J McMichael K R Smith et al ldquoPublic healthbenefits of strategies to reduce greenhouse-gas emissionsoverview and implications for policy makersrdquo e Lancetvol 374 no 9707 pp 2104ndash2114 2009

[8] J Du M Ouyang and J Chen ldquoProspects for Chineseelectric vehicle technologies in 2016-2020 ambition andrationalityrdquo Energy vol 120 pp 584ndash596 2017

[9] Z Li A Khajepour and J Song ldquoA comprehensive review ofthe key technologies for pure electric vehiclesrdquo Energyvol 182 pp 824ndash839 2019

[10] L Qian and D Soopramanien ldquoUsing diffusion models toforecast market size in emergingmarkets with applications tothe Chinese car marketrdquo Journal of Business Researchvol 67 no 6 pp 1226ndash1232 2014

[11] M Song G Zhang K Fang and J Zhang ldquoRegional op-erational and environmental performance evaluation inChina non-radial DEA methodology under natural andmanagerial disposabilityrdquo Natural Hazards vol 84 no 1pp 243ndash265 2016

[12] Q Li R Long H Chen and J Geng ldquoLow purchase will-ingness for battery electric vehicles analysis and simulationbased on the fault tree modelrdquo Sustainability vol 9 no 5p 809 2017

[13] X Zhang J Xie R Rao and Y Liang ldquoPolicy incentives forthe adoption of electric vehicles across countriesrdquo Sustain-ability vol 6 no 11 pp 8056ndash8078 2014

Mathematical Problems in Engineering 19

[14] Z Rezvani J Jansson and J Bodin ldquoAdvances in consumerelectric vehicle adoption research a review and researchagendardquo Transportation Research Part D Transport andEnvironment vol 34 pp 122ndash136 2015

[15] X Zhao O C Doering and W E Tyner ldquoe economiccompetitiveness and emissions of battery electric vehicles inChinardquo Applied Energy vol 156 pp 666ndash675 2015

[16] F Manrıquez E Sauma J Aguado S de la Torre andJ Contreras ldquoe impact of electric vehicle chargingschemes in power system expansion planningrdquo AppliedEnergy vol 262 Article ID 114527 2020

[17] Y Kwon S Son and K Jang ldquoUser satisfaction with batteryelectric vehicles in South Koreardquo Transportation ResearchPart D Transport and Environment vol 82 Article ID102306 2020

[18] e General Office of the State Council e New Develop-ment Plan for the NEV Industry e General Office of theState Council Beijing China 2020 httpwwwgovcnzhengcecontent2020-1102content_5556716htm

[19] N Wang L Tang W Zhang and J Guo ldquoHow to face thechallenges caused by the abolishment of subsidies for electricvehicles in Chinardquo Energy vol 166 pp 359ndash372 2019

[20] C Lu H-C Liu J Tao K Rong and Y-C Hsieh ldquoA keystakeholder-based financial subsidy stimulation for ChineseEV industrialization a system dynamics simulationrdquo Tech-nological Forecasting and Social Change vol 118 pp 1ndash142017

[21] X Sun X Liu Y Wang and F Yuan ldquoe effects of publicsubsidies on emerging industry an agent-based model of theelectric vehicle industryrdquo Technological Forecasting andSocial Change vol 140 pp 281ndash295 2019

[22] N Wang H Pan and W Zheng ldquoAssessment of the in-centives on electric vehicle promotion in Chinardquo Trans-portation Research Part A Policy and Practice vol 101pp 177ndash189 2017

[23] A Jenn K Springel and A R Gopal ldquoEffectiveness ofelectric vehicle incentives in the United Statesrdquo EnergyPolicy vol 119 pp 349ndash356 2018

[24] W Li R Long and H Chen ldquoConsumersrsquo evaluation ofnational new energy vehicle policy in China an analysisbased on a four paradigm modelrdquo Energy Policy vol 99pp 33ndash41 2016

[25] S Wang J Fan D Zhao S Yang and Y Fu ldquoPredictingconsumersrsquo intention to adopt hybrid electric vehicles usingan extended version of the theory of planned behaviormodelrdquo Transportation vol 43 no 1 pp 123ndash143 2016

[26] China Association of Automobile Manufactures (CAAM)Economic Operation of Automobile Industry China Asso-ciation of Automobile Manufactures Beijing China 2020httpwwwcaamorgcnchn4cate_30list_1html

[27] W Li R Long H Chen and J Geng ldquoA review of factorsinfluencing consumer intentions to adopt battery electricvehiclesrdquo Renewable and Sustainable Energy Reviews vol 78pp 318ndash328 2017

[28] H-C Liu X-Y You Y-X Xue and X Luan ldquoExploringcritical factors influencing the diffusion of electric vehicles inChina a multi-stakeholder perspectiverdquo Research inTransportation Economics vol 66 pp 46ndash58 2017

[29] G Cecere N Corrocher and M Guerzoni ldquoPrice or per-formance A probabilistic choice analysis of the intention tobuy electric vehicles in European countriesrdquo Energy Policyvol 118 pp 19ndash32 2018

[30] F Liao E Molin and B van Wee ldquoConsumer preferencesfor electric vehicles a literature reviewrdquo Transport Reviewsvol 37 no 3 pp 252ndash275 2017

[31] F Nazari E Rahimi and A K Mohammadian ldquoSimulta-neous estimation of battery electric vehicle adoption withendogenous willingness to payrdquo eTransportation vol 1Article ID 100008 2019

[32] K Petrauskiene M Skvarnaviciute and J DvarionieneldquoComparative environmental life cycle assessment of electricand conventional vehicles in Lithuaniardquo Journal of CleanerProduction vol 246 Article ID 119042 2020

[33] J Shin W-S Hwang and H Choi ldquoCan hydrogen fuelvehicles be a sustainable alternative on vehicle marketcomparison of electric and hydrogen fuel cell vehiclesrdquoTechnological Forecasting and Social Change vol 143pp 239ndash248 2019

[34] F Ecer ldquoA consolidatedMCDM framework for performanceassessment of battery electric vehicles based on rankingstrategiesrdquo Renewable and Sustainable Energy Reviewsvol 143 Article ID 110916 2021

[35] H C Sonar and S D Kulkarni ldquoAn integrated AHP-MABAC approach for electric vehicle selectionrdquo ldquoResearchin Transportation Business amp Management vol 260 ArticleID 100665 2021

[36] J Bas C Cirillo and E Cherchi ldquoClassification of potentialelectric vehicle purchasers a machine learning approachrdquoTechnological Forecasting and Social Change vol 168 ArticleID 120759 2021

[37] D De Clercq N F Diop D Jain B Tan and ZWen ldquoMulti-label classification and interactive NLP-based visualization ofelectric vehicle patent datardquo World Patent Informationvol 58 Article ID 101903 2019

[38] T Yang C Xing and X Li ldquoEvaluation and analysis of new-energy vehicle industry policies in the context of technicalinnovation in Chinardquo Journal of Cleaner Production vol 281Article ID 125126 2021

[39] M Naumanen T Uusitalo E Huttunen-Saarivirta andR van der Have ldquoldquoDevelopment strategies for heavy dutyelectric battery vehicles comparison between China EUJapan and USArdquo Resources Conservation and Recyclingvol 151 Article ID 104413 2019

[40] D Aguilar-Dominguez J Ejeh A D F Dunbar andS F Brown ldquoMachine learning approach for electric vehicleavailability forecast to provide vehicle-to-home servicesrdquoEnergy Reports vol 7 pp 71ndash80 2021

[41] R Basso B Kulcsar and I Sanchez-Diaz ldquoElectric vehiclerouting problem with machine learning for energy predic-tionrdquo Transportation Research Part B Methodologicalvol 145 pp 24ndash55 2021

[42] S-C Ma Y Fan J-F Guo J-H Xu and J Zhu ldquoAnalysingonline behaviour to determine Chinese consumersrsquo prefer-ences for electric vehiclesrdquo Journal of Cleaner Productionvol 229 pp 244ndash255 2019

[43] A Alsaeedi and M Z Khan ldquoA study on sentiment analysistechniques of twitter datardquo International Journal of Ad-vanced Computer Science and Applications vol 10 no 2pp 361ndash374 2019

[44] R Jena ldquoAn empirical case study on Indian consumersrsquosentiment towards electric vehicles a big data analyticsapproachrdquo Industrial Marketing Management vol 90pp 605ndash616 2020

[45] W-Y Chiu G-H Tzeng and H-L Li ldquoA new hybridMCDM model combining DANP with VIKOR to improve

20 Mathematical Problems in Engineering

e-store businessrdquo Knowledge-Based Systems vol 37pp 48ndash61 2013

[46] M H Aghdaie S H Zolfani and E K Zavadskas ldquoDecisionmaking in machine tool selection an integrated approachwith SWARA and COPRAS-G methodsrdquo Energy Economicsvol 24 no 1 pp 5ndash17 2013

[47] C Li M Negnevitsky X Wang W L Yue and X ZouldquoMulti-criteria analysis of policies for implementing cleanenergy vehicles in Chinardquo Energy Policy vol 129 pp 826ndash840 2019

[48] N C Onat S Gumus M Kucukvar and O Tatari ldquoAp-plication of the TOPSIS and intuitionistic fuzzy set ap-proaches for ranking the life cycle sustainability performanceof alternative vehicle technologiesrdquo Sustainable Productionand Consumption vol 6 pp 12ndash25 2016

[49] S Ccedilali and S Y Balaman ldquoA novel outranking based multicriteria group decision making methodology integratingELECTRE and VIKOR under intuitionistic fuzzy environ-mentrdquo Expert Systems with Applications vol 119 pp 36ndash502019

[50] E Strantzali K Aravossis and G A Livanos ldquoEvaluation offuture sustainable electricity generation alternatives the caseof a Greek islandrdquo Renewable and Sustainable Energy Re-views vol 76 pp 775ndash787 2017

[51] M C Das A Pandey A K Mahato and R K SinghldquoComparative performance of electric vehicles using eval-uation of mixed datardquo Opsearch vol 56 no 3pp 1067ndash1090 2019

[52] G H Tzeng C W Lin and S Opricovic ldquoMulti-criteriaanalysis of alternative-fuel buses for public transportationrdquoEnergy Policy vol 33 no 11 pp 1373ndash1383 2005

[53] H Liang J Ren R Lin and Y Liu ldquoAlternative-fuel basedvehicles for sustainable transportation a fuzzy group deci-sion supporting framework for sustainability prioritizationrdquoTechnological Forecasting and Social Change vol 140pp 33ndash43 2019

[54] S M Skippon N Kinnear L Lloyd and J Stannard ldquoHowexperience of use influences mass-market driversrsquo willing-ness to consider a battery electric vehicle a randomisedcontrolled trialrdquo Transportation Research Part A Policy andPractice vol 92 pp 26ndash42 2016

[55] R Liu Z Ding X Jiang J Sun Y Jiang and W QiangldquoHow does experience impact the adoption willingness ofbattery electric vehicles e role of psychological factorsrdquoEnvironmental Science and Pollution Research vol 27 no 20pp 25230ndash25247 2020

[56] J H Kim G Lee J Y Park J Hong and J Park ldquoConsumerintentions to purchase battery electric vehicles in KoreardquoEnergy Policy vol 132 pp 736ndash743 2019

[57] W Li R Long H Chen and J Geng ldquoHousehold factorsand adopting intention of battery electric vehicles a multi-group structural equation model analysis among consumersin Jiangsu Province Chinardquo Natural Hazards vol 87 no 2pp 945ndash960 2017

[58] Z-Y She Q Qing Sun J-J Ma and B-C Xie ldquoWhat are thebarriers to widespread adoption of battery electric vehiclesA survey of public perception in Tianjin Chinardquo TransportPolicy vol 56 pp 29ndash40 2017

[59] X Dong B Zhang B Wang and Z Wang ldquoUrbanhouseholdsrsquo purchase intentions for pure electric vehiclesunder subsidy contexts in China do cost factors matterrdquoTransportation Research Part A Policy and Practice vol 135pp 183ndash197 2020

[60] L Li Z Wang L Chen and Z Wang ldquoConsumer prefer-ences for battery electric vehicles a choice experimentalsurvey in Chinardquo Transportation Research Part D Transportand Environment vol 78 Article ID 102185 2020

[61] M Kukova C Diels P Jordan M Franco-JorgeJ Anderson and H Kharouf ldquoDo we really know whichvehicle attributes are important for customersrdquo in Pro-ceedings of the 10th International Conference on Design ampEmotion pp 27ndash30 Amsterdam Netherlands September2016

[62] S Almeida Neves A Cardoso Marques and J AlbertoFuinhas ldquoTechnological progress and other factors behindthe adoption of electric vehicles empirical evidence for EUcountriesrdquo Research in Transportation Economics vol 74pp 28ndash39 2019

[63] C F Chen G Z de Rubens L Noel J Kester andB K Sovacool ldquoAssessing the socio-demographic technicaleconomic and behavioral factors of Nordic electric vehicleadoption and the influence of vehicle-to-grid preferencesrdquoRenewable and Sustainable Energy Reviews vol 121 ArticleID 109692 2020

[64] T L Saaty Decision Making with Dependence and Feedbacke Analytic Network Process RWS Publications PittsburghPA USA 1996

[65] D M Blei ldquoProbabilistic topic modelsrdquo Communications ofthe ACM vol 55 no 4 pp 77ndash84 2012

[66] D Blei M Jordan and A Y Ng ldquoLatentdirichlet allocationrdquo Journal of machine Learning researchvol 3 pp 8993ndash1022 2003

[67] I Golcuk and A Baykasoglu ldquoAn analysis of DEMATELapproaches for criteria interaction handling within ANPrdquoldquoExpert Systems with Applications vol 46 pp 346ndash366 2016

[68] A Gabus and E Fontela World Problems an Invitation toFurther ought within the Framework of DEMATEL Bat-telle Geneva Research Center Geneva Switzerland 1972

[69] K-Y Shen S-K Hu and G-H Tzeng ldquoFinancial modelingand improvement planning for the life insurance industry byusing a rough knowledge based hybrid MCDM modelrdquoInformation Sciences vol 375 pp 296ndash313 2017

[70] S K Liao H Y Hsu and K L Chang ldquoOTAs selection forhot spring hotels by a hybrid MCDM modelrdquo MathematicalProblems in Engineering vol 2019 Article ID 42513629 pages 2019

[71] Y Su D Liang and W Guo ldquoApplication of multiattributedecision-making for evaluating regional innovation capac-ityrdquoMathematical Problems in Engineering vol 2020 ArticleID 2851840 20 pages 2020

[72] W Song Y Zhu and Q Zhao ldquoAnalyzing barriers foradopting sustainable online consumption a rough hierar-chical DEMATEL methodrdquo Computers amp Industrial Engi-neering vol 140 Article ID 106279 2020

[73] Q Zhao P H Tsai and J L Wang ldquoldquoImproving financialservice innovation strategies for enhancing Chinarsquos bankingindustry competitive advantage during the fintech revolu-tion a hybrid MCDM modelrdquo Sustainability vol 11 no 5p 1419 2019

[74] S-B Tsai ldquoUsing the DEMATEL model to explore the jobsatisfaction of research and development professionals inChinarsquos photovoltaic cell industryrdquo Renewable and Sus-tainable Energy Reviews vol 81 pp 62ndash68 2018

[75] J J H Liou G-H Tzeng and H-C Chang ldquoAirline safetymeasurement using a hybrid modelrdquo Journal of Air Trans-port Management vol 13 no 4 pp 243ndash249 2007

Mathematical Problems in Engineering 21

[76] W-S Lee G-H Tzeng J-L Guan K-T Chien andJ-M Huang ldquoCombined MCDM techniques for exploringstock selection based on Gordonmodelrdquo Expert Systems withApplications vol 36 no 3 pp 6421ndash6430 2009

[77] R Wang X Li C Xu and F Li ldquoStudy on location decisionframework of electric vehicle battery swapping station usinga hybrid MCDM methodrdquo Sustainable Cities and Societyvol 61 Article ID 102149 2020

[78] Y-P Ou Yang H-M Shieh and G-H Tzeng ldquoA VIKORtechnique based on DEMATEL and ANP for informationsecurity risk control assessmentrdquo Information Sciencesvol 232 pp 482ndash500 2013

[79] R Liu H Sun L Zhang et al ldquoLow-carbon energy planninga hybrid MCDM method combining DANP and VIKORapproachrdquo Energies vol 11 no 12 p 3401 2018

[80] H-C Liu J-X You L Zhen and X-J Fan ldquoA novel hybridmultiple criteria decision making model for material selec-tion with target-based criteriardquo Materials amp Design vol 60pp 380ndash390 2014

[81] G Buyukozkan and S Guleryuz ldquoAn integrated DEMATEL-ANP approach for renewable energy resources selection inTurkeyrdquo International Journal of Production Economicsvol 182 pp 435ndash448 2016

[82] S Opricovic Multicriteria Optimization of Civil EngineeringSystems Vol 2 Faculty of Civil Engineering BelgradeSerbia 1998

[83] P L Yu ldquoA class of solutions for group decision problemsrdquoManagement Science vol 19 no 8 pp 936ndash946 1973

[84] S Opricovic and G-H Tzeng ldquoCompromise solution byMCDM methods a comparative analysis of VIKOR andTOPSISrdquo European Journal of Operational Research vol 156no 2 pp 445ndash455 2004

[85] S Opricovic and G-H Tzeng ldquoExtended VIKOR method incomparison with outranking methodsrdquo European Journal ofOperational Research vol 178 no 2 pp 514ndash529 2007

[86] M Achtnicht G Buhler and C Hermeling ldquoe impact offuel availability on demand for alternative-fuel vehiclesrdquoTransportation Research Part D Transport and Environmentvol 17 no 3 pp 262ndash269 2012

[87] Y Zhang Z Qian F Sprei and B Li ldquoe impact of carspecifications prices and incentives for battery electric ve-hicles in Norway choices of heterogeneous consumersrdquoTransportation Research Part C Emerging Technologiesvol 69 pp 386ndash401 2016

[88] J P Helveston Y Liu E M Feit E Fuchs E Klampfl andJ J Michalek ldquoWill subsidies drive electric vehicle adoptionMeasuring consumer preferences in the US and ChinardquoTransportation Research Part A Policy and Practice vol 73pp 96ndash112 2015

[89] E Valeri and R Danielis ldquoSimulating the market penetrationof cars with alternative fuelpowertrain technologies in ItalyrdquoTransport Policy vol 37 pp 44ndash56 2015

[90] L Noel A Papu Carrone A F Jensen G Zarazua deRubens J Kester and B K Sovacool ldquoWillingness to pay forelectric vehicles and vehicle-to-grid applications a Nordicchoice experimentrdquo Energy Economics vol 78 pp 525ndash5342019

[91] B Junquera B Moreno and R Alvarez ldquoAnalyzing con-sumer attitudes towards electric vehicle purchasing inten-tions in Spain technological limitations and vehicleconfidencerdquo ldquoTechnological Forecasting and Social Changevol 109 pp 6ndash14 2016

[92] S-C Ma J-H Xu and Y Fan ldquoWillingness to pay andpreferences for alternative incentives to EV purchase

subsidies an empirical study in Chinardquo Energy Economicsvol 81 pp 197ndash215 2019

[93] A F Jensen E Cherchi and J de Dios Ortuzar ldquoA longpanel survey to elicit variation in preferences and attitudes inthe choice of electric vehiclesrdquo Transportation vol 41 no 5pp 973ndash993 2014

[94] M K Hidrue G R Parsons W Kempton andM P Gardner ldquoWillingness to pay for electric vehicles andtheir attributesrdquo Resource and Energy Economics vol 33no 3 pp 686ndash705 2011

[95] P Ayoung-Chee C D Mack R Kaufman and E BulgerldquoPredicting severe injury using vehicle telemetry datardquoJournal of trauma and acute care surgery vol 74 no 1pp 190ndash195 2013

[96] F Buhler P Cocron I Neumann T Franke and J F KremsldquoIs EV experience related to EV acceptance Results from aGerman field studyrdquo Transportation Research Part F TrafficPsychology and Behaviour vol 25 pp 34ndash49 2014

[97] P Cocron F Buhler T Franke I Neumann B Dielmannand J F Krems ldquoEnergy recapture through deceleration -regenerative braking in electric vehicles from a user per-spectiverdquo Ergonomics vol 56 no 8 pp 1203ndash1215 2013

[98] F Schmalfuszlig K Muhl and J F Krems ldquoDirect experiencewith battery electric vehicles (BEVs) matters when evaluatingvehicle attributes attitude and purchase intentionrdquo Trans-portation Research Part F Traffic Psychology and Behaviourvol 46 pp 47ndash69 2017

[99] G Long F Ding N Zhang J Zhang and A Qin ldquoRe-generative active suspension system with residual energy forin-wheel motor driven electric vehiclerdquo Applied Energyvol 260 Article ID 114180 2020

[100] L Zhang and Q Qin ldquoChinarsquos new energy vehicle policiesevolution comparison and recommendationrdquo Trans-portation Research Part A Policy and Practice vol 110pp 57ndash72 2018

[101] M M Lopes F Moura and L M Martinez ldquoA rule-basedapproach for determining the plausible universe of electricvehicle buyers in the Lisbon Metropolitan Areardquo Trans-portation Research Part A Policy and Practice vol 59pp 22ndash36 2014

[102] P Weldon P Morrissey and M OrsquoMahony ldquoLong-termcost of ownership comparative analysis between electricvehicles and internal combustion engine vehiclesrdquo Sus-tainable Cities and Society vol 39 pp 578ndash591 2018

[103] N Wang L Tang and H Pan ldquoEffectiveness of policy in-centives on electric vehicle acceptance in China a discretechoice analysisrdquo Transportation Research Part A Policy andPractice vol 105 pp 210ndash218 2017

[104] W Li R Long H Chen T Yang J Geng and M YangldquoEffects of personal carbon trading on the decision to adoptbattery electric vehicles analysis based on a choice experi-ment in Jiangsu Chinardquo Applied Energy vol 209 pp 478ndash488 2018

[105] X Zhang Y Liang E Yu R Rao and J Xie ldquoReview ofelectric vehicle policies in China content summary and effectanalysisrdquo Renewable and Sustainable Energy Reviews vol 70pp 698ndash714 2017

[106] M-K Kim J Oh J-H Park and C Joo ldquoPerceived valueand adoption intention for electric vehicles in Koreamoderating effects of environmental traits and governmentsupportsrdquo Energy vol 159 pp 799ndash809 2018

[107] C Y Huang and I Tung ldquoStrategies for heterogeneous RampDalliances of in vitro diagnostics firms in rapidly catching-up

22 Mathematical Problems in Engineering

economiesrdquo International Journal of Environmental Re-search and Public Health vol 17 no 10 2020

[108] T Lieven S Muhlmeier S Henkel and J F Waller ldquoWhowill buy electric cars An empirical study in GermanyrdquoTransportation Research Part D Transport and Environmentvol 16 no 3 pp 236ndash243 2011

[109] S Skippon and M Garwood ldquoResponses to battery electricvehicles UK consumer attitudes and attributions of symbolicmeaning following direct experience to reduce psychologicaldistancerdquo Transportation Research Part D Transport andEnvironment vol 16 no 7 pp 525ndash531 2011

[110] Y Liu Y Yang Y Liu and G H Tzeng ldquoImproving sus-tainable mobile health care promotion a novel hybridMCDM methodrdquo Sustainability vol 11 no 3 2019

[111] F Del Pero M Delogu and M Pierini ldquoe effect oflightweighting in automotive LCA perspective estimation ofmass-induced fuel consumption reduction for gasolineturbocharged vehiclesrdquo Journal of Cleaner Productionvol 154 pp 566ndash577 2017

[112] D Diakoulaki G Mavrotas and L Papayannakis ldquoDeter-mining objective weights in multiple criteria problems thecritic methodrdquo Computers amp Operations Research vol 22no 7 pp 763ndash770 1995

[113] Q Qiao F Zhao H Hao S Jiang and Z Liu ldquoCradle-to-gate greenhouse gas emissions of battery electric and internalcombustion engine vehicles in Chinardquo Applied Energyvol 204 2017

[114] I A Nienhueser and Y Qiu ldquoEconomic and environmentalimpacts of providing renewable energy for electric vehiclechargingmdasha choice experiment studyrdquo Applied Energyvol 180 pp 256ndash268 2016

Mathematical Problems in Engineering 23

[76] W-S Lee G-H Tzeng J-L Guan K-T Chien andJ-M Huang ldquoCombined MCDM techniques for exploringstock selection based on Gordonmodelrdquo Expert Systems withApplications vol 36 no 3 pp 6421ndash6430 2009

[77] R Wang X Li C Xu and F Li ldquoStudy on location decisionframework of electric vehicle battery swapping station usinga hybrid MCDM methodrdquo Sustainable Cities and Societyvol 61 Article ID 102149 2020

[78] Y-P Ou Yang H-M Shieh and G-H Tzeng ldquoA VIKORtechnique based on DEMATEL and ANP for informationsecurity risk control assessmentrdquo Information Sciencesvol 232 pp 482ndash500 2013

[79] R Liu H Sun L Zhang et al ldquoLow-carbon energy planninga hybrid MCDM method combining DANP and VIKORapproachrdquo Energies vol 11 no 12 p 3401 2018

[80] H-C Liu J-X You L Zhen and X-J Fan ldquoA novel hybridmultiple criteria decision making model for material selec-tion with target-based criteriardquo Materials amp Design vol 60pp 380ndash390 2014

[81] G Buyukozkan and S Guleryuz ldquoAn integrated DEMATEL-ANP approach for renewable energy resources selection inTurkeyrdquo International Journal of Production Economicsvol 182 pp 435ndash448 2016

[82] S Opricovic Multicriteria Optimization of Civil EngineeringSystems Vol 2 Faculty of Civil Engineering BelgradeSerbia 1998

[83] P L Yu ldquoA class of solutions for group decision problemsrdquoManagement Science vol 19 no 8 pp 936ndash946 1973

[84] S Opricovic and G-H Tzeng ldquoCompromise solution byMCDM methods a comparative analysis of VIKOR andTOPSISrdquo European Journal of Operational Research vol 156no 2 pp 445ndash455 2004

[85] S Opricovic and G-H Tzeng ldquoExtended VIKOR method incomparison with outranking methodsrdquo European Journal ofOperational Research vol 178 no 2 pp 514ndash529 2007

[86] M Achtnicht G Buhler and C Hermeling ldquoe impact offuel availability on demand for alternative-fuel vehiclesrdquoTransportation Research Part D Transport and Environmentvol 17 no 3 pp 262ndash269 2012

[87] Y Zhang Z Qian F Sprei and B Li ldquoe impact of carspecifications prices and incentives for battery electric ve-hicles in Norway choices of heterogeneous consumersrdquoTransportation Research Part C Emerging Technologiesvol 69 pp 386ndash401 2016

[88] J P Helveston Y Liu E M Feit E Fuchs E Klampfl andJ J Michalek ldquoWill subsidies drive electric vehicle adoptionMeasuring consumer preferences in the US and ChinardquoTransportation Research Part A Policy and Practice vol 73pp 96ndash112 2015

[89] E Valeri and R Danielis ldquoSimulating the market penetrationof cars with alternative fuelpowertrain technologies in ItalyrdquoTransport Policy vol 37 pp 44ndash56 2015

[90] L Noel A Papu Carrone A F Jensen G Zarazua deRubens J Kester and B K Sovacool ldquoWillingness to pay forelectric vehicles and vehicle-to-grid applications a Nordicchoice experimentrdquo Energy Economics vol 78 pp 525ndash5342019

[91] B Junquera B Moreno and R Alvarez ldquoAnalyzing con-sumer attitudes towards electric vehicle purchasing inten-tions in Spain technological limitations and vehicleconfidencerdquo ldquoTechnological Forecasting and Social Changevol 109 pp 6ndash14 2016

[92] S-C Ma J-H Xu and Y Fan ldquoWillingness to pay andpreferences for alternative incentives to EV purchase

subsidies an empirical study in Chinardquo Energy Economicsvol 81 pp 197ndash215 2019

[93] A F Jensen E Cherchi and J de Dios Ortuzar ldquoA longpanel survey to elicit variation in preferences and attitudes inthe choice of electric vehiclesrdquo Transportation vol 41 no 5pp 973ndash993 2014

[94] M K Hidrue G R Parsons W Kempton andM P Gardner ldquoWillingness to pay for electric vehicles andtheir attributesrdquo Resource and Energy Economics vol 33no 3 pp 686ndash705 2011

[95] P Ayoung-Chee C D Mack R Kaufman and E BulgerldquoPredicting severe injury using vehicle telemetry datardquoJournal of trauma and acute care surgery vol 74 no 1pp 190ndash195 2013

[96] F Buhler P Cocron I Neumann T Franke and J F KremsldquoIs EV experience related to EV acceptance Results from aGerman field studyrdquo Transportation Research Part F TrafficPsychology and Behaviour vol 25 pp 34ndash49 2014

[97] P Cocron F Buhler T Franke I Neumann B Dielmannand J F Krems ldquoEnergy recapture through deceleration -regenerative braking in electric vehicles from a user per-spectiverdquo Ergonomics vol 56 no 8 pp 1203ndash1215 2013

[98] F Schmalfuszlig K Muhl and J F Krems ldquoDirect experiencewith battery electric vehicles (BEVs) matters when evaluatingvehicle attributes attitude and purchase intentionrdquo Trans-portation Research Part F Traffic Psychology and Behaviourvol 46 pp 47ndash69 2017

[99] G Long F Ding N Zhang J Zhang and A Qin ldquoRe-generative active suspension system with residual energy forin-wheel motor driven electric vehiclerdquo Applied Energyvol 260 Article ID 114180 2020

[100] L Zhang and Q Qin ldquoChinarsquos new energy vehicle policiesevolution comparison and recommendationrdquo Trans-portation Research Part A Policy and Practice vol 110pp 57ndash72 2018

[101] M M Lopes F Moura and L M Martinez ldquoA rule-basedapproach for determining the plausible universe of electricvehicle buyers in the Lisbon Metropolitan Areardquo Trans-portation Research Part A Policy and Practice vol 59pp 22ndash36 2014

[102] P Weldon P Morrissey and M OrsquoMahony ldquoLong-termcost of ownership comparative analysis between electricvehicles and internal combustion engine vehiclesrdquo Sus-tainable Cities and Society vol 39 pp 578ndash591 2018

[103] N Wang L Tang and H Pan ldquoEffectiveness of policy in-centives on electric vehicle acceptance in China a discretechoice analysisrdquo Transportation Research Part A Policy andPractice vol 105 pp 210ndash218 2017

[104] W Li R Long H Chen T Yang J Geng and M YangldquoEffects of personal carbon trading on the decision to adoptbattery electric vehicles analysis based on a choice experi-ment in Jiangsu Chinardquo Applied Energy vol 209 pp 478ndash488 2018

[105] X Zhang Y Liang E Yu R Rao and J Xie ldquoReview ofelectric vehicle policies in China content summary and effectanalysisrdquo Renewable and Sustainable Energy Reviews vol 70pp 698ndash714 2017

[106] M-K Kim J Oh J-H Park and C Joo ldquoPerceived valueand adoption intention for electric vehicles in Koreamoderating effects of environmental traits and governmentsupportsrdquo Energy vol 159 pp 799ndash809 2018

[107] C Y Huang and I Tung ldquoStrategies for heterogeneous RampDalliances of in vitro diagnostics firms in rapidly catching-up

22 Mathematical Problems in Engineering

economiesrdquo International Journal of Environmental Re-search and Public Health vol 17 no 10 2020

[108] T Lieven S Muhlmeier S Henkel and J F Waller ldquoWhowill buy electric cars An empirical study in GermanyrdquoTransportation Research Part D Transport and Environmentvol 16 no 3 pp 236ndash243 2011

[109] S Skippon and M Garwood ldquoResponses to battery electricvehicles UK consumer attitudes and attributions of symbolicmeaning following direct experience to reduce psychologicaldistancerdquo Transportation Research Part D Transport andEnvironment vol 16 no 7 pp 525ndash531 2011

[110] Y Liu Y Yang Y Liu and G H Tzeng ldquoImproving sus-tainable mobile health care promotion a novel hybridMCDM methodrdquo Sustainability vol 11 no 3 2019

[111] F Del Pero M Delogu and M Pierini ldquoe effect oflightweighting in automotive LCA perspective estimation ofmass-induced fuel consumption reduction for gasolineturbocharged vehiclesrdquo Journal of Cleaner Productionvol 154 pp 566ndash577 2017

[112] D Diakoulaki G Mavrotas and L Papayannakis ldquoDeter-mining objective weights in multiple criteria problems thecritic methodrdquo Computers amp Operations Research vol 22no 7 pp 763ndash770 1995

[113] Q Qiao F Zhao H Hao S Jiang and Z Liu ldquoCradle-to-gate greenhouse gas emissions of battery electric and internalcombustion engine vehicles in Chinardquo Applied Energyvol 204 2017

[114] I A Nienhueser and Y Qiu ldquoEconomic and environmentalimpacts of providing renewable energy for electric vehiclechargingmdasha choice experiment studyrdquo Applied Energyvol 180 pp 256ndash268 2016

Mathematical Problems in Engineering 23

economiesrdquo International Journal of Environmental Re-search and Public Health vol 17 no 10 2020

[108] T Lieven S Muhlmeier S Henkel and J F Waller ldquoWhowill buy electric cars An empirical study in GermanyrdquoTransportation Research Part D Transport and Environmentvol 16 no 3 pp 236ndash243 2011

[109] S Skippon and M Garwood ldquoResponses to battery electricvehicles UK consumer attitudes and attributions of symbolicmeaning following direct experience to reduce psychologicaldistancerdquo Transportation Research Part D Transport andEnvironment vol 16 no 7 pp 525ndash531 2011

[110] Y Liu Y Yang Y Liu and G H Tzeng ldquoImproving sus-tainable mobile health care promotion a novel hybridMCDM methodrdquo Sustainability vol 11 no 3 2019

[111] F Del Pero M Delogu and M Pierini ldquoe effect oflightweighting in automotive LCA perspective estimation ofmass-induced fuel consumption reduction for gasolineturbocharged vehiclesrdquo Journal of Cleaner Productionvol 154 pp 566ndash577 2017

[112] D Diakoulaki G Mavrotas and L Papayannakis ldquoDeter-mining objective weights in multiple criteria problems thecritic methodrdquo Computers amp Operations Research vol 22no 7 pp 763ndash770 1995

[113] Q Qiao F Zhao H Hao S Jiang and Z Liu ldquoCradle-to-gate greenhouse gas emissions of battery electric and internalcombustion engine vehicles in Chinardquo Applied Energyvol 204 2017

[114] I A Nienhueser and Y Qiu ldquoEconomic and environmentalimpacts of providing renewable energy for electric vehiclechargingmdasha choice experiment studyrdquo Applied Energyvol 180 pp 256ndash268 2016

Mathematical Problems in Engineering 23