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1

Appendices

Appendix A: Entity types and the reference variables

In an entity-based modeling, while all entity types have their own dynamics, they can also use variables of the other entities. These variables indeed connect

the entity types to each other and integrate the disaggregated system. In Ventity software, these variables are called ‘reference variables’. In the following

table, you can find the main references of each entity type.

Entity Type

Related entities and variables

1

EV purchasers

E-mobility innovation system. EV knowledge development and diffusion1

Charging point. Facilitating conditions

EV pricing Collection. Average Price value of EV2

2

Charging points

EV purchasers Collection. Sum EV adopters

E-mobility innovation system. Entrepreneurial activities

3

E-mobility

innovation system

EV purchasers Collection. Sum EV adopters

EV related subsidies. Total subsidies for EVs

4

EV related subsidies

EV purchasers Collection. Sum EV adopters

Charging points. Installed charging points

5

EV pricing

EV purchasers Collection. Sum EV adoption rate

1

This phrase indicates that ‘EV purchasers’ entity type utilizes the variable ‘EV knowledge development and diffusion’ from the ‘E-mobility innovation system’ entity type.

2

This phrase indicates that ‘EV purchasers’ entity type uses the average value of variable ‘Price value of EV’ from the ‘EV pricing’ entity type. In Ventity software, aggregate

values of the variables (e.g., Min, Max, MEAN, MEDIAN, STDEV, and SUM) in each entity type can be accessed through the related collections.

2

Appendix B: The overall variables and equations of the model

We used Ventity software to build the model. For more information on the functions used in the model, see the following link: http://ventity.biz/

For complementary information about the variables and the equations of each entity type, see the related section in the paper.

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Entity Type: Model

Final Time

Auxiliary

360

Month

We ran the model for 30 years.

Initial Time

Auxiliary

0

Month

2016 is the earliest time that we have relevant

information on the e-mobility development for the

Netherlands.

Time

Auxiliary

1

Month

Time Step

Auxiliary

0.0625

Month

Entity Type: EV Related Subsidies

Cars per adopter

Auxiliary

1

Car/Person

We assume that every person buys only one car.

Sold cars

Auxiliary

Cars per adopter*EV purchasers

Collection.Sum EV adopters

Car

Subsidy per charging point

Auxiliary

1000

Euro/Charging

Point

The cost of a public EV charging station can vary

significantly based on the requirements and existing

electrical infrastructure, but we considered an

average of 1,000 Euros.

Subsidy per EV

Auxiliary

TableFunction1(Sold cars)

Euro/Car

TableFunction1

Table

Function

([0,8200000],[0,5000]) [(0,5000),

(412020.95,4983.56), (681064.48,4886.88),

(1065412.37,4790.2), (1257586.32,4693.51),

(1500000,4500), (2000000,4000),

(2466006.12,3412.45), (2794066.7,2784.01),

(3273539.85,1913.85), (3853954.72,1357.92),

(4409134.15,971.19), (4989549.02,560.28),

(5771847.32,318.57), (6579381.04,173.55),

(8200000,0)]

Car,Euro/Car

As we mentioned in the paper, we assume that the

government offers a changeable direct subsidy to the

purchasers (5000 to 0 Euros as EV adoption

increases over time). As such, we assume that after a

period, direct subsidy of the government will

decrease till approach zero. This is because e-

mobility will be more affordable for the purchasers

and as such there is no need to pay subsidy.

Total subsidies for the

purchase of EVs

Auxiliary

Sold cars*Subsidy per EV

Euro

3

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Total subsidies for

charging points

Auxiliary

Subsidy per charging point *Charging

point.Installed charging points

Euro

Total subsidies for EVs

Auxiliary

Total subsidies for the purchase of EVs +

Total subsidies for charging points

Euro

Entity Type: EV Pricing

Cost reduction per

doubling of Experience

Auxiliary

0.3

Dmnl

(Dimensionless)

Heuristics (see e.g., Argote, 1996; 2013; Morrison,

2008)

Initial Experience

Auxiliary

90275

Car

This is the same as the number of EVs at the initial

time (RVO, 2017)

Effect of EVA on L

Auxiliary

1

Car/Person

Effect of EV adoption on learning

Effect of experience on

price

Auxiliary

(Cumulative experience/Initial

Experience)^(Strengths of learning curve)

Dmnl

See Sterman (2000: 338)

Expected annual growth of

EV price

Auxiliary

(- 0.005)/12

Dmnl

We assume that EV price decreases by a steady rate

of (0.005) every year (rule of thumb).

Expected annual growth of

ICEV price

Auxiliary

0.01/12

Dmnl

We assume that ICEV price increases by a steady

rate of (0.01) every year (rule of thumb).

Expected annual growth of

Tax on ICEV

Auxiliary

0.2/12

Dmnl

We assume that Tax on ICEV increases by a steady

rate of (0.02) every year (rule of thumb).

Initial EV price

Auxiliary

30000

Euro/Car

We assume that EV price is 10,000 Euros more

expensive than ICEVs in the same model (see e.g.,

Axsen et al., 2016).

Initial ICEV price

Auxiliary

20000

Euro/Car

Initial tax on ICEV

Auxiliary

75

Euro/Car

We assume that initial average tax for ICEV is 75

Euros

Price of EV

Auxiliary

Initial EV price * ((Effect of experience on

price) + ((1+ Expected annual growth of EV

price)^ Model.Time)) - EV related

subsidies.Subsidy per EV

Euro/Car

See the ‘EV pricing entity type’ in the paper.

Price of ICEV

Auxiliary

(Initial ICEV price * ((1 + Expected annual

growth of ICEV price)^Model.Time))+ Tax

on ICEV

Euro / Car

See the ‘EV pricing entity type’ in the paper

Price value of EV

Auxiliary

Price of ICEV/Price of EV

Dmnl

Strengths of learning curve

Auxiliary

log(1 - cost reduction per doubling of

experience)/log(2)

Dmnl

See Sterman (2000: 338)

Tax on ICEV

Auxiliary

Initial tax on ICEV *(1 +Expected annual

growth of Tax on ICEV)^Model.Time

Euro/Car

See the ‘EV pricing entity type’ in the paper

4

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Time to forget

Auxiliary

5*12

Month

Heuristics (see e.g., Argote, 1996; 2013; Morrison,

2008)

Cumulative experience

Stock

INTEG (Learning – Forgetting)

[Default initial value = Initial Experience]

Car

See Sterman (2000: 338)

Learning

Flow

EV purchasers Collection.Sum EV adoption

rate*Effect of EVA on L

Car/Month

See Sterman (2000: 338)

Forgetting

Flow

Cumulative experience/Time to forget

Car/Month

See Morrison (2008)

EV Pricing[]

Average Price value of EV

Aggregate

Average

Dmnl

This variable calculates the average prices of EV

Entity Type: EV Purchasers

Adoption fraction

Auxiliary

RandomUniform(0.0001,0.0005)

Dmnl

This variable denotes the proportion of contacts that

are sufficiently persuasive (if positive WoM) or

incredible (if negative WoM) to induce the potential

adopter to purchase/or not purchase an EV. In this

model we assume that positive and negative WOMs

have the same strength (see e.g., Berger and

Milkman, 2012; East et al., 2008; Fiedler, 2007;

Mizerski, 1982; Park and Lee, 2009).

Using RandomUniform we specify a min (0.0001)

and a max (0.0005) for different contacts that might

be happened within the population.

Adoption from WoM

Auxiliary

Contact rate * Adoption fraction * ((EV

purchasers Collection.Sum EV adopters)-(EV

purchasers Collection.Sum dissatisfied

adopters))*(EV purchasers Collection.Sum

Potential adopters of EV/Initial potential

adopters)

Person/Month

Adoption from Word of Mouth: as we argued in

chapter 4, we formulate both negative and positive

WoM as the recommendations made, subsequently,

by (dis)satisfied EV adopters to the prospective EV

purchasers (see e.g., Berger and Milkman, 2012; East

et al., 2008; Fiedler, 2007; Mizerski, 1982; Park and

Lee, 2009).

Advertising effectiveness

Auxiliary

0.01

Dmnl

This constant represents how successful the

advertising of EVs is in communicating the message

– whether potential EV adopters saw or liked it, or be

motivated to think of EV adoption (rule of thumb).

AT delivering EV

Auxiliary

5

Month

The average time required for delivering an EV to a

purchaser (rule of thumb).

5

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

AT DoP

Auxiliary

2*12

Month

Determines how rapidly ‘Desirability of EVs price

for purchasers’ will be perceived (rule of thumb).

AT EV purchase intention

Auxiliary

5

Month

Adjustment time to decide (intent) an EV adoption

(rule of thumb).

AT EVP

Auxiliary

12

Month

The average time required for manufacturers to

produce EV based on the demand (rule of thumb).

AT FCE

Auxiliary

4*12

Month

Determines how rapidly available facilitating

conditions are perceived by potential purchasers (rule

of thumb).

AT HE

Auxiliary

2*12

Month

The adjustment time determines how rapidly

available hedonic benefits of EVs are perceived by

potential purchasers (rule of thumb).

AT KH

Auxiliary

5*12

Month

The average time required for ‘EV knowledge

development and diffusion’ to convert into ‘Applied

knowledge’ (rule of thumb).

AT PE

Auxiliary

2*12

Month

Determines how rapidly ‘performance of EVs’ and

‘degree of ease associated with EV adoption’ are

perceived by potential purchasers (rule of thumb).

Average EV performance

Auxiliary

Effect of EV KD and Dif on

performance*Applied knowledge

Dmnl

Average lifetime of EV

Auxiliary

8*12

Month

See e.g., Struben and Sterman (2008)

Car demands

Auxiliary

EV purchasers Collection.Sum Intent to buy

EV *Cars per adopter

Car

Cars per adopter

Auxiliary

1

Car/Person

We assume that every person buys only one car

Contact rate

Auxiliary

randomuniform(0,15)

1/Month

The monthly rate, with which potential adopters

come into contact with adopters.

Using RandomUniform we specify a min (0) and a

max (15) as a monthly rate, with which various

potential adopters come into contact with adopters.

Effect of AEVs P on HM

Auxiliary

0.75

Dmnl

We assume that 0.75 of hedonic benefits can be

conveyed to the purchasers (rule of thumb).

Effect of AEVS P on PEE

Auxiliary

0.75

Dmnl

We assume that 0.75 of performance can be

conveyed to the purchasers (rule of thumb).

6

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Effect of DoP on IRPE

Auxiliary

0.3

Dmnl

For the base run, we assume that potential purchasers

consider (0.3) as weight of importance for the

‘Desirability of EVs price’ on ‘Intention rate to

purchase EV’ (rule of thumb).

Effect of EV KD and Dif

on H benefits

Auxiliary

0.25

Dmnl

We assume that 0.25 of applied EV knowledge are

allocated to hedonic benefits (rule of thumb).

Effect of EV KD and Dif

on performance

Auxiliary

0.75

Dmnl

We assume that 0.75 of applied EV knowledge are

dedicated to performance of EVs (rule of thumb).

Effect of FC on IRPE

Auxiliary

0.3

Dmnl

For the base run, we assume that potential purchasers

consider (0.3) as weight of importance for the

‘Facilitating condition expectancy’ on ‘Intention rate

to purchase EV’ (rule of thumb).

effect of HE on IRPE

Auxiliary

0.1

Dmnl

For the base run, we assume that potential purchasers

consider (0.1) as weight of importance for their

‘Hedonic expectancy’ on ‘Intention rate to purchase

EV’ (rule of thumb).

Effect of PEE on IRPE

Auxiliary

0.3

Dmnl

For the base run, we assume that potential purchasers

consider (0.3) as weight of importance for their

‘Performance and effort expectancy’ on ‘Intention

rate to purchase EV’(rule of thumb).

EV stock shortfall

Auxiliary

Car demands-EVs in stock

Car

First time intended

Auxiliary

0

Month

In the base run, we assume that whole population

think of EV as a car that they can adopt.

Hedonic benefits of EV

Auxiliary

Applied knowledge*Effect of EV KD and Dif

on H benefits

Dmnl

Ignoring rate

Auxiliary

0.001

1/Month

We assume that every month, (0.01%) of EV

adopters stop EV adoption. This might be happened

because of their unpleasant experience of EV

adoption (rule of thumb).

Initial potential adopters

Auxiliary

8200000

Person

Intention rate to purchase

EV

Auxiliary

((Effect of FC on IRPE *Facilitating condition

expectancy) + (Effect of PEE on

IRPE*Performance and effort expectancy) +

(effect of DoP on IRPE* Desirability of EVs

price for purchasers) + (effect of HE on IRPE

* Hedonic expectancy))

Dmnl

See the ‘EV purchasers entity type’ in the paper.

7

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Percentages of the total

population

Auxiliary

1

Dmnl

For the base run, we assume that the whole initial

population are potential adopters. As such, we set

this variable as 1. However, in the scenario analysis,

we assume that different classes of potential EV

adopters constitutes a subset of whole population.

The effect of EA on EV

production

Auxiliary

0.35

Dmnl

We assume that 35% of entrepreneurial activities will

be dedicated to EV production (rule of thumb).

Time to become neutral

Auxiliary

4*12

Month

The average time that dissatisfied EV adopters might

become neutral about their unpleasant experience of

EV adoption (rule of thumb).

Applied knowledge

Stock

INTEG(Knowledge application rate)

[Default initial value = 0]

Dmnl

Knowledge that can be applied in car industry

(translated in terms of performance and hedonic

benefits)

Knowledge application

rate

Flow

Conversion rate *(Emobility Innovation

System.EV knowledge development and

diffusion - Applied knowledge) / AT KH

Dmnl/Month

The rate that (theoretical) knowledge will be applied

in car industry (translated in terms of performance

and hedonic benefits)

Desirability of EVs price

for purchasers

Stock

INTEG(Change in DPP)

[Default initial value = 0]

Dmnl

This formulation returns a number between (0, 1)

Change in DPP

Flow

(1-Desirability of EVs price for purchasers)*

EVpricing.Average Price value of EV/ AT

DoP

Dmnl/Month

Change in Desirability of EVs price for purchasers

Dissatisfied adopters

Stock

INTEG(Stop EV adoption rate- Becoming

neutral)

[Default initial value = 0]

Person

Stop EV adoption rate

Flow

EV adopters * Ignoring rate

Person/Month

Becoming neutral

Flow

Dissatisfied adopters/Time to become neutral

Person/Month

EV adopters

Stock

INTEG(EV adoption rate - Attrition rate -

Stop EV adoption rate)

[Default initial value = 90275]

Person

(RVO, 2017)

EV adoption rate

Flow

Min(Intent to buy EV,EVs in stock)/AT

delivering EV

Person / Month

Attrition rate

Flow

EV adopters/Average lifetime of EV

Person / Month

Stop EV adoption rate

Flow

EV adopters * Ignoring rate

Person/Month

EVs in stock

Stock

INTEG(EV production rate)

[Default initial value = 0]

Car

8

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

EV production rate

Flow

(Emobility Innovation System.Entrepreneurial

activities) * the Effect of EA on EV

production*EV stock shortfall/AT EVP

Car/Month

Facilitating condition

expectancy

Stock

INTEG(Change in FCE)

[Default initial value = 0]

Dmnl

This formulation returns a number between (0, 1)

Change in FCE

Flow

(1+ Advertising effectiveness) *(Charging

point.Facilitating conditions)* (1-Facilitating

condition expectancy)/AT FCE

Dmnl/Month

Change in ‘Facilitating condition expectancy’

Hedonic expectancy

Stock

INTEG(change in HE)

[Default initial value = 0]

Dmnl

This formulation returns a number between (0, 1)

Change in HE

Flow

(1+ Advertising effectiveness)*(Effect of

AEVS P on HM * Average EV

performance)*(Hedonic benefits of EV)*(1-

Hedonic expectancy)/ AT HE

Dmnl/Month

Change in ‘Hedonic motivation’

Intent to buy EV

Stock

INTEG(EV purchase intention rate - EV

adoption rate)

[Default initial value = 0]

Person

EV purchase intention rate

Flow

if (model.time> First time

intended){((Potential adopters of

EV*Intention rate to purchase EV)/AT EV

purchase intention) + (Adoption from WoM)}

else {0}

Person / Month

EV adoption rate

Flow

Min(Intent to buy EV,EVs in stock)/AT

delivering EV

Person / Month

Performance and effort

expectancy

Stock

INTEG(change in PEE)

[Default initial value = 0]

Dmnl

This formulation returns a number between (0, 1)

Change in PEE

Flow

(1+ Advertising effectiveness) * (Effect of

AEVS P on PEE *Average EV performance)*

(1 - Performance and effort expectancy)/ AT

PE

Dmnl/Month

Change in ‘Performance and effort expectancy’

Potential adopters of EV

Stock

INTEG(Becoming neutral - EV purchase

intention rate)

[Default initial value = Initial potential

adopters*Percentages of the total population]

Person

Becoming neutral

Flow

Dissatisfied adopters/Time to become neutral

Person/Month

EV Purchasers[]

9

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Sum Dissatisfied adopters

Aggregate

Sum

Person

Calculates sum of Dissatisfied adopters belong to

different classes of purchasers

Sum EV adopters

Aggregate

Sum

Person

Calculates sum of EV adopters belong to different

classes of purchasers

Sum EV adoption rate

Aggregate

Sum

Person / Month

Calculates sum of EV adoption rate for different

classes of purchasers

Sum Intent to buy EV

Aggregate

Sum

Person

Calculates sum of potential adopters who intends to

buy EV in different classes of purchasers

Sum Potential adopters of

EV

Aggregate

Sum

Person

Calculates sum of Potential adopters of EV in

different classes of purchasers

Entity Type: Emobility Innovation System

AT EDI

Auxiliary

7*12

Month

Time that Entrepreneurs might be dissuade to

continue in e-mobility system (rule of thumb).

AT EI

Auxiliary

3*12

Month

Time that Entrepreneurs will be interested in e-

mobility after receiving signals from ‘Guidance of

the Search’ and perceiving more legitimacy of EVs

(rule of thumb).

AT EKD

Auxiliary

3*12

Month

The time required to convert ‘resource mobilization’

to ‘EV knowledge development and diffusion’(rule

of thumb).

AT PLEM

Auxiliary

5*12

Month

This variable represents how quickly the determined

cause variables leads to ‘Perceived legitimacy of e-

mobility’(rule of thumb).

AT RM

Auxiliary

2*12

Month

This variable represents how quickly ‘Guidance of

the Search’ leads to ‘Resource mobilization’(rule of

thumb).

AT SSE

Auxiliary

1

Month

Time required for sailing ship effect to adjust

TableFunction2

Table

Function

([0,8200000], [0,1]), [(0,0), (825703.24, 0.03),

(1254705.53, 0.05), (1835120.4, 0.08),

(2995950.13, 0.15), (4131544.43, 0.24) ,

(5368080.45, 0.39), (6478439.33, 0.57),

(7538327.34, 0.83), (8200000,1)]

Person,Dmnl

We assume an exponential growth for this table

function.

Beliefs in EVs growth

potential

Auxiliary

(TableFunction2(EV purchasers

Collection.Sum EV adopters)+Perceived

legitimacy of emobility)/2

Dmnl

10

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Effect of experience on

Knowledge dev and dif

Auxiliary

(EV pricing.Cumulative experience/EV

pricing.Initial Experience)^(Strength of

learning curve for knowledge)

Dmnl

See Sterman (2000: 338)

Effect size of SSE on

legitimacy

Auxiliary

0.3

Dmnl

Effect size of Sailing ship effect on the legitimacy of

e-mobility (based on rule of thumb; see e.g.,

Rosenberg, 1976; Utterback, 1996; Walrave and

Raven, 2016).

TableFunction5

Table

Function

([0,8200000], [0, 1]), [(0, 0), (1002351.24,

0.03), (1910826.69, 0.06), (2743595.84, 0.11),

(3677306.71, 0.19), (4081073.58, 0.33),

(4207250.72, 0.46), (4510075.87, 0.61),

(4913842.73, 0.76), (5544728.46, 0.84),

(6402733.04, 0.9), (7210266.77, 0.96),

(8169213.07,1)]

Person,Dmnl

We assume an S-shaped growth for this table

function.

EVs effect on legitimacy

Auxiliary

TableFunction5(EV purchasers

Collection.Sum EV adopters)

Dmnl

Guidance of the search

Auxiliary

(Beliefs in EVs growth potential + EV

knowledge development and diffusion)/2

Dmnl

This formulation returns a number between (0, 1)

Knowledge increase per

doubling experience

Auxiliary

0.3

Dmnl

Heuristics (see e.g., Argote, 1996; 2013; Morrison,

2008)

Knowledge decay time

Auxiliary

9*12

Month

The time that ‘EV knowledge development and

diffusion’ is decayed or forgotten (Argote, 1996;

2013; Morrison, 2008).

Sailing ship effect

Auxiliary

Effect size of SSE on legitimacy*SSE

triggers*SSE limit

Dmnl

See the section on ‘e-mobility innovation system’

entity type in the paper.

SSE limit

Auxiliary

IfThenElse(SSE counter<SSE limit threshold,

1, 0)

Dmnl

Binary variable (true/false) that indicates if the SSE

is active (or not).

SSE limit threshold

Auxiliary

7*12

Car/Month

Sailing ship effect limit threshold: Time that sailing

ship will minimally remain active (see e.g.,

Rosenberg, 1976; Utterback, 1996; Walrave and

Raven, 2016).

Strength of learning curve

for knowledge

Auxiliary

log(1 + Knowledge increase per doubling

experience)/log(2)

Dmnl

See Sterman (2000: 338)

11

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Threshold percentages for

SSE

Auxiliary

0.15

Dmnl

We assume that if (15%) of potential purchasers

adopt EV, then a ‘sailing ship effect’ will be

triggered (see e.g., Rosenberg, 1976; Utterback,

1996; Walrave and Raven, 2016).

Threshold EVs adopters

Auxiliary

Total potential adopters* Threshold

percentages for SSE

Person

Total potential adopters

Auxiliary

8200000

Person

TableFunction1

Table

Function

([0,36576000000], [0,1]), [(0,0),

(2669978333.71, 0.15) , (6609659641.86,

0.32) ,(10549340950, 0.49) ,

(16177457104.49, 0.67), (21918135582.08,

0.79), (27996501028.93, 0.91),

(36576000000, 1)]

Euro,Dmnl

We considered a goal seeking behavior for this table

function. Accordingly, based on rule of thumb we

assume that the effect of subsidies on ‘resource

mobilization’ function will be decreased as e-

mobility develop.

Subsidies effect on

mobilization

Auxiliary

TableFunction1(EV related subsidies.Total

subsidies for EVs)

Dmnl

Entrepreneurial activities

Stock

INTEG(Entrepreneurial interest -

Entrepreneurial disinterest)

[Default initial value = 0.06]

Dmnl

We estimate the initial value considering the current

development of e-mobility in The Netherlands (to

estimate the values, we use, more specifically, the

recent reports on EV via the following link:

www.ieahev.org).

Entrepreneurial interest

Flow

(1 - Entrepreneurial activities) * (Guidance of

the search) * (Perceived legitimacy of

emobility) / AT EI

1/Month

Entrepreneurial disinterest

Flow

Entrepreneurial activities / AT EDI

Dmnl/Month

EV knowledge

development and diffusion

Stock

INTEG(Knowledge dev and dif rate -

Knowledge dev and dif decay rate)

[Default initial value = 0.004]

Dmnl

This formulation returns a number between (0, 1); we

estimate the initial value considering the current

development of e-mobility in The Netherlands

Knowledge dev and dif

rate

Flow

(1- EV knowledge development and diffusion)

*(Effect of experience on Knowledge dev and

dif)*(Resource mobilization)/ AT EKD

Dmnl/Month

Knowledge development and diffusion rate

Knowledge dev and dif

decay rate

Flow

EV knowledge development and

diffusion/Knowledge decay time

Dmnl/Month

Knowledge development and diffusion decay rate

12

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Perceived legitimacy of

emobility

Stock

INTEG(Changes in PL of EM)

[Default initial value = 0.03]

Dmnl

We estimate the initial value considering the current

development of e-mobility in The Netherlands (to

estimate the values, we use, more specifically, the

recent reports on EV via the following link:

www.ieahev.org).

Changes in PL of EM

Flow

(1-Perceived legitimacy of emobility)* ((EV

knowledge development and diffusion)+ (EVs

effect on legitimacy) - (Sailing ship

effect))/AT PLEM

Dmnl/Month

Changes in ‘Perceived legitimacy of e-mobility’

Resource mobilization

Stock

INTEG(R mobilization rate)

[Default initial value = 0.001]

Dmnl

This formulation returns a number between (0, 1); we

estimate the initial value considering the current

development of e-mobility in The Netherlands.

R mobilization rate

Flow

(1-Resource mobilization)*(guidance of the

search) *(Subsidies effect on

mobilization)/AT RM

1/Month

Resource mobilization rate (see e.g., Rosenberg,

1976; Utterback, 1996; Walrave and Raven, 2016).

SSE counter

Stock

INTEG(SSE counter rate)

[Default initial value = 0]

Dmnl

Sailing Ship Effect counter (see e.g., Rosenberg,

1976; Utterback, 1996; Walrave and Raven, 2016).

SSE counter rate

Flow

SSE triggers/AT SSE

Dmnl/Month

Sailing Ship Effect counter rate (see e.g., Rosenberg,

1976; Utterback, 1996; Walrave and Raven, 2016).

SSE triggers

Stock

INTEG(SSE trigger rate)

[Default initial value = 0]

Dmnl

Sailing Ship Effect triggers (see e.g., Rosenberg,

1976; Utterback, 1996; Walrave and Raven, 2016).

SSE trigger rate

Flow

IfThenElse(EV purchasers Collection.Sum EV

adopters>threshold EVs adopters && SSE

triggers=0, 1/Model.Time Step,0)

1/Month

Sailing Ship Effect trigger rate (see e.g., Rosenberg,

1976; Utterback, 1996; Walrave and Raven, 2016).

Entity Type: Charging Point

Actual capacity of

charging points

Auxiliary

EV purchasers Collection.Sum EV

adopters/Installed charging points

Person/

Charging Point

AT CPP

Auxiliary

1.5

Month

We assume that average time required to produce a

public charging point after charging points shortfall

(rule of thumb).

AT ICP

Auxiliary

1.5

Month

We assume that average delay to install a public

charging point upon the request (rule of thumb).

Average lifetime of CP

Auxiliary

15*12

Month

We assume that average lifetime of charging points is

15 years (see e.g., Weider and Philip, 2010).

Charging points shortfall

Auxiliary

Planned capacity of CP -Installed charging

points

Charging Point

13

Element

(Variable)

Element

Type

Expression (Equation)

Units

Note

Effect of EA on CPP

Auxiliary

0.35

Dmnl

Based on rule of thumb, we assume that 35% of

entrepreneurial activities will be dedicated to

installing charging points.

Facilitating conditions

Auxiliary

Planned relation between EV and CP/Actual

capacity of charging points

Dmnl

Planned capacity of CP

Auxiliary

EV purchasers Collection.Sum EV

adopters/Planned relation between EV and CP

Charging Point

Planned capacity of charging points

Planned relation between

EV and CP

Auxiliary

TableFunction1(EV purchasers

Collection.Sum EV adopters)

Person

/Charging Point

Planned relation between EV and charging point

TableFunction1

Table

Function

([0,8200000], [1,4]), [(0,4), (548113.52,3.51),

(1078057.53,3.03), (1633236.97,2.61),

(2264122.69,2.18), (3122127.28,1.8),

(4131544.43,1.5), (5166197.02,1.23),

(6402733.04,1.1), (8194448.5,1)]

Person,Person /

Charging Point

We posit that the desired relation between the

number of EVs and charging points will be decreased

from four to one as the number of ‘EV adopters’

increases (see e.g., Shulock et al., 2011).

CP in stock

Stock

INTEG(CP production rate- Net installing CP)

[Default initial value = 0]

Charging Point

Charging points in stock

CP production rate

Flow

Charging points shortfall*Effect of EA on

CPP*Emobility Innovation

System.Entrepreneurial activities/AT CPP

Charging

Point/Month

Charging point production rate

Net installing CP

Flow

CP on stock/AT ICP

Charging Point/

Month

Net installing charging point

Installed charging points

Stock

INTEG(Net installing CP- Scrap rate of CP)

[Default initial value = 17786]

Charging Point

(RVO, 2017)

Net installing CP

Flow

CP on stock/AT ICP

Charging Point/

Month

Net installing charging point

Scrap rate of CP

Flow

Installed charging points/Average lifetime of

CP

Charging

Point/Month

Scrap rate of charging point

14

Appendix C: The stylized overview of the entity types

Figure C.1. Feedback loops in E-Mobility Innovation System entity type.

15

Figure C.2. Feedback loops in Charging Point entity type.

16

Figure C.3. Feedback loops in EV Pricing entity type.

Figure C.4. Feedback loops in EV Related Subsidies entity type.

17

Figure C.5. Feedback loops in EV Purchasers entity type.

18

Appendix D: Initialization of the UTAUT variabless for different scenarios

Following table provides more details about the scenarios which are illustrated in ‘experimental

setup’. The assumed weights of UTAUT variables, initial values of EV adopters, and the first time that

each class of EV purchasers intended to buy EV follow from the logic described for each scenario. The

percentage of the total population for each class of EV purchasers are derived from Maltha et al.

(2017) and Bockarjova and Steg (2014).

Scenario

Class of EV

purchasers

Definition

Initial value

of EV

adopters

Percentage

of the total

population

First time

intended to

buy EV

Performance

& effort

weight

Hedonic

weight

Price

weight

Facilitating

conditions

weight

Scenario 2

Aggregated purchasers

90275

1

0

0,275

0,025

0,4

0,3

Scenario 2-1

Income1

0-10,000 Euros

0

0,024

264

0,29

0,05

0,35

0,31

Income2

10,000-20,000 Euros

0

0,12

216

0,3

0,075

0,3

0,325

Income3

20,000-30,000 Euros

0

0,227

124

0,315

0,1

0,25

0,335

Income4

30,000-40,000 Euros

0

0,214

96

0,4

0,15

0,1

0,35

Income5

40,000-50,000 Euros

0

0,167

36

0,6

0,2

0

0,2

Income6

50,000+ Euros

90275

0,248

0

0,275

0,025

0,4

0,3

Scenario 2-2

Age1

18-19

22569

0,003

0

0,25

0,2

0,25

0,3

Age2

20-29

22569

0,073

0

0,3

0,175

0,25

0,275

Age3

30-39

22569

0,14

0

0,35

0,15

0,25

0,25

Age4

40-49

22569

0,199

0

0,4

0,125

0,25

0,225

Age5

50-64

0

0,315

124

0,45

0,1

0,25

0,2

Age6

65-74

0

0,17

216

0,5

0,075

0,25

0,175

Age 7

75+

0

0,101

264

0,55

0,05

0,25

0,15

Scenario 2-3

Urbanization1

Very high density

60183

0,152

0

0,2

0,03

0,57

0,2

Urbanization2

High density

30091

0,246

0

0,225

0,05

0,5

0,225

Urbanization3

Moderately high density

0

0,2

96

0,28

0,07

0,4

0,25

Urbanization4

Low density

0

0,248

124

0,3

0,1

0,3

0,3

Urbanization5

Very low density

0

0,155

216

0,33

0,12

0,2

0,35

19

Appendix E: Credibility and quality of the model

Testing SD models is a continuous process that is essentially starts from the early stages of modeling

(Barlas and Carpenter, 1990). In this regard, a wide variety of procedures has been conducted to

uncover model flaws and assure sufficient confidence in the structure

3

and associated behavior

4

(e.g.,

Sterman, 2000; Barlas, 1996; Coyle and Exelby, 2000).

Structural validation was performed through unit checks, structural verification and extreme

condition analysis.

• In this regard, first the unit names of the variables have been checked in order to see whether

any variable is missing in the equations, and to assure dimensional consistency in the

equations (see the unit names of the variables in Appendix B). Accordingly, we made a few

revisions in the structure of the model to correct the unit errors.

• Structural verification addresses whether the model structure is consistent with the

descriptive knowledge about the real system. In this paper, the constructs used in the all

entity types are drawn from the theoretical frameworks and sensible empirical insights.

Therefore, the model can be considered, structurally, verified (see section 2 and section 3.1 of

the paper).

• Extreme condition analysis evaluates whether the parameters in the model behave

appropriately under extreme conditions. In this regard, we assessed whether any parameter

takes on values outside the prescribed limits. We also examined the response of the

endogenous variable to changes in the inputs. Accordingly, behaviors that do not make sense

in the extreme points lead to more introspective evaluation of the equations. For instance, we

used the following equation for EV adoption rate:

EV adoption rate= Min(Intent to buy EV, EVs in stock / Cars per adopters ) / AT delivering EV

As such, the model is also robust in case of no EVs in stock, because no EV will be also

adopted accordingly.

Furthermore, in case of no population (no potential adopters), no EV adoption will be

occurred. In addition, in case of no car demand, there will not be any EV in stock, and EV

adopters, and subsequently installed charging points, will be approached to zero eventually

(see Figure E.1). Overall, the model passed this analysis.

3

A structural validation seeks to determine if the model reflects the real world accurately.

4

Behavioral assessment focuses on the model behavior during execution, and assesses the degree of confidence

that can be placed in the results

20

Figure E.1. The outputs of extreme conditions test in case of no (new) car demand.

0

100000

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EV adopters (Persons)

Time (Months)

00.0625 0.125 0.1875 0.25 0.3125

0.375 0.4375 0.5 0.5625 0.625 0.6875

0.75 0.8125 0.875 0.9375 11.0625

1.125 1.1875 1.25 1.3125 1.375 1.4375

1.5 1.5625 1.625 1.6875 1.75 1.8125

1.875 1.9375 22.0625 2.125 2.1875

2.25 2.3125 2.375 2.4375 2.5 2.5625

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

360

Charging Points

Time (Months)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EVs in Stock (Cars)

Time (Months)

21

Behavioral assessment requires running the entire model and examination of the results.

• First, parameters are varied systematically to assess whether the entire model is functioning

as expected. In addition, the behavior of individual variables is followed over time, and

analyzed for any aberrant behavior. Accordingly, a closer examination of the unexpected

behaviors have been conducted, and required recalibration and restructuring of the model

have been done.

• We, also, halve the time step (0.0625), and make sure that the model behavior does not

change, accordingly.

• To further validate the model settings, a sensitivity analysis is also conducted. In this respect,

we investigated whether model output changes substantially when the assumptions

(parameter values) are varied over a plausible range of uncertainty (Sterman, 2000). More

specifically, we concentrated on ‘The effect of EA on EV production’, ‘Effect of EA on CPP’,

‘Cost reduction per doubling of experience’, ‘Effect size of SSE on legitimacy’. As such, these

parameters are varied between (-/+ 5%) while the other variables are kept constant. The

results illustrates that the number of EV adopters will not be changed (significantly)

comparing to the ‘base case (i.e., Scenario 2)’, within the specified ranges of these variables

(see Figures E.2 to E.6). We also consider the simultaneous variation of these parameters

between these ranges. However, the number of EV adopters does not change significantly

(see Figure E.7). Therefore, the model can be considered robust.

• Hence, we also formulated the variables of ‘adoption fraction’ and ‘contact rate’ based on a

random function. This function made it possible to consider various random numbers within

a numerical range. However, these results also show that the outputs do not change

meaningfully within the specified ranges.

adoption fraction= RandomUniform (0.0001,0.0005);

Contact rate = randomuniform(0,15)

All in all, we can conclude that the overall model settings, the structure and the corresponding

behaviors are credible in line with the research problem and the specified purpose of modeling and

simulation.

22

Figure E.2. Sensitivity analysis of ‘EV adopters’ based on (-/+ 5%) changes in the value of ‘The effect of

EA on EV production’ that is 0.35; this value has been varied between [0.3325, 0.3675].

Figure E.3. Sensitivity analysis of ‘EV adopters’ based on (-/+ 5%) changes in the value of ‘Effect of EA

on CPP’ that is 0.35; this value has been varied between [0.3325, 0.3675].

0

1,0 M

2,0 M

3,0 M

4,0 M

5,0 M

6,0 M

7,0 M

8,0 M

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EV Adopters (Persons)

Time (Months)

EV adopters in case of 'The effect of EA on EV production=0.3325'

EV adopters in case of 'The effect of EA on EV production=0.3675'

EV adopters in case of 'The effect of EA on EV production=0.35'

0

1M

2M

3M

4M

5M

6M

7M

8M

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EV Adopters (Persons)

Time (Months)

EV adopters in case of 'Effect of EA on CPP=0.3325'

EV adopters in case of 'Effect of EA on CPP= 0.3675'

EV adopters in case of 'Effect of EA on CPP= 0.35'

23

Figure E.4. Sensitivity analysis of ‘EV adopters’ based on (-/+ 5%) changes in the value of ‘Cost

reduction per doubling of experience’ that is 0.3; this value has been varied between [0.285, 0.315].

Figure E.5. Sensitivity analysis of ‘perceived legitimacy of e-mobility’ based on (-/+ 5%) changes in the

value of ‘Effect size of SSE on legitimacy’ that is 0.3; this value has been varied between [0.285, 0.315].

0

1M

2M

3M

4M

5M

6M

7M

8M

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EV Adopters (Persons)

Time (Months)

EV adopters in case of ‘Cost reduction per doubling of experience=0.285’

EV adopters in case of ‘Cost reduction per doubling of experience=0.315’

EV adopters in case of ‘Cost reduction per doubling of experience=0.3’

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

360

Perceived Legitimacy of EVs

Time (Months)

Perceived legitimacy of EVs in case of ‘Effect size of SSE on legitimacy= 0.315’

Perceived legitimacy of EVs in case of ‘Effect size of SSE on legitimacy= 0.285’

Perceived legitimacy of EVs in case ‘Effect size of SSE on legitimacy=0.3’

24

Figure E.6. Sensitivity analysis of ‘EV adopters’ based on (-/+ 5%) changes in the value of ‘Effect size of

SSE on legitimacy’ that is 0.3; this value has been varied between [0.285, 0.315].

0

1 M

2 M

3 M

4 M

5 M

6 M

7 M

8 M

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EV Adopters (Persons)

Time (Months)

EV adopters in case of 'Effect size of SSE on legitimacy=0.315’

EV adopters in case of 'Effect size of SSE on legitimacy=0.285’

EV adopters in case of 'Effect size of SSE on legitimacy= 0.3'

25

0

1M

2M

3M

4M

5M

6M

7M

8M

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

285

300

315

330

345

EV Adopters (Persons)

Time (Months)

EV adopters in case of the min values of the selected variables

EV adopters in case of the max values of the selected variables

EV adopters in case of the values of the selected variables in Scenario 2

Figure E.7. Sensitivity analysis of ‘EV adopters’ based on the simultaneous (-/+ 5%) changes in the values of the

following variables:

* The value of ‘The effect of EA on EV production’ is 0.35; this value has been varied between [0.3325, 0.3675];

* The value of ‘Effect of EA on CPP’ is 0.35; this value has been varied between [0.3325, 0.3675];

* The value of ‘Cost reduction per doubling of experience’ is 0.3; this value has been varied between [0.285, 0.315];

* The value of ‘Effect size of SSE on legitimacy’ is 0.3; this value has been varied between [0.285, 0.315].

26

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