Analysis of Factors Affecting the Use of Bicycle Rental for Tourism in

Surakarta

Dewi Handayani

, P. T. M. Sari

, Erma Fitria Rini

and Widi Hartono

Civil Engineering Department, Sebelas Maret University, Surakarta, Indonesia

Urban and Regional Planning Department, Sebelas Maret University, Surakarta, Indonesia

Keywords: Bicycle Rental, Tourism.

Abstract: Cultural diversity is a rich asset for individuals and communities. Protection, promotion and maintenance of

cultural diversity are essential requirements for sustainable development for the benefit of present and future

generations. Surakarta is one of the cities in Indonesia that has many cultural heritage. Surakarta is also known

as green city with green transportation as one of policies. Sustainable development strategies can not forget

the culture: the strategy should be culturally sensitive as well as take advantage of the dynamic interaction

between cultures. To that end, the city of Surakarta plans the introduction of cultural tourist areas to visitors

by offering a choice of environmentally friendly mode of transportation in the form of bike rental to get around

in the cultural tourism area. Sampling technique using non-random sampling technique to 100 respondents of

visitor attractions Surakarta Palace, Vastenburg Fortress, and Pura Mangkunegaran. Questionnaires were

given by interview. The method of analysis used is factor analysis method principal component analysis

(PCA). Based on the analysis of 22 variables formed 8 new factors that influence the visitors to use bicycle

rental in the cultural tourism area of Surakarta City that is operational bicycle rental, bicycle rental user safety,

supporting conditions, bicycle facilities, affordability and cost, bicycle rental system, and clarity of rental fee

bike.

1 INTRODUCTION

The Indonesia is a nation that has a high level of

cultural diversity. Culture in Indonesia is very

influential on the development of time from time to

time and changes in natural conditions in Indonesia

(Melina,2016). The Convention on Protection and

Promotion of the Diversity of Cultural Expression of

UNESCO 2005 in its definition says cultural diversity

is a rich asset for individuals and communities

(UNESCO,2005). Protection, promotion and

maintenance of cultural diversity are essential

requirements for sustainable development for the

benefit of present and future generations. In achieving

sustainable development, the United Nations has set

17 sustainable development objectives by 2015. The

11th objective is sustainable cities and communities

and the 13th objective is climate change

Surakarta is one of the cities in Indonesia that has

a cultural heritage and also as green city. One of the

policies in green city of Surakarta is green

transportation. As a green city that has many cultures,

Surakarta city is faced to preserve and maintain the

culture also develops green city. This is in line with

the United Nations sustainable development

objective of making cities more inclusive, safe and

sustainable and taking important steps to fight climate

change and its impact.

Previous research on bikes as environmentally

friendly transportation with bikes sharing concept has

been done (Bachand-Marleau, J., 2012; Lee B H Y,

2012; El-Geneidy A. M., 2012), (Tajuddin, 2012;

Musyafir, 2012; Wunas S., 2012; Hamzah B., 2012),

(Faghih-Imani A., 2014; Eluru N., 2014; El-Geneidy

A M., 2014; Rabbat M., 2014; Haq U., 2014), and

(Yanyong G., 2016; Zhou J., 2016; Wu Y., 2016; Li

Z., 2016). This research uses the same method of

(Fatma I., 2013; Ambar, 2013; Saino, 2013) that is

the factor analysis method Principal Component

Analysis. The research variables are taken from

(Bachand-Marleau, J., 2012; Lee B H Y, 2012; El-

Geneidy A. M., 2012), (Tajuddin, 2012; Musyafir,

2012; Wunas S., 2012; Hamzah B., 2012), (Faghih-

Imani A., 2014; Eluru N., 2014; El-Geneidy A M.,

2014; Rabbat M., 2014; Haq U., 2014), and (Yanyong

G., 2016; Zhou J., 2016; Wu Y., 2016; Li Z., 2016)

154

Handayani, D., Sari, P., Rini, E. and Hartono, W.

Analysis of Factors Affecting the Use of Bicycle Rental for Tourism in Surakarta.

DOI: 10.5220/0010004100002917

In Proceedings of the 3rd International Conference on Social Sciences, Laws, Arts and Humanities (BINUS-JIC 2018), pages 154-159

ISBN: 978-989-758-515-9

total 22 variables (Sari P T M, 2018) that have been

adjusted to the condition of Surakarta City.

Sustainable development strategies cannot forget

the culture: they should be culturally sensitive as well

as take advantage of the dynamic interaction between

cultures. To that end, the city of Surakarta plans the

introduction of cultural tourist areas to visitors by

offering a choice of environmentally friendly mode of

transportation in the form of bike sharing to get

around in the cultural tourism area. Therefore, this

study is to aim the factors that influence the visitors

to use bike sharing in tourist area of Surakarta city

culture.

2 THEORETICAL BASE

Region is a space that is a geographical unity and all

elements related to it whose limits and systems are

determined based on functional aspects and have

certain characteristics or specific or special (Anonym,

1992). So, in other words the area is a place that is

limited by functional aspects. Vastenburg Fortress,

Kasunanan Surakarta, and Pura Mangkunegaran have

similar functions as cultural sights, where they show

evidence of the history of the city of Surakarta.

Sharing a bike or bike sharing is a bicycle rental

system that principally uses bicycles in turn and

together with a rental system managed by a particular

operator. This concept has been developed in various

parts of the world starting from the 1960’s (Bachand-

Marleau, J., 2012; Lee B H Y, 2012; El-Geneidy A.

M., 2012). Bike sharing has four generations in its

operational and logistical development: The White

Bicycle System, the Coin Deposit Systems, the IT-

based system, and the fourth generation is the

generation used up to now with smartcard and

smartphone systems. In Indonesia bike sharing has

been developed in the city of Bandung with the name

Boseh (Bike on Street Everyone Happy) and Obike.

Bike sharing in Bandung is a collaboration of

Banopolis (transport consultant) and the city

government of Bandung. Boseh and Obike are

already using modern IT-based systems and

applications. Boseh uses smartcards and Obike uses

smartphone apps in lending transactions (Yolanda F,

2017), (Pikiran rakyat, 2017).

Factor analysis is a statistical technique primarily

used to reduce or summarize data from multiple

variables that are converted into a few variables, e.g.

from 15 variables that are long changed to 4 or 5 new

variables called factors and still contain some big

information contained in the original variable

(Pikiran rakyat, 2017). In the factor analysis there are

no independent and dependent variables, the factor

analysis process itself tries to find the relationship

between a number of variables that are mutually

dependent with others so that one or more sets of

variables may be less than the initial number. There

are two types of factor analysis namely principal

component analysis and common factor analysis.

3 RESEARCH

This research was conducted in the Surakarta cultural

tourism area of Palace, Vastenburg Fortress, and

Pura Mangkunegaran, as seen in Figure 1. Primary

data obtained from the results of questionnaires

spread to 100 respondents covering three tourist sites.

Questionnaire consists of 2 pieces of form. Form 1

contains questions about the characteristics of

respondents and form 2 contains the factors that

influence visitors using bike sharing in a cultural

tourism area. There are 22 variables in this research

which can be seen in table 1 below (Supranto J.,

2004). The method of analysis used is factor analysis

method principal component analysis (PCA).

Figure 1 Location Research Map.

Analysis of Factors Affecting the Use of Bicycle Rental for Tourism in Surakarta

155

Table 1. Research Variables.

Variable Symbol

Weather conditions determine

decisions for c

clin

High air pollution conditions reduce

clin

interes

Topographic bike lanes are

convenientl

skipped (not wav

)

Distance determines the decision to

ike

Adequate bike lane capacity or not

ammed between c

clists

The presence of a barrier (in the

form of markers and nails) with

other modes for c

clin

safet

The special lane availability for

clin

The presence of si

ns for c

clists X8

Map / route of bicycle paths

availabilit

in the tourist destination

Cycling place availability close to

the destination

X10

The bic

cle has baske

X11

Bicycle equipped with bicycle rack

facilit

(2 persons capacit

)

X12

Sophisticated bicycles such as

electric bic

cles are available

X13

The availability of sufficient bikes at

the place where the rental bikes stop

X14

Affordable cost for bike sharin

X15

Bicycle rental fee is obviously in

accordance with the length of

bicycle lending (multiples of 30

minutes)

X16

The duration of flexible bike lendin

X17

Bicycle rental operator availability

(customer service)

X18

Easy instructions for using bike

sharin

X19

Convenience in bike lending

transactions

X20

Bike sharing using application-

ased s

stems on smartphones

X21

Bike sharing using manual system

with direct pa

men

X22

4 RESULT AND DISCUSSION

The first step in factor analysis is to form a correlation

matrix and then perform testing determinant of

correlation matrix obtained determinant value of

0.002 where the value is close to 0 so it is stated that

the inter-related variables. The value of Kaiser Meyer

Olkin Measure of Sampling finds 0.682 greater than

0.5 means that the variables are sufficient and

appropriate to be analysed using factor analysis. In

Bartlett's test of sphericity, the value of chi-square

558,907 and significant value = 0,000 <0,005 then the

hypothesis that the variables are not correlated is

rejected, it means that the variables are correlated and

appropriate to be analysed using factor analysis. Next,

testing the value of MSA seen from diagonal anti-

image correlation matrix. The result of MSA value

analysis from 22 variables has MSA value more than

0,5 thus the whole variable is declared valid and

proper so it can be continued with factor analysis

calculation.

Article I. The second step is determining

number of factors. Factor is a new variable or

formation variable. The number of factors is

determined from the value of the eigen value of each

variable. The Total Variance Explained shows the

contribution of a factor component. There are two

kinds of analysis of variance explanation: Initial

Eigenvalue shows the factors that are formed. The

total column shows the eigenvalue value. The

determination of factor number is obtained from

eigenvalues value more than 1 (one) and Extraction

Sums of Squared Loadings shows the number of

variants obtained.

Article II. The third step is Factors Rotation.

The factor matrix rotation is done so that no

overlapping variable occurs in explaining the factor.

Rotation method used is orthogonal rotation with

varimax procedure. The following Table 3 after the

rotation.

Table 3 show the results formed eight new factors

with new factor name.

From the results of research the eight new factors can

explain 66.448% variants of 22 variables that affect

visitors using a bicycle rental in the area of cultural

tourism. This means that 8 new factors are able to

represent the previous 22 variables (Sari P T M,

2018). The results of this study support (Tajuddin,

2012; Musyafir, 2012; Wunas S., 2012; Hamzah B.,

2012) which states that the convenience of cycling,

safety and safety of cycling, and infrastructure

facilities are factors that affect people to use bicycles.

Similarly, research (Faghih-Imani A., 2017;

Hampshire R., 2017; Marla L., 2017; Eluru N., 2017)

which states that bicycle facilities is an influential

factor for bicycle rental users. The above research

also supports the research of (Bachand-Marleau, J.,

2012; Lee B H Y, 2012; El-Geneidy A. M., 2012)

which states that the location of a shelter / station

BINUS-JIC 2018 - BINUS Joint International Conference

156

affects the willingness to cycling in research over

these factors including supporting conditions.

Similarly, (Yanyong G., 2016; Zhou J., 2016; Wu Y.,

2016; Li Z., 2016) stating that the cost and ease of

access to borrow and restore the bike into a factor that

affects the public using a bicycle.This study gained

new factors that have not existed in previous studies.

The factor is rental bike sharing system. There are

two types of rental bike sharing systems that are

obtained include the system manually with direct

payments and rental systems using applications with

certain payment systems.

5 CONCLUSIONS

Based on the results of research and discussion of 22

variables formed 8 new factors that influence visitors

to use bicycle rental in the cultural tourism area of

Surakarta City include the operation of the bicycle

rental, the safety of bicycle rental users, supporting

conditions, bicycle facilities, affordability and cost,

bicycle rental system, and the clarity of the cost of

bicycle rental.

ACKNOWLEDGEMENTS

The authors would like to thankfully acknowledge

Sebelas Maret University and Ministry of Research

and High Education for funding support of this

research project.

REFERENCES

Melina 2016 Cultural Role in Human Development of

Indonesia World of Science 2(4)

UNESCO 2005 Convention on the Protection and

Promotion: Diversity of Cultural Expressions

Bachand-Marleau J, Lee B H Y, El-Geneidy A M 2012

Better understanding of factors influencing likehood of

using shared bycicle systems and frecuency of use

Transportation Research Record:Journal of the

Trasnsportation Research Board 2314 66-71.

Tajuddin, Musyafir, Wunas S and Hamzah B 2012 Bicycle

As Eco Friendly Alternative Transportation Mode

(Case Study: Cbd Panakukang Makassar City Journal

of Hassanudin University

Faghih-Imani A, Eluru N, El-Geneidy A M, Rabbat M, Haq

U 2014 How land-use and urban form impact bicycle-

sharing system (BIXI) in Montreal Journal of

Transport Geography. 41 406-314

Yanyong G, Zhou J, Wu Y, Li Z 2016 Identifiying the

factors affecting bike-sharing usage and degree of

satisfaction in Ningbo, China (China: Shanghai

University of finance and Economic)

Fatma I, Ambar and Saino 2013 Analysis of Factors

Affecting Consumer Decisions To Use Train Services

Comuter Journal University of Surabaya

Sari P T M 2018 Analysis of Factors Affecting Visitors

Using Bike Sharing in Culture Tourism City of

Surakarta Thesis (Surakarta: Sebelas Maret University)

Anonym Undang-Undang Republik Indonesia Nomor 24

Tahun 1992 tentang Penataan Ruang

Yolanda F 2017 Bandung City Government to prepare New

Bike Sharing concept Online

http://nasional.republika.co.id/berita/nasional/daerah/1

6/02/16/o2my4p370-pemkot-bandung-siapkan-bike-

sharing-berkonsep-baru

Pikiran rakyat 2017 Now oBike Comes in Bandung Online

http://www.pikiran-rakyat.com/bandung-

raya/2017/12/20/kini-obike-hadir-di-bandung-416274

Supranto J 2004 Multivariate Analysis: Meaning and

Interpretation (Jakarta: Rineka Cipta)

Faghih-Imani A, Hampshire R, Marla L, Eluru N 2017 An

empirical analysis of bike sharing usage and

rebalancing: Evidence from Barcelona and Seville

Transportation Research Part A: Policy and Practice

97 177-191

Analysis of Factors Affecting the Use of Bicycle Rental for Tourism in Surakarta

157

APPENDIX

Table 2. Factor Matrix after Rotation.

Componen

1 2 3 4 5 6 7 8

x20 .808 .167 .035 .066 .006 -.008 .064 .052

x19 .756 .146 .149 .045 .082 -.024 .035 .015

x14 .625 -.111 .096 .269 .200 .167 -.034 .071

x8 .128 .799 .058 .060 .024 -.046 .097 .034

x9 .099 .714 .114 -.055 .220 .195 -.263 .029

x7 .429 .468 -.022 .356 .106 -.223 .148 -.168

x2 .065 -.064 .745 .046 -.074 -.101 .157 .315

x1 .102 -.502 .595 -.004 .192 .129 -.190 -.195

x6 .115 .328 .548 .208 .053 .328 .183 -.160

x10 .068 .288 .513 .391 .074 .033 -.275 .014

x18 .387 .159 .511 -.252 .273 .246 .001 -.160

x17 .191 .209 .440 .275 .158 -.333 -.183 -.082

x3 .107 .190 .042 .671 .002 .402 -.267 .227

x5 .217 -.128 .105 .664 .144 .099 .145 -.018

x13 -.006 .367 .072 .485 -.266 -.167 .207 -.165

x11 .052 -.028 .112 .114 .842 .117 -.065 -.095

x12 .142 .220 .022 -.031 .725 -.027 .084 .272

x4 .007 .016 .015 .201 .261 .784 .200 -.081

x15 .531 -.044 .118 -.009 -.246 .561 -.132 .087

x22 .103 .053 .072 .014 -.008 .121 .801 .099

x16 .123 .051 .099 -.030 .070 -.004 .062 .816

x21 .132 .076 .302 -.146 -.066 .019 -.503 -.537

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation conver

ed in 12 iterations.

Table 3 show the results formed eight new factors with new factor name.

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Table 3. New Factor.

Variabel Symbol New Factor Symbol

Variant

Value

1. Convenience in bike lending

transactions

X20

Bike Sharing

Operational

F1 19,660

2. Easy instructions for using bike

sharin

X19

3. The availability of sufficient bikes at

the place where the rental bikes stop

X14

4. The presence of signs for cyclists X8

Bike Sharing

User Safety

F2 9,526

5. Map / route of bicycle paths

availability in the tourist destination

6. The special lane availability for

clin

7. High air pollution conditions reduce

clin

interes

Supporting

Conditions

F3 8,341

8. Weather conditions for c

clin

9. The presence of a barrier (in the form

of markers and nails) with other

modes for c

clin

safet

10. Cycling place availability close to the

destination

X10

11. Bicycle rental operator availability

(customer service)

X18

12. The duration of flexible bike lendin

X17

13. Bike sharing using application-based

stems on smartphones

X21

14. Topographic bike lanes are

convenientl

skipped (not wav

)

Comfort

Cycling

6,861

15. Adequate bike lane capacity or not

ammed between c

clists

16. Sophisticated bicycles such as electric

cles are available

X13

17. The bic

cle has baske

X11

Bicycle

Facility

F5 6,425

18. Bicycle equipped with bicycle rack

facilit

(2 persons capacit

)

X12

19. Distance determines the decision to

ike

Affordability

Distance and

Cost

F6 5,998

20. Affordable cost for bike sharing X15

21. Bike sharing using manual system

with direct pa

men

X22

Bicycle

Rental S

ste

F7 4,985

22. Bicycle rental fee is obviously in

accordance with the length of bicycle

lendin

(multiples of 30 minutes)

X16

Bicycle

Rental Fees

Clarit

F8 4,651

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