Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

Vol. 03, No. 10, October 2023

e - ISSN : 2807-8691 | p- ISSN : 2807-839X

IJSSR Page 2586

https://doi.org/ 10.46799/ijssr.v3i10.556

This work is licensed under a Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

Mapping PT PLN (Persero) Consumer Willingness to Make

Electricity Account Payments (Willingness To Pay) Based

on Regional Characteristics Using the Ranking Analysis

Method

Rahmada Mulia Whardana Moljoadie

Electrical Engineering, Faculty of Electricity and Renewable Energy, Institut PLN Technology,

Indonesia

Email: [email protected]

Keywords

ABSTRACT

Account Receivable, Electricity Account,

Willingness to Pay, Regional

Characteristics, Ranking Analysis

Method

Like companies that are oriented to the buying and selling business

in general, PT PLN (Persero) or PLN also has the same business

risk, namely the existence of accounts receivables arising from

electricity buying and selling transactions with consumers. By

getting cash in from billing electricity bills, PLN's operations will of

course be maintained so that service to customers can be even

better. To support securing the company's cash flow, it is necessary

to carry out an analysis related to external factors that influence

the willingness to pay electricity bills by looking at the

characteristics of regional conditions so that mitigation can be

carried out to control electricity receivables. One of the relevant

methods to obtain a mapping of consumers' willingness to make

electricity bill payments is to use the ranking analysis method,

which is an analysis of several rankings on methods including

conducting surveys, interviews with experts based on the Delphi

method ( 2 rounds of interviews) and statistical calculations using

the independent sample t-test and the final test using the Borda

calculation method. In this study, the factors that most influenced

customer behavior related to their willingness to pay electricity

bills were obtained based on regional characteristics, namely

regional education and culture factors, economic factors and

consumer income then the electricity service reliability factor. The

mapping of these factors can be used by companies as one of the

considerations for making decisions to develop operational

strategies in an effort to control the values of electricity account

receivables.

INTRODUCTION

In doing sell buy goods nor services , then will happen A transactions involved seller and

buyer. Transactions that occur the is results agreement second split party or more Good done in a way

cash nor with system instalment or installments. In selling buy also get to know accounts payable

system , because has There is agreement or agreement as buyer can use goods/ services the moreover

formerly new Then do payment, so goods / services the become a debt for buyer/ user if Not yet done

payment. This also applies if buyer use system installments or installments, fees goods or services not

yet paid off become receivables business for seller.

According to the Big Indonesian Dictionary (KBBI), receivables is an amount of money or funds

that are lent and can be obtained billed . In context business , understanding receivables that is bill of

money by a company to expected consumers can paid or paid off in one at most year moment bill

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2587

published . Whereas arrears , according to the KBBI, constitute installments that have not yet been

made paid . Companies must Can arrange the best current assets, cause current assets will used For

operational company . When operating company can walk with OK , then expected company can

produce and improve profitability (Harapan and Prasetiono 2016).

Usual PT PLN (Persero). called with PLN, namely company property of the moving State in the

field provision Genre power electricity For need public common in Indonesia. As a business entity ,

PLN owns business main that is provision power electricity through generator, distribute through

transmission and distribution power electricity to consumer For used . For operate business main ,

PLN does transaction sell buy with consumer order activities provision power electricity can Keep

going walk and can Keep going fulfil need society in Indonesia. Transaction patterns sell buy PLN with

consumer is PLN selling goods in the form of Genre power electricity to consumer Good with service

payment postpaid nor prepaid. Service postpaid in question here is, PLN consumers use power

electricity moreover first and pay in accordance with those used, whereas service prepaid , PLN

consumers do purchase energy electricity moreover formerly in accordance need with do payment up

front new can enjoy Genre the electricity (Sihombing, Sitompul, and Sinaga 2022).

Like business oriented company sell buy in general , PLN also has it risk the same business that

is exists arrears from receivables account that appears from transaction sell buy power electricity with

consumer. Arrears the No Can avoided Because PLN consumers who own one of the different

characteristics is factor economy from consumer That yourself . Apart from that, force majeure is also

one of the reasons reason appearance arrears on transactions sell buy power electricity including

disasters natural nor disaster others. Based on data during in 2022, PLN noted ratio arrears compared

to with income business with percentage monthly highest in the month February is amounting to

4.04% as described in research, whereas For percentage lowest happened in June with ratio 2.61%

(Sundt and Rehdanz 2015; Abdullah and Mariel 2010; Hensher, Shore, and Train 2014; Graber et al.

2018).

On research this, got it PLN still looks at it own ratio arrears to the total income already Enough

low with the average in 2022 being 3%. Based on GMT Research, for company World electricity on

term time 2010 to 2015 , average ratio arrears to total sales is 25%. Even with average ratio of 3% in

2022 , PLN remains own task heavy For maintain mark that . This thing naturally For still maintain

level quality PLN services to consumers , because with good cash flow, then PLN will still can give

maximum service and with reliability supply Genre electricity to consumer (Hampton et al. 2022).

Apart from that , PLN also remains must support government programs For provide supply electricity

to Indonesian society with affordable price in accordance with trustworthy Article 33 of the 1945

Constitution which reads "Economy held nationally based on on democracy economy with principle

togetherness, efficiency fair, insightful environment, independence, as well with guard balance

progress and unity economy national”(Giannopoulos et al. 2013).

Since it was launched in 2008 in Indonesia, it turns out there is receivables pile of customers

until in 2022. Noted based on data, values receivables prepaid on December 31 2022 is reached Rp.

1.93 Trillion , the value Far more tall compared to receivables customer postpaid with value IDR 541

billion.

Become question , why service prepaid payment in advance still own receivables, it turns out

96.6% receivables prepaid originate from P2TL findings or Order Electrical Power Usage, ie violations

committed by customers so that must published bill follow up installments so that make it A

receivables. Meanwhile, the remaining 3.4% originate from bill continuation from implementation

migration and installments Cost Connection (BP) (Blocher et al. 2019).

In 2019 , PLN published Regulation Directors related Consumer Administration Guidelines , as

since perdir the published, PLN will do penalty or fine termination to customer prepayments in

arrears the receivables exceed the stipulated time limit . Receivables Balance Trend Prepaid every the

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2588

month experience increase , while the trend is repayment receivables prepaid experience decrease ,

with setting performance targets receivables prepaid in 2023 , expected receivables prepaid will can

more controlled and of course at a time support acceleration of cash in flow company .

METHODS

This research flow chart was prepared to understand the research process carried out

(Bryman 2016; Bell, Bryman, and Harley 2022). The flow chart used in carrying out the research is as

presented in Figure 3.1 below :

Table 1Research Flow Chart

RESULTS

Criteria Screening Survey

The next stage was to conduct a survey of respondents spread throughout Indonesia who were

PLN employees who were currently handling consumer receivables collection work in their respective

START

Selection of factors that influence the

willingness to pay customer electricity

bills

Carrying on a survey of predetermined

factors, n = 150

Expert Interview

Method

Delphi n=10

Analysis of survey results

Analysis of interview rresults

Independent analysis of samples T-test

Analysis results for

research conclusions

END

Analysis of the Borda Calculation Method

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2589

work units or had previously done this work. The number of respondents who were successfully

collected was 156 respondents with questions as follows:

The results of the survey via online media (online ) are as follows:

Figure 1. Amount respondents based on work units

Figure 2. Appearance results survey question First

Figure 3. Appearance results survey question second

Based on the results of the survey conducted, it produces a score or survey value as the

poverty ratio is considered by respondents to be the most influencing factor with a total value of 76.

The second factor is the size of the regional minimum wage or UMR with a value of 62. To recapitulate

the survey results, it is presented in the table as follows. below this.

Table 1Implementation Results Survey

No.

Factor affecting

Survey scores

Regional poverty ratio

Regional/Provincial Minimum Wage

Gross Regional Domestic

Product (GRDP)

Regional Income

per Capita

Electrical service

reliability (SAIDI/SAIFI)

Menagih langsung/

penyampaian Invoice

Informasi melalui

broadcast (Email/WA)

Pemutusan sementara

secara langsung

Remote shunt trip

Prepaid migration

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2590

Table 2Service strategy information and communication public by the Ministry of

Communication and Information

The Cultural Development Index (IPK) is prepared by referring to the Culture Development

Indicators (CDIs) framework developed by UNESCO. Based on this framework, CDIs are compiled by

22 indicators grouped into the seven dimensions mentioned above with weight percentages as in the

following table:

Table 4. Percentage weight dimensions in GPA measurement

Dimensi

% Bobot

Dimension : Culture Economic

10%

Dimension : Education

20%

Dimension : Ketahanan Sosial Budaya

20%

Dimension : Warisan Budaya

25%

Dimension : Ekspresi Budaya

10%

Dimension : Budaya Literasi

10%

Dimension : Gender

Discussion

Electrical service reliability

Regional Income per Capita

Regional Happiness Index

Regional density ratio (area/population)

Gross Regional Domestic Product (GRDP)

PROBLEM

Information is not

yet synergistic/there

is no agenda yet

Limited &

uncoordinated access

to information

Subjective/a priori

assessment of state

institutions

GOALS

1. Fulfillment of the

public's right to know

2. Accommodating

Community

Aspirations in the

formulation of public

policies

3. Positive image of state

institutions

Public Information and

Communication Management

INSTITUTIONAL

COMPONENTS

Brainware

Software

Hardware

4. Spiritualware

SUCCESS KEY

Authority/Legality

2. Access/coordinati

HR is quite capable

4. Infrastructure

Media Usage:

1. Outdoors

2. Traditional

3. Print

4. Broadcasting

5. Face to face

6. Internet - Online

Amount & coverage of

existing information

INTENSIFICATION and

EXTENSIFICATION

Support for state

administrators' policies

and programs

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2591

Expert Interviews

Using the Delphi Method, in this research interviews were conducted with competent experts

in the field of receivables collection. These experts are PLN employees consisting of manager and

senior manager level officials as well as expert director staff who have many years of experience in

collecting PLN consumer receivables.

The interviews were carried out at the PLN Head Office located on Jl. Trunojoyo Blok MI No.

135 Kebayoran Baru, South Jakarta. The number of experts who were interviewed was 10 people,

taking into account that this number was >5% of the number of respondents from the survey

conducted at the beginning of the research.

The results of the first interview along with evidence from its implementation are as shown in

the picture below.

Figure 4. Documentation results interview with experts

The recapitulation of the results of the first interview with the expert is as shown in the table

below.

Table 5. Recapitulation results interview First

No.

Factor affecting

Number of Values

Regional poverty ratio

Regional/Provincial Minimum Wage

Electrical service reliability

Regional Income per Capita

Regional Happiness Index

Regional density ratio (area/population)

Gross Regional Domestic Product (GRDP)

Public Information and Communication

Management Index

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2592

Education Index

Average

30.67

In accordance with the method used, namely the Delphi method, interviews were conducted

twice to ensure whether the assessment made in the first interview was appropriate or the expert

reviewed the answers at the first meeting with the results as shown in the evidence picture below. An

additional influencing factor is the cultural development index, because the education index is one of

the dimensions of cultural development.

Figure 5. Documentation results interview second with expert

The recapitulation of the results of the second interview with the expert is as shown in the

table below.

Table 6. Recapitulation results interview second

No.

Factor affecting

Number of Values

Regional poverty ratio

Regional/Provincial Minimum Wage

Electrical service reliability

Regional Income per Capita

Regional Happiness Index

Regional density ratio

Gross Regional Domestic Product (GRDP)

Public Information and Communication

Management Index

Education and culture index

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2593

Average

33.89

Implementation of the Regional Characteristics Index on Electricity Receivables

From the results of using the Delphi method which has been implemented in this research, the

author will make a comparison between the index value and the actual receivables data per each

province. The receivables data used will be calculated as a percentage to compare the amount of

arrears with turnover in the province. The receivables used are electricity receivables as per data on

31 December 2022 based on reports published by PLN through the 2022 statistical report which can

be downloaded from the web www.pln.co.id. Meanwhile, sales per report for 2022 are also obtained

from the PT PLN (Persero) statistical report which is downloaded on the page www.pln.co.id. The PLN

Main Unit receivables data as of December 31 2022 is as shown in the table below.

Figure 6. PLN Main Unit Electricity Receivables Report as of December 31, 2022

When compared with sales, the average speed of billing days is obtained based on data

contained in the PLN statistical report with results as in the table below:

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2594

Figure 7. Average Billing Speed Receivables PLN consumers in 2022

Meanwhile, for the receivables ratio based on the data above, the receivables ratio obtained

per each PLN parent unit is as shown in the table below:

Table 7. Ratio Receivables Consumer compared to with Income Sale

Parent Unit

Rp Sales

Rp. Receivables

% Ratio

Aceh

3,169,860.30

264,615.45

8.35%

North Sumatra

13,657,082.12

1,030,845.27

7.55%

Boast

3,997,069.69

272,817.57

6.83%

S2JB

10,734,493.35

814,475.73

7.59%

Babylon

1,880,964.69

70,614.79

3.75%

Lampung

5,836,114.55

505,711.64

8.67%

Riau and Riau Islands

8,732,099.94

776,680.04

8.89%

West Kalimantan

3,473,571.51

220,391.17

6.34%

Central Kalimantan

5,717,645.35

388,764.17

6.80%

Kaltimra

5,740,416.33

454,406.52

7.92%

North Sulawesi

4,518,353.18

444,344.83

9.83%

South Sulawesi, Rabar

10,764,478.40

1,079,542.52

10.03%

MMU

1,562,502.32

119,601.89

7.65%

PPB

2,486,011.53

151,894.87

6.11%

NTT

1,460,257.21

45,455.17

3.11%

NTB

2,383,894.84

150,823.87

6.33%

East Java

44,002,627.20

3,398,398.68

7.72%

Central Java and DIY

32,423,939.74

2,239,287.48

6.91%

West Java

62,900,363.72

5,193,774.44

8.26%

Jaya

29,870,292.40

3,014,583.38

10.09%

Bali

7,147,173.78

487,503.36

6.82%

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2595

Banten

44,698,953.02

3,895,233.52

8.71%

Combined

307,158,165.17

25,019,766.36

8.15%

In contrast to the previous average speed of collecting consumer receivables, if you look at the

receivables ratio, which is IDR Receivables compared to IDR Sales, then UID Jakarta Raya has the

highest ratio with a value of 10.09%, different from the highest average collection days found at UID

Banten. Meanwhile, for the lowest value, there is a similarity between the average collection days and

the receivables ratio, namely PLN UIW NTT is ranked first as the unit with the lowest value.

In this research, regional characteristic index trials will be carried out only on parent units that

oversee 1 province, including the Main Distribution Unit/Region of Aceh, North Sumatra, West

Sumatra, Bangka Belitung, Lampung, West Kalimantan, NTT, NTB, East Java, Java West, Greater

Jakarta, Bali and Banten. These 13 units will be assessed based on the Regional Characteristics Index

determined by the Expert . Obtained from this research are as in the table below.

Table 8. Recapitulation of data per region

UNIT x

PROVINCE

SAIDI

SAIFI

ACEH

14.75

3,413,666

7.54

6.55

26.06

71.24

10.7

184.98

79.65

52.61

NUMUT

8.33

2,710,493

14.78

9.84

37.94

70.57

4.8

859.87

67.20

50.33

BOAST

6.04

2,742,476

8.95

5.75

32.38

71.34

7.4

252.75

65.95

54.60

BABYLON

4.61

3,498,479

1.95

3.35

38.67

73.25

11.0

85.07

64.20

54.70

LAMPUNG

11.44

2,633,284

9.66

5.82

28.06

71.64

3.8

371.90

47.50

55.38

KALBAR

6.81

2,608,601

21.18

19.57

26.78

72.49

26.6

231.22

92.20

49.72

NTT

20.23

2,123,994

7.63

9.15

13.30

70.31

8.9

110.89

74.95

48.93

NTB

13.82

2,371,407

6.39

4.72

18.65

69.98

3,4

140.15

58.35

61.26

East Java

10.49

2,040,244

3.35

3.33

42.72

72.08

1,2

2,454.50

57.00

57.88

JABAR

7.98

1,986,670

12.81

8.62

32.18

70.23

0.7

2,209.82

61.55

52.04

JAKARTA

4.61

4,900,798

2.90

2.02

182.91

70.68

0.1

2,914.58

32.15

57.13

BALI

4.53

2,713,672

1.03

1.08

34.16

71.44

1.3

219.80

74.70

66.40

BANTEN

6.24

2,661,280

1.11

1.00

39.52

68.08

0.8

665.92

47.40

48.95

Data Testing

From the data for each of the regional characteristic criteria above, testing was then carried

out by comparing it with the percentage of receivables as previously calculated using INDEPENDENT-

SAMPLE T TEST analysis via the SPSS application (Imam 2018).

The provisions used in this research are to look at the significance value of t with the following

explanation:

a. If the significance value of t < 0.05, it means that there is a significant influence between one

independent variable and the dependent variable.

b. If the significance value of t> 0.05, it means that there is no significant influence between one

independent variable and the dependent variable.

a. Regional Poverty Ratio

From the results of the analysis via SPSS, the t-count value was -0.4 with a significance

of 0.35. Because the significance value is 0.35 > 0.05, it can be concluded that H0 is accepted

so that there is no significant influence between the two groups. The point estimate using

Cohen's d for the Regional Poverty Ratio is -0.22.

b. Regional/Provincial Minimum Wage

From the results of the analysis via SPSS, the t-count value was obtained at 1 with a

significance of 0.17. Because the significance value is 0.17 > 0.05, it can be concluded that H0

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2596

is accepted so that there is no significant influence between the two groups. For the point

estimate using Cohen's d for the Regional Minimum Wage is 0.56.

c. Electrical Service Reliability - SAIDI

From the results of the analysis via SPSS, the t-count value was obtained at 0.86 with a

significance of 0.2. Because the significance value is 0.2 > 0.05, it can be concluded that H0 is

accepted so that there is no significant influence between the two groups. The point estimate

using Cohen's d for SAIDI is 0.48.

d. Electrical Service Reliability – SAIFI

From the results of the analysis via SPSS, the t-count value was obtained at 0.69 with a

significance of 0.25. Because the significance value is 0.25 > 0.05, it can be concluded that H0

is accepted so that there is no significant influence between the two groups. The point

estimate using Cohen's d for SAIFI is 0.38.

e. Regional Income Capita

From the results of the analysis via SPSS, the t-count value was obtained at 1.04 with a

significance of 0.16. Because the significance value is 0.16 > 0.05, it can be concluded that H0

is accepted so that there is no significant influence between the two groups. The point

estimate using Cohen's d for Income per Capita is 0.58.

f. Regional Happiness Index

From the results of the analysis via SPSS, the t-count value was -0.76 with a significance of

0.23. Because the significance value is 0.23 > 0.05, it can be concluded that H0 is accepted so

that there is no significant influence between the two groups. The point estimate using

Cohen's d for the Regional Happiness Index is -0.42.

g. Regional Density Ratio

From the results of the analysis via SPSS, the t-count value was obtained at 0.4 with a

significance of 0.35. Because the significance value is 0.35 > 0.05, it can be concluded that H0

is accepted so that there is no significant influence between the two groups. For the point

estimate using Cohen's d for the Area Density Ratio it is 0.23.

h. Gross Regional Domestic Product (GRDP)

From the results of the analysis via SPSS, the t-count value was obtained at 0.9 with a

significance of 0.19. Because the significance value is 0.19 > 0.05, it can be concluded that H0

is accepted so that there is no significant influence between the two groups. The point

estimate using Cohen's d for GRDP is 0.5.

i. Public Information and Communication Management Index (PIKP)

From the results of the analysis via SPSS, the t-count value was -0.67 with a significance of

0.26. Because the significance value is 0.26 > 0.05, it can be concluded that H0 is accepted so

that there is no significant influence between the two groups. The point estimate using

Cohen's d for the PIKP Index is -0.37

j. Educational and Cultural Development Index (IPK)

From the results of the analysis via SPSS, the t-count value was -1.3 with a significance of

0.11. Because the significance value is 0.11 > 0.05, it can be concluded that H0 is accepted so

that there is no significant influence between the two groups. The point estimate using

Cohen's d for the Cultural Development Index is -0.73 . Recapitulation of test results using the

SPSS application with Independent Sample T Test analysis is as shown in the table below.

Table 9. Recapitulation of test results Independent sample T Test using SPSS

No.

Factor affecting

t value

Sign

Point

Estimates

Regional poverty ratio

-0.4

0.35

-0.22

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2597

Regional/Provincial Minimum Wage

1.0

0.17

0.56

Reliability of Electrical services -SAIDI

0.86

0.2

0.48

Reliability of Electrical services -SAIFI

0.69

0.25

0.38

Average

0.77

0.22

0.43

Regional Income per Capita

1.04

0.16

0.58

Regional Happiness Index

-0.76

0.23

-0.42

Regional density ratio

0.4

0.35

0.23

Gross Regional Domestic Product (GRDP)

0.9

0.19

0.5

Public Information and Communication

Management Index

-0.67

0.26

-0.37

Education and Culture Index

-1.3

0.11

-0.73

From the table above, if you pay attention to the significance value as those with smaller values

have a better influence than larger values, then the Cultural Development Index has the smallest value

which is close to 0.05 so that the cultural development index is the factor that most influences the size

electricity receivables.

Research Results using the Ranking Analysis Method

If compared with the implementation of the survey and the results of interviews with experts

using the Delphi method (2x interviews) and testing using the t-test, the following comparison is

obtained:

Table 10. Recapitulation comparison results study

No.

Factor affecting

Survey

Interview

Expert

T-Test

Sign

Regional poverty ratio

0.35

Regional/Provincial Minimum Wage

0.17

Electrical service reliability

0.22

Regional Income per Capita

0.16

Regional Happiness Index

0.23

Regional density ratio

0.35

Gross Regional Domestic Product (GRDP)

0.19

Public Information and Communication

Management Index

0.26

Education and Culture Index

0.11

By considering the similarity of factors related to income or economic level of the community,

including regional poverty ratio, regional/provincial minimum wage, regional per capita income and

gross regional domestic product (GRDP), taking into account the research objectives to obtain more

specific analysis results, then The average calculation was carried out on these four factors with the

results as shown in the table below.

Table 11. Grouping factor in accordance with similarity character

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2598

No.

Factor affecting

Survey

Expert

interviews

T-Test

Sign

Regional poverty ratio

0.35

Regional/Provincial Minimum Wage

0.17

Regional Income per Capita

0.16

Gross Regional Domestic Product (GRDP)

0.19

Average

0.22

So the final results of the research based on the three methods are as shown in the table below.

Table 2Research results end with using 3 methods

No.

Factor affecting

Survey

Interview

Expert

T-Test

Sign

Economic conditions/income

0.22

Electrical service reliability

0.22

Regional Happiness Index

0.23

Regional density ratio

0.35

Public Information and Communication

Management Index

0.26

Education and Culture Index

0.11

From the data above, with the results ordered based on ranking, the results obtained are as

shown in the table below:

Table 13. Comparison results study based on Ranking

No.

Factor affecting

Survey

Expert

interviews

T-Test

Sign

Rate

Economic conditions/income

2.3

Electrical service reliability

2.7

Regional Happiness Index

4.0

Regional density ratio

5.3

Public Information and Communication

Management Index

6.0

Education and Culture Index

1.0

This is in accordance with the results of the second interview with experts , as the formation of

culture in society is the main factor that makes PLN consumers carry out routine payment of

electricity bills with a rate of 1.0. Another influencing factor is economic conditions/income with a rate

of 2.3, followed by the reliability of electricity services from PLN itself with a value of 2.7.

Data testing using the Borda calculation method

If an analysis is carried out based on regions, in this research the regions in Indonesia are

grouped into 3 regions, namely:

1. Sumkal Regional which consists of all provinces on the islands of Sumatra and Kalimantan

2. Jamali Regional which consists of all provinces on the islands of Java, Madura and Bali

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2599

3. Sulmapana Regional which consists of all provinces on the islands of Sulawesi, Maluku, Papua and

Nusa Tenggara

The results of the analysis using the Borda calculation method, according to the literature

obtained from the Toolshero article with the title Borda Calculation Method (Janse, 2019)are as

follows:

Table 3Calculation results weight with use Borda method

Priority

Selection

Calculation Results

Weight Calculation Results

Selvice

Reliability

Economic

Conditions

Education and

Culture

Service

Reliability

Economic

Conditions

Education and

Culture

Sumkal

9x7

4x7

13x7

3x6

7x6

3x6

3x5

8x5

4x5

3x4

0x4

3x3

0x3

3x3

2x2

3x2

3x1

0x1

Final Score Sumkal

124

126

144

Jamali

10x7

6x7

13x7

2x6

8x6

7x6

6x5

7x5

5x5

1x4

3x4

0x4

2x3

1x3

3x2

1x2

2x2

5x1

3x1

2x1

Final Score Jamali

133

148

167

Sulmapana

8x7

11x7

15x7

105

10x6

9x6

5x6

3x5

2x5

3x5

1x4

2x4

0x4

0x3

1x3

0x2

2x2

3x1

1x1

Final Score Sulmapana

138

154

158

Interpretation of Research Results

From the results of research using both the ranking analysis method and calculations using the

Borda method, it was found that there were 3 (three) major regional characteristic factors that

influence the willingness of PLN consumers to pay electricity bills, namely educational and cultural

factors, economic factors and community income and then electricity service reliability factors

(Bornmann et al. 2014; Chen et al. 2019). Education and cultural factors are the first factors in this

research, supported by several journals such as (Ajzen 1980; Zeithaml 1988) as consumer behavior or

consumer behavior can influence the consumer's purchasing power, including electricity bill payments

(Al Irsyad et al. 2020). Apart from being related to consumer behavior, it turns out that company

culture also influences the size of receivables, as stated in the research journal by (Jeyachitra et al.

2010; Juliati 2021; Viklund and Wallvik 2014; Anand and Gupta 2002; Atkinson 2011).

The next results of this research are economic factors and community income (poverty ratio,

minimum wage, per capita income, people's purchasing power and others), both based on survey

results, interview results, calculation approaches from the t-test and calculation tests using the Borda

method, both show that economic factors are one of the factors that influence the size of consumer

receivables, this is supported by several research journals, including (Sopranzetti 1998; Mian and

Smith Jr 1992; Sopranzetti 1999; Khairani and Veralita 2015; Unsulbar, Purwati, and Dahlia 2018).

CONCLUSION

Willingness to pay electricity bills by PLN consumers can be influenced by regional

characteristics in order of the most influencing, among others is education index and cultural

development index , economic factors and income , reliability of electricity services , regional

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2600

happiness index , regional density ratio , public information and communication management index ,

the use of ranking analysis methods from several methods can be used to test the dominant factors in

determining priority levels between criteria that refer to regional/provincial characteristics, namely

survey method, Delphi-based interview method with experts two interviews), calculation analysis

method using independent sample t-test, namely comparing statistical data with actual arrears data

per region, then finally Testing was carried out using the Borda calculation method to see regional

conditions based on survey results. From these results, initial mapping can be carried out to obtain the

right strategy for billing consumer electricity bills based on the consumer's willingness to pay

behavior according to the characteristics of the consumer's area.

From the results of 3 methods, namely conducting surveys, expert interviews, and t-test

sample calculations, the average ranking results were obtained where the main factor influencing

consumers' willingness to pay electricity bills was the development of education and culture with an

average value of 1.0, then followed by economic conditions and income factors, namely with the same

average value of 2.3. The third factor with a value of 2.7 is related to the reliability of electricity

services, namely the reliability of electricity services which is measured through the realization of

SAIDI and SAIFI in each region. Meanwhile, the results of calculations using the Borda method get the

same results as the average calculations using ranking. This shows that the 3 main factors that

influence the willingness of PLN consumers to make electricity payments, thus influencing the size of

receivables, are educational and cultural factors, economic factors and community income and

electricity service reliability factors.

REFERENCES

Abdullah, Sabah, and Petr Mariel. 2010. “Choice Experiment Study on the Willingness to Pay to

Improve Electricity Services.” Energy Policy 38 (8): 4570–81.

https://doi.org/10.1016/j.enpol.2010.04.012.

Ajzen, Icek. 1980. “Understanding Attitudes and Predictiing Social Behavior.” Englewood Cliffs.

Anand, Manoj, and Chandra Prakash Gupta. 2002. “Working Capital Performance of Corporate India:

An Empirical Survey for the Year 2000-2001.” Management and Accounting Research, January-

June.

Atkinson, William. 2011. “Think of Accounts Receivable as Sales, Not Collections: Distributors Need to

Get Smarter about Credit.” EHS Today 4.

Bell, Emma, Alan Bryman, and Bill Harley. 2022. Business Research Methods. Oxford university press.

Blocher, E J, D E Stout, P E Juras, and Steven Smith. 2019. Cost Management (A Strategic Emphasis) 8e.

McGraw-Hill Education.

Bornmann, Lutz, Moritz Stefaner, Felix de Moya Anegón, and Rüdiger Mutz. 2014. “What Is the Effect of

Country-Specific Characteristics on the Research Performance of Scientific Institutions? Using

Multi-Level Statistical Models to Rank and Map Universities and Research-Focused Institutions

Worldwide.” Journal of Informetrics 8 (3): 581–93. https://doi.org/10.1016/j.joi.2014.04.008.

Bryman, Alan. 2016. Social Research Methods. Oxford university press.

Chen, Ning, Lu Chen, Yingchao Ma, and An Chen. 2019. “Regional Disaster Risk Assessment of China

Based on Self-Organizing Map: Clustering, Visualization and Ranking.” International Journal of

Disaster Risk Reduction 33: 196–206. https://doi.org/10.1016/j.ijdrr.2018.10.005.

Giannopoulos, George, Andrew Holt, Ehsan Khansalar, and Stephanie Cleanthous. 2013. “The Use of the

Balanced Scorecard in Small Companies.” International Journal of Business and Management 8

(14): 1–22. https://doi.org/10.5539/ijbm.v8n14p1.

Graber, Sachiko, Tara Narayanan, Jose Alfaro, and Debajit Palit. 2018. “Solar Microgrids in Rural India:

Consumers’ Willingness to Pay for Attributes of Electricity.” Energy for Sustainable

Development 42: 32–43. https://doi.org/10.1016/j.esd.2017.10.002.

International Journal of Social Service and Research https://ijssr.ridwaninstitute.co.id/

IJSSR Page 2601

Hampton, Harrison, Aoife Foley, Dylan Furszyfer Del Rio, Beatrice Smyth, David Laverty, and Brian

Caulfield. 2022. “Customer Engagement Strategies in Retail Electricity Markets: A

Comprehensive and Comparative Review.” Energy Research & Social Science 90: 102611.

https://doi.org/10.1016/j.erss.2022.102611.

Harapan, Anthonius, and Prasetiono Prasetiono. 2016. “Pengaruh Average Collection Period, Average

Payment Period, Turnover in Days, Sales Growth Dan Debt Ratio Terhadap Profitabilitas

Perusahaan.” Diponegoro Journal of Management 5 (3): 390–400.

Hensher, David A, Nina Shore, and Kenneth Train. 2014. “Willingness to Pay for Residential Electricity

Supply Quality and Reliability.” Applied Energy 115: 280–92.

https://doi.org/10.1016/j.apenergy.2013.11.007.

Imam, Ghozali. 2018. Aplikasi Analisis Multivariete Dengan Program IBM SPSS 23. 9th ed. Semarang:

Badan Penerbit Universitas Diponegoro.

Irsyad, Muhammad Indra Al, Anthony Halog, Rabindra Nepal, and Deddy Priatmodjo Koesrindartoto.

2020. “Economical and Environmental Impacts of Decarbonisation of Indonesian Power

Sector.” Journal of Environmental Management 259: 109669.

https://doi.org/10.1016/j.jenvman.2019.109669.

Jeyachitra, A, E Bennet, P Nageswari, and S Parasuraman. 2010. “Receivable Management of Indian

Cement Industry in a Changed Scenario.” SMART Journal of Business Management Studies 6 (1):

78–87.

Juliati, Fina. 2021. “The Influence of Organizational Culture, Work Ethos and Work Discipline on

Employee Performance.” AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis 1 (1): 34–39.

Khairani, Siti, and Milda Veralita. 2015. “FAKTOR-FAKTOR MEMPENGARUHI PENYEBAB PIUTANG

TAK TERTAGIH PADA KOPERASI BAITUL MALWAT TAMWIL (BMT) TARBIYAH PALEMBANG.”

Fordema 12 (1): 1–15.

Mian, Shehzad L, and Clifford W Smith Jr. 1992. “Accounts Receivable Management Policy: Theory and

Evidence.” The Journal of Finance 47 (1): 169–200. https://doi.org/10.1111/j.1540-

6261.1992.tb03982.x.

Sihombing, Geofani Goran, Rosalinda S Sitompul, and Leonard R Sinaga. 2022. “PENGARUH PERILAKU

KONSUMEN TERHADAP TUNGGAKAN REKENING LISTRIK PADA PT. PLN (PERSERO) ULP

SIBORONGBORONG.” Tapanuli Journals 4 (1): 97–107. https://doi.org/10.2201/unita.v4i1.284.

Sopranzetti, Ben J. 1998. “The Economics of Factoring Accounts Receivable.” Journal of Economics and

Business 50 (4): 339–59. https://doi.org/10.1016/S0148-6195(98)00008-3.

———. 1999. “Selling Accounts Receivable and the Underinvestment Problem.” The Quarterly Review

of Economics and Finance 39 (2): 291–301. https://doi.org/10.1016/S1062-9769(99)00016-2.

Sundt, Swantje, and Katrin Rehdanz. 2015. “Consumers’ Willingness to Pay for Green Electricity: A

Meta-Analysis of the Literature.” Energy Economics 51: 1–8.

https://doi.org/10.1016/j.eneco.2015.06.005.

Unsulbar, Administrator Jepa, Wiwi Purwati, and Dahlia Dahlia. 2018. “Pengaruh Faktor Eksternal

Terhadap Piutang Tak Tertagih Pada Koperasi Guru Rambate Rata (KGRR) Tinambung

Kabupaten Polewali Mandar.” Journal of Economic, Public, and Accounting (JEPA) 1 (1): 1–14.

https://doi.org/10.31605/jepa.v1i1.

Viklund, Emmelie, and Emma Wallvik. 2014. “Dependence of Strategic Management in Account

Receivable Collections.”

Zeithaml, Valarie A. 1988. “Consumer Perceptions of Price, Quality, and Value: A Means-End Model and

Synthesis of Evidence.” Journal of Marketing 52 (3): 2–22.

https://doi.org/10.1177/002224298805200302.

International Journal of Asian Education ,

Rahmada Mulia Whardana Moljoadie

IJSSR Page 2602

Rahmada Mulia Whardana Moljoadie (2023)

First publication rights:

International Journal of Social and Service (IJSSR)

This article is licensed under: