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Andarningtyas, N. (2013). Media sosial kian penting dalam pemasaran. [Online]. http://www.antaranews.com/berita/379660/media-sosial-kian-penting-dalam-pemasaran [7 Desember 2013].
Bigne´-Alcan˜ iz, E., Ruiz-Mafe´, C., Alda´s, J., & ManzanodanSanz-Blas, S. (2008). Influence of online shopping information dependency and innovativeness on internet shopping adoption. Online Information Review, 32(5), 648-667. Emerald Group Publishing Limited.
Broekhuizen, T. (2006). Understanding Channel Purchase Intentions: Measuring Online and Offline Shopping Value Perceptions.1-256. Labyrinth Publications.
Harian TI. (2013). Kemenkominfo: 95 Persen Akses Internet Orang Indonesia untuk Jejaring Sosial. [Online]. http://harianti.com/kemenkominfo-95-persen-akses-internet-orang-indonesia-untuk-jejaring-sosial/ [7 Desember 2013].
Hawkins, D.I. & Mothersbaugh, D.L. (2013). Consumer Behavior: Building Marketing Strategy Twelfth Edition. McGraw-Hill International Edition. [Google Scholar]
Internet World Stats. (2012). Top 20 Countries With The Highest Number Of Internet Users. [Online]. http://www.internetworldstats.com/top20.htm [10 Desember 2013].
Laohapensang, O. (2009). Factors influencing internet shopping behaviour: a survey of consumers in Thailand. Journal of Fashion Marketing and Management, 13(4), 501-513. Emerald Group Publishing Limited.
Lim, W.M. (2013). Toward a Theory of Online Buyer Behavior Using Structural Equation Modeling. Modern Applied Science, 7(10), 34-41. Canadian Center of Science and Education.
Lin, G.T.R. & Sun, C. (2009). Factors influencing satisfaction and loyalty in online shopping: an integrated model. Online Information Review, 33(3), 458-475. Emerald Group Publishing Limited.
Mullins, J.W. & Walker, O.C.Jr. (2013). Marketing Management A Strategic Decision Making Approach.McGraw Hill.
Park, C., & Kim, Y. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail and Distribution Management, 31(1), 16-29. Emerald International Journal.
Peter, J.P. & Olson, J.C. (2010). Consumer Behavior and Marketing Strategy, 9th Edition. McGraw Hill.
Prihadi, Susetyo D. (2012). Ini Dia 20 Jejaring Sosial Terbesar di Dunia. [Online]. http://inet.detik.com/read/2012/11/27/103748/2102315/398/ini-dia-20-jejaring-sosial-terbesar-di-dunia [8 Desember 2013].
Santoso, Singgih. (2012). Aplikasi SPSS pada Statistik Multivariat. Jakarta: PT. Elex Media Komputindo. [Google Scholar]
Sugiyono. (2011). Metode Penelitian Kuantitatif, Kualitatif, Dan Kombinasi (Mixed Methods). Bandung: ALFABETA.
SumAll. (2013). For your entire business view. [Online]. https://sumall.com/ [2 Januari 2014].
Thamizhvanan, A., & Xavier, M.J. (2013). Determinants of customers’ online purchase intention: An empirical study in India. Journal of Indian Business Research, 5(1), 17-32. Emerald Group Publishing Limited.
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Open Access
Research article

Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram

riana satriana1,
indira rachmawati2*,
farah alfanur2
1
Telecommunication and Informatics Business Management Program Study, Faculty of Economics and Business, Telkom University
2
A lecture of Economics and Business Faculty, Telkom University
Journal of Corporate Governance, Insurance, and Risk Management
|
Volume 1, Issue 2, 2014
|
Pages 231-240
Received: 01-11-2014,
Revised: 02-15-2014,
Accepted: 02-23-2014,
Available online: 03-29-2014
View Full Article|Download PDF

Abstract:

An activity of online buying and selling products causes many online shops on social media. One of social media being used for buying and selling products in society is Instagram. Factors in influencing online purchasing need to be considered by online shops in order to meet the needs and desires of customers. This study aims to determine the factors that influence the online clothes fashion product purchasing on Instagram and other social media to find out the most dominant variables of each factor. This study employs a descriptive quantitative method and factor analysis in SPSS 20:00 in windows seven. The variables analyzed in this study is the impulse purchase orientation, attitude to online shopping, service quality, perceived risk, informativeness, online trust, specific holdup cost, perceived ease of use, and purchase intention. Data collection techniques are on students of Faculty of Economics and Business (FEB) and students of Faculty of Communication and Business (FKB) of Telkom University done by interviews and questionnaires. The sample in this study is using proportionate stratified random sampling of 100 people with a confidence level of 95% and an error of 5%. The results showed that the newly formed five factors that influence online purchase. The fifth factor is the perceived ease of use, online trust, informativeness, attitude to online shopping, and impulse purchase orientation. Online shopping businesses today are expected to pay attention to these factors in order to improve the service. Future studies are expected to use other variables such as enjoyment, perceived usefulness, and innovativeness while also able to use other analytical techniques such as Structural Equation Modelling (SEM).

Keywords: Factor analysis, Online purchasing, Instagram

1. Introduction

In this modern era, internet is inevitably used by the society to access information. Internet service today has been existed in some regions with the big total of users. Based on the official site of Internet World Stats, it is said that from the internet user side, Indonesia is the eighth rank of all countries in 2012 (Internet World Stats). Ministry of Communication and Informatics says that Indonesian internet users in 2012 reach 63 million people and 95 percent of them use the internet to access the social media (TI newspaper, 2013). Social network in Indonesia is also significantly used by the users as a medium to sell their products as well as their product selling and buying activities. John Kerr, Head of Zeno Asia argues that the businessmen nowadays start to look at the social media to sell their products. According to John, there are 27% promotion media that use the social media,  21% use mobile marketing, 15% use television, 10% use advertising in networking, and the rest of it use other media (Andarningtyas, 2013). Shopping and advertising with using internet becomes a trend today. The innovation in softwares enable the advertiser to determine the costumer who will online purchase, to determine how many customers are, and what margins in each selling (Mullins and walker 2013:356).

The phenomenon of online selling and buying occurs in some social media. In this research, the writer chooses Instagram as the research object since Instagram is one of big ten social media in the world in 2012 based on Silverpop survey result (Prihadi, 2012). The official site of SumAll which is an analyis agency in its newest year-end report in 2013 says that Instagram is the effective social media platform in increasing the business (SumAll, 2013). The phenomenon of online selling and buying is also experienced by the students of Economics and Business Faculty (FEB) as well as Communication and Business Faculty (FKB) of Telkom Univeristy. Instagram is accessed by many students as a medium to buy various types of good like clothes, accessories, foods, shoes, and electronically stuffs. The Instagram application provides easiness for the students to do shopping in everywhere and everytime. The products they desire can be searched only by opening the explore feature and inputing the keyword of the products. The information related to the products which will be searched will be displayed in pictures as well as video. FEB and FKB students of Telkom University also use Instagram as a medium to sell. Instagram eases the students who have desire in business without spending money for the place rent. They just open the online shop by creating an account in Instagram and then posting the products they want to sell.

(Laohapensang, 2009) says that with the development of information search engine in internet since last 20 years, it cannot be denied that the online shopping system will be an alternative way in purchasing products. The online shopping system has undergone a development in relation to various things such as the service, efficiency, security, and popularity. However, the marketing of online media needs to be contionusly fixed if we want to meet the change and development accordance with the needs and expectations of the costumers. Hence, the research about factors which influence the students so that they do online clothes fashion shopping on Instagram as one of trending social media needs to be conducted. Based on the background explained before, this research aims to find out what factors influencing the purchase of online clothes fashion among students of Faculty of Economics and Business as well as Faculty of Communication and Business of Telkom University.

2. Theoretical Foundation/Research Methodology

2.1 Marketing

Marketing is a social process which involves an activity that is needed to enable an individual and an organization to gain what they need and desire by exchanging with others and developing the relation of ongoing exchange (Mullins and Walker, 2013).

2.2 Mixed Marketing

Mixed marketing is a combination of controled marketing variable which uses a manager to conduct a marketing strategy in pursuing the company goal in particular market target (Mullins and Walker, 2013). McCarthy classifies the marketing activities as the means of mixed marketing from four broad types, namley four P in arketing: product, price, place, and promotion.

2.3 Customers’ Attitude

The American Marketing Association in (Peter and Olso, 2010) say that Costumer Behaviour is a dynamic interaction between affection and cognition, behaviour, as well as enviorenment where people do the aspects exchange in their lives. The online community and social network users are interesting for some people for some reasons. (Hawkins and Mothersbaugh, 2013) say that the reasons of social network usage by some people are:

1. The customers’ usage in accessing social network is high and it increases in every time.

2. The majority of costumer uses the network social sites to share information, including the information about a brand and a product.

3. The potency of customers’ acquisition tends to be high.

4. The costumer who interacts about a brand through social media are more potential to remember the brand, tends to share information related to the brand to others, tends to feel connected with the brand, and then tends to buy that brand.

2.4 Nine Attributes Which Influence Online Buying

Based on some previous studies, (Park and Kim, 2003), (Broekhuizen, 2006), (Bigne Alcan iz, et al, 2008), (Lin an Sun, 2009), (Lim, 2013), (Thamizhvanan & Xavier, 2013), there are some variables which influence the online buying. Nine of them are Impulse Purchase Orientation, Attitude to Online Shopping, Service Quality, Perceived Risk, Informativeness, Online Trust, Specific Holdup Cost, Perceived Ease of Used, and Purchase Intention.

2.5 Framework

In this research, the writer used nine variables which influence online purchasing, namely impulse purchase orientation, attitude to online shopping, service quality, perceived risk, informativeness, online trust, specific holdup cost, perceived ease of used, purchase intention.

Figure 1. Framework
2.6 Data and Data Analysis

This research employed descriptive research using quantitative approach. The data are from the quisionnaires. The respondents of this reseach are Instagram users among the students of Faculty of Economics and Business and Faculty Communication and Business of Telkom University. The data are form 100 respondents. The data are analysed by using factor analysis.

3. Discussion

Pre-test of the quisionnaire are conducted. The result is showed in Table 1.1.

Table 1. Data realiability

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.936

.937

26

Since the result of reliability shows 0,937, so the items on this research are reliable with the value> 0.6 (Sugiyonno 2011:184) [15] so that the questionnaires can be used in this research. Table 2 presents the descriptive analysis from the sample of the research.

Table 2. Descriptive analysis

Item

Category

Sample Size

Percentage (%)

Amount of time in online purchasing

> 2 times

44

44%

2 times

31

31%

1 time

25

25%

Sex

Female

89

89%

Male

11

11%

Age

18-20 years old

42

42%

21-23 years old

58

58%

24 years old

0

0%

Study Program

Business Administration

23

23%

Acoounting

19

19%

Communication

20

20%

MBTI

35

35%

International MBTI

3

3%

Income per month

< Rp. 1,000.000

18

18%

Rp 1,000,000 until. Rp.

3,000,000

76

76%

> Rp. 3,000,000

6

6%

Pengeluaran Untuk Berbelanja Online Perbulan

< Rp. 100,000

25

25%

Rp. 100000 unti Rp.

300,000

62

62%

> Rp. 300,000

13

13%

The descriptive analysis in this reseach is conducted to find out the percentage of each variable which is the most dominant in accordance with the repsondent based on the answers of the quisionnaire. The grouping category is divided into four which is conducted with using the same range.

Table 3. Percentage grouping category

Range Value

Category

25% - 43,75%

Very bad

43,75% - 62,5%

Bad

62,5% - 81,25%

Good

81,25% - 100%

Very good

Table 4. Respondent respons

Variable

Average of Total Score Percentage (%)

Category Description

Impuls Purchase Orientation

70,08%

Good

Attitude to Online Shopping

69,25%

Good

Variable

Average of Total Score Percentage (%)

Category Description

Service Quality

64,5%

Good

Perceived Risk

67,37%

Good

Informativeness

78,08%

Good

Online Trust

68,25%

Good

Specific Holdup Cost

73,5%

Good

Perceived Ease of Use

77,8%

Good

Purchase Intention

71,5%

Good

3.1 Factor Analysis

The result of factor analysis done on the fifth data processing reaches 0,763 (0,763>0.5) of the Kaiser- Meyer-Olkin Measure of Sampling Adequacy number and 0.000(0.000<0.05) of the significance number so that the existing indicator can be further analsyed. The MSA number is around zero until one. A variable can be predicted and further analysed if it has >0.5 of MSA (Santoso 2010:66) [14]. The result of anti-image correlation on the table of Anti Image Matrices indicates that all analysed indicators have >0.5 MSA value so the further analysis can be conducted.

Table 5. KMO and bartlett’s test of sphericity (fifth test)

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.763

Approx. Chi-Square

1300.398

Bartlett's Test of

Sphericity

Df

231

Sig.

.000

On the first KMO and Barlett’s Test of Sphericity Test, the analysed indicatot amount is 26. After the fifth test is conducted, it remains 22 indicators since there are four issued indicators on the previous test, they are IPO1, SQ9, SQ7, and IPO2.

Table 6. Total variance explained

Total Variance Explained

Compone nt

Initial Eigenvalues

Extraction Sums of Squared Loadings

Total

% of

Variance

Cumulative

%

Total

% of

Variance

Cumulative

%

1

8.176

37.165

37.165

8.176

37.165

37.165

2

2.085

9.477

46.642

2.085

9.477

46.642

3

1.588

7.216

53.858

1.588

7.216

53.858

4

1.473

6.696

60.554

1.473

6.696

60.554

5

1.249

5.679

66.233

1.249

5.679

66.233

6

.975

4.434

70.667

7

.850

3.866

74.533

8

.838

3.808

78.341

9

.718

3.264

81.605

10

.656

2.982

84.587

11

.596

2.710

87.297

12

.479

2.178

89.475

13

.449

2.041

91.516

14

.342

1.553

93.069

15

.303

1.378

94.447

16

.284

1.289

95.736

17

.238

1.081

96.817

18

.201

.914

97.732

19

.170

.775

98.506

20

.147

.666

99.172

21

.097

.441

99.613

22

.085

.387

100.000

Extraction Method: Principal Component Analysis.

The SPP processing result on the table 6 shows that if 22 variants from nine variables are summarised in a single factor so the variants which can be explained by that single factor will be 8.176/22 x 100%= 37.165%. Hence, a single factor can explain 37.165% from the variability of the nine variables. If the nine variables are extracted into five factors, the five total factors can explain the variability of the nine original variables, which are 37,165% + 9,477% + 7,216%+ 6,696% + 5,679%= 66.233% . Eigen values show the relative importance of each factor in counting the anlysed variants (Santoso 2010:83) [14]. The criteria in determining the formed factor is by using the amounts of eigenvalue that are worth more than one. The eige value which is less than one can not be used as the formed factor. In the table 6, it is obtained that the eigenvalues which is still worth more than one exists in the first until fifth factor. It can be concluded that it can form five new factors.

Table 7. Rotated component matrix

Rotated Component Matrixa

Component

1

2

3

4

5

IPO3

.192

-.021

.224

.078

.734

AOS4

.598

.260

-.048

.534

.182

AOS5

.007

.161

.446

.687

-.235

AOS6

.720

.148

.345

.050

-.217

SQ8

.320

.560

.057

.230

-.359

PR10

.492

.448

.089

.198

.222

PR11

.148

.488

-.031

-.112

.402

I12

.423

.170

.592

.282

.062

I13

.182

.098

.796

.346

.096

I14

.193

.174

.792

.208

.121

OT15

.158

.665

.031

.428

.127

OT16

.003

.800

.230

.162

.148

OT17

.130

.787

.193

-.154

-.042

SHC18

.385

-.143

.278

.553

.098

SHC19

-.019

.206

.242

.421

.520

SHC20

.117

.503

.117

.270

.534

EOU2

1

.720

-.066

.365

-.194

.130

EOU2

2

.216

.137

.709

.009

.333

EOU2

3

.110

.110

.209

.672

.314

PI24

.709

.287

.121

.215

.363

PI25

.563

.266

.162

.365

.120

PI26

.508

.118

.392

.419

.388

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 18 iterations.

The result from the data processing in the table 7 shows there are 22 analsyed indicators but not all indicators gives the contribution on the five newly formed factors. There are 17 indicators which gives the contribution on the five newly formed factors and five other indicators do not have contribution since they have <0.55 factor loading. The indicators which have <0.55 factor loading are PR10 for 0.492, PR11 for 0.4888, SHC19 for 0.520, SHC20 for 0.534, and PI26 for 0.508.

Based on the analysis factor, there are five newly formed factors within new indicators. The grouping is done in accordance with the factor loading value. Factor loading is the correlation amount between one variable and a newly formed factor (Santoso 2010:85) [14]. These are the newly formed factors:

1. Perceived Ease of Use. The first factor has 8.176 Eigen value and 37.165% contribution in percentage. This factor covers five variables: the customers can visit the online shops easily (0.720), the customers need online shop to buy products they need (0.720), the customers prefer to choose shopping in inline shop (0.709), the customers feel comfortable when they are purchasing in online shops (0.598), and the customers will recommend others to do online shopping (0.563). These factors are called Perceived Ease of Use since the largest factor loading is from the Perceived Ease of Use factor, which is the customers can visit the online shop easily on media social Instagram (0.720).

2. Online Trust. The second factor has 2.085 Eigen value and 9.477% contribution in percentage. This factor covers four variables: the online shops keep personal data safely (0.787), the online shops have a good reputation (0.665), and the online shops consistenly deliver on the promise (0.560). These factors are called Online Trust since the largest factor loading is from the Online Trust variable which is the online shops on social media Instagram are trusted to sell with honest (0.800).

3. Informativeness. The third factor has 1.5888 Eigen value and 7.216% contribution in percentage. This factor covers four variables: the customers can access information they need (0.769), the customers can plan the purchases with the existing information on the online shop (0.792), the customers can easily find the products they desire on the online shops (0.709), and the customers can access the newest information about the available products on the online shops (0.592). These factors are called Informativeness which is the customers can find the information about the clothes they need (0.796).

4. Online Attitude to Online Shopping. The fourth factor has 1.473 Eigen value and 6.696% contribution in percentage. This factor covers three variables: the customers think that online clothes shopping on social media Instagram is very interesting (0.687), the payment is easy on online shops (0.672), and the customers are accostumed to visit online shops (0.553). These factors are called Online Attitude to Online Shopping since the largest factor loading is from Attitude to Online Shopping variable which is the customers think that online clothes shoppings on social media Instagram are very interesting (0.687).Impuls Purchase Orientation. The fifth factor has 1.249 Eigen value and 5.679% contribution in percentage. This factor is called Impuls Purchase Orientation since it only covers one variable so that the largest factor loading is when the customers visit the online shop which sells clothes on social emdia Instagram, there is possibility that they will do the purchasing (0.734).

4. Conclusion

Result of this research shows that there are five newly formed factors. Those factors have the addition and subtraction of indicators which are from the different variables. The five newly formed factors are:

1. Perceived Ease Of Use,

2. Online Trust,

3. Informativeness,

4. Attitude To Online Shopping,

5. Impuls Purchase Orientation.

The businessmen today are expected to concern those factors so that they can increase the service quality. The further research is expected to use other variables such as enjoyment, perceived usefulness, and innovativeness or to use another analysis technique like Structural Modelling (SEM).

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
Andarningtyas, N. (2013). Media sosial kian penting dalam pemasaran. [Online]. http://www.antaranews.com/berita/379660/media-sosial-kian-penting-dalam-pemasaran [7 Desember 2013].
Bigne´-Alcan˜ iz, E., Ruiz-Mafe´, C., Alda´s, J., & ManzanodanSanz-Blas, S. (2008). Influence of online shopping information dependency and innovativeness on internet shopping adoption. Online Information Review, 32(5), 648-667. Emerald Group Publishing Limited.
Broekhuizen, T. (2006). Understanding Channel Purchase Intentions: Measuring Online and Offline Shopping Value Perceptions.1-256. Labyrinth Publications.
Harian TI. (2013). Kemenkominfo: 95 Persen Akses Internet Orang Indonesia untuk Jejaring Sosial. [Online]. http://harianti.com/kemenkominfo-95-persen-akses-internet-orang-indonesia-untuk-jejaring-sosial/ [7 Desember 2013].
Hawkins, D.I. & Mothersbaugh, D.L. (2013). Consumer Behavior: Building Marketing Strategy Twelfth Edition. McGraw-Hill International Edition. [Google Scholar]
Internet World Stats. (2012). Top 20 Countries With The Highest Number Of Internet Users. [Online]. http://www.internetworldstats.com/top20.htm [10 Desember 2013].
Laohapensang, O. (2009). Factors influencing internet shopping behaviour: a survey of consumers in Thailand. Journal of Fashion Marketing and Management, 13(4), 501-513. Emerald Group Publishing Limited.
Lim, W.M. (2013). Toward a Theory of Online Buyer Behavior Using Structural Equation Modeling. Modern Applied Science, 7(10), 34-41. Canadian Center of Science and Education.
Lin, G.T.R. & Sun, C. (2009). Factors influencing satisfaction and loyalty in online shopping: an integrated model. Online Information Review, 33(3), 458-475. Emerald Group Publishing Limited.
Mullins, J.W. & Walker, O.C.Jr. (2013). Marketing Management A Strategic Decision Making Approach.McGraw Hill.
Park, C., & Kim, Y. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail and Distribution Management, 31(1), 16-29. Emerald International Journal.
Peter, J.P. & Olson, J.C. (2010). Consumer Behavior and Marketing Strategy, 9th Edition. McGraw Hill.
Prihadi, Susetyo D. (2012). Ini Dia 20 Jejaring Sosial Terbesar di Dunia. [Online]. http://inet.detik.com/read/2012/11/27/103748/2102315/398/ini-dia-20-jejaring-sosial-terbesar-di-dunia [8 Desember 2013].
Santoso, Singgih. (2012). Aplikasi SPSS pada Statistik Multivariat. Jakarta: PT. Elex Media Komputindo. [Google Scholar]
Sugiyono. (2011). Metode Penelitian Kuantitatif, Kualitatif, Dan Kombinasi (Mixed Methods). Bandung: ALFABETA.
SumAll. (2013). For your entire business view. [Online]. https://sumall.com/ [2 Januari 2014].
Thamizhvanan, A., & Xavier, M.J. (2013). Determinants of customers’ online purchase intention: An empirical study in India. Journal of Indian Business Research, 5(1), 17-32. Emerald Group Publishing Limited.

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Satriana, R., Rachmawati, I., & Alfanur, F. (2014). Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram. J. Corp. Gov. Insur. Risk Manag., 1(2), 231-240. https://doi.org/10.56578/jcgirm010214
R. Satriana, I. Rachmawati, and F. Alfanur, "Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram," J. Corp. Gov. Insur. Risk Manag., vol. 1, no. 2, pp. 231-240, 2014. https://doi.org/10.56578/jcgirm010214
@research-article{Satriana2014FactorAO,
title={Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram},
author={Riana Satriana and Indira Rachmawati and Farah Alfanur},
journal={Journal of Corporate Governance, Insurance, and Risk Management},
year={2014},
page={231-240},
doi={https://doi.org/10.56578/jcgirm010214}
}
Riana Satriana, et al. "Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram." Journal of Corporate Governance, Insurance, and Risk Management, v 1, pp 231-240. doi: https://doi.org/10.56578/jcgirm010214
Riana Satriana, Indira Rachmawati and Farah Alfanur. "Factor Analysis of Online Clothes Fashion Purchase on Social Media Instagram." Journal of Corporate Governance, Insurance, and Risk Management, 1, (2014): 231-240. doi: https://doi.org/10.56578/jcgirm010214
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