Progress Conference Volume 2 Number 2, August 2019| 68
E-Commerce Organization Account Analysis As An Effort To Boost The Sales Volume Of UMKM Products
Fauziyah1, Muhammad Rijalus Sholihin2
Management Department, STIE Widya Gama Lumajang1 Accounting Department, STIE Widya Gama Lumajang2 [email protected]
Abstract
Indonesia is a big country, based on the Population Census data released by the Central Statistics Agency (BPS), the number of inhabitants in 2018 is not less than 260 million. This figure will continue to grow each year, projected in 2035 the number reached more than 300 million inhabitants. The existence and role of MSMEs (Micro, Small and Medium Enterprises) in supporting national economic activity, especially in addressing the problems of poverty, unemployment, inequality between sectors, is very important and strategic. Therefore, a strengthening of the economy of small and medium priority towards the creation of a solid economic fundamentals. However, in realizing the great potential, SMEs still face many problems, both internally and externally. The purpose of this study was to determine the extent of product variety, posting and updating, as well as keywords affect the increase in sales volume either partially or simultaneously. The method used in this penelitiaan is quantitative descriptive. The results of this study indicate that the product variety, posting and updating, as well as keywords affect the increase in sales volume either partially or simultaneously.
Keywords: Sales Volume; Organizations Accounts, E-Commerce
INTRODUCTION
Indonesia is a big country, based on the Population Census data released by the Central Statistics Agency (BPS), the number of inhabitants in 2018 is not less than 260 million.
(https://databoks.katadata.co.id). This figure will continue to grow each year, projected in 2035 the number reached more than 300 million inhabitants.
Aside from being a big country in terms of population, Indonesia is also very rich in natural resources. One factor abundant natural resources Indonesia is a geographically strategic location, flora, fauna and hydrographic potential and abundant natural resource deposits. Indonesia's natural resources come from agriculture, forestry, marine and fisheries, livestock, plantation and mining and energy. (http://indonesia.go.id).
No fewer than 11 types of natural resources owned by Indonesia of the world, among which are the best quality gold mines, coal mines, natural gas reserves, soil fertility and underwater riches in abundance. (https://ilmugeografi.com).
Existenceand the role of MSMEs (Micro, Small and Medium Enterprises) in supporting national economic activity, especially in addressing the problems of poverty, unemployment, inequality between sectors, is very important and strategic. Therefore, a strengthening of the economy of small and medium priority towards the creation of a solid economic fundamentals. However, in realizing the great potential, SMEs still face many problems, both internally and externally.
(http://www.riaupos.co). But slowly, the SMEs to improve by improving their skills, improved management, organizational management and optimize the mastery of technology in marketing their products.
The opportunities are very wide, considering the smartphone users in Indonesia is a 4 Smartphone users worldwide, as released by the Ministry of Communications and Information Technology (Communications) that digital marketing research firm eMarketer estimates that by 2018 the number of active users of smartphones in Indonesia for more than 100 million people. With that amount, Indonesia will become the country with the fourth largest smartphone active users in the world after China, India, and America.
It has been very much the SMEs that utilize information and communications technology to market their products through an account in the online marketplace, as Tokopedia and bukalapak. But their sales have not been in significant numbers, due account created is a personal account that has a weakness the lack of intensity of the seller in terms of updating the products it sells, so the system Search Engine Optimization (SEO) slow response to finding your product to potential buyers. In addition, some technical things that have not been controlled by them such as keyword selection as one of the online-based marketing strategy.
The concept of empowerment of SMEs through an account-based business organization E- Commerce may be one of the solutions of some of the key issues at the top, especially in the field of marketing in today's digital era. As has been done by the SMEs in Jember. They have a joint account in the various marketplaces for example bukalapak, Tokopedia, Lazada, Shoopee and others. The objective of this joint account is to maximize the intensity of the uploading and updating the products sold by the perpetrators of these SMEs. This movement they do consistently every day. Compiled schedule of anyone among those who have to make uploading and updating their products and how many times the number of those posts.
The government also supports the business transformation from conventional to digital systems by launching the National Road Map-Based Electronic Trading System (Road map E-Commerce) Year 2017-2019. The road map also promulgated into a Presidential Regulation (Pepres) 74 Year 2017 is expected to accelerate the development of electronic-based national trading system (e-Commerce), business starter (start-up), business development, and accelerating the distribution of integrated logistics. SME sector's contribution to Gross Domestic Product (GDP) from year to year has increased, so that the Government strongly encourages SMEs to digital (go online) in order to extend the reach of their business market.
Online Forum SME KSRN (Community Seller Reseller National) was first established in Jember, a movement toward the era of the rise of Indonesian SMEs in a draft Conspiracy Digital Marketing with the main weapon is the establishment of an organizational account by families, with a passion to empower SMEs to go online dream can dominate the global market, without having to rely on large capital ruler.
Not a few of the results of the SME products in Indonesia are sold cheaply to middlemen at very low prices, then packaged and sold at very high prices. With Online Sales platform that SMEs must rise to dominate the world with its superior product range. Must be literate internet technology, good management by way of managing a good online store page to appear interactive with consumers. Based on data from Kemenkop and there are about 4,000 SMEs SME business people who peddle their products via the internet. But they are still trading with each other personally and price wars. Unhealthy price competition makes the SMEs will not survive long existence.
Formulation required a set of binding rules in an SME organization on several conditions, so it is
Progress Conference Volume 2 Number 2, August 2019| 70 not colonized with the great power that could threaten the existence of SMEs themselves in running their online business. The purpose of this study was to determine the extent of product variety, posting and updating, as well as keywords affect the increase in sales volume either partially or simultaneously.
METHODS
This study by ekspanasinya level is the research that intends to explain the position of the variables studied and the relationship between one variable with other variables. Therefore, this type of research is an association in which the study is conducted to determine the relationship between two or more variables (Siregear, 2013). This study determines the influence of variable compensation on the motivation and performance of employees where the variable compensation consists of direct financial compensation, indirect and non-financial.
The research was conducted on SME members KSRN in Jember. This is done because SMEs KSRN Jember district is the first SME organizations are creating accounts on multiple platforms E- Commerce as Tokopedia and products that have been sold already reached the figure of 500 items in a fairly short period of time. The data used in this study is internal as obtained directly from members of the SME KSRN in Jember. Researchers distributed questionnaires to members of the SME KSRN to be filled. While this type of data from this study is a type of primary data where the data obtained from the distribution of questionnaires.
The population in this study are all paramedics in private clinics Lumajang with sampling techniques using simple random sampling. This technique is a sampling technique to provide equal opportunities for every member of the population to become members of the sample (Sugiyono, 2009). As for the method of determining the sample size used was a method developed shaky Roscoe in the book Research methods for Business (1982) with the provisions is the sample size was decent in the study were between 30 to 500, and if the sample in a category then the number of members of the sample of each category at least 30, and if the research will conduct a multivariate analysis with the number of sample members at least 10 times from jumla variables studied,
Collecting data in this study is to use a questionnaire or a list of questions to respondents in the SME KSRN Jember. In addition the environmental observations beforehand to add object-related information used in this research. Scoring in this study is based on a Likert scale. The form of Likert scale according to Sugiyono (2009) are:
Table 1 Likert scale according to Sugiyono (2009)
No. choice answers value score
1. Strongly agree / always / very positive (SS) 5
2. Agree / often / positive (ST) 4
3. Hesitant / sometimes / neutral (RG) 3
4. Disagree / hardly ever / negative (TS) 2
5. Strongly disagree / never (STS) 1
Indicator variable in this study has three independent variables namely the variety of products, posting and updating as well as the use of keywords, while the dependent variable is the volume of sales, the measurement scale used in this study is an ordinal scale.
Before the testing of hypotheses, it is necessary to test the validity and reliability of the questionnaire used in this study. The following explanation regarding the test in question.
According to Husein Umar (2008), the validity of the test is useful to know if there are questions on the questionnaire that must be removed / replaced as irrelevant. Meanwhile, according Sugiyono (2009), minumum requirement for a qualitative data are considered eligible if the validity of the minimum-value of 0.3 or.
Reliability is a measure showing the stability and consistency of an instrument that measures a concept and useful for accessing the "goodness" of a measure. Reliabitias or reliability conducted to determine the extent to which the questionnaires may give different results (Sugiyono, 2009). A questionnaire called if the questionnaire had high reliability are stable and reliable. The method used in this research is Cronbach Alpha method.
According to Imam Ghozali (2011: 48) a construct or variable is said to be reliable if the value of Cronbach Alpha> 0.6.
Normality test is used to determine whether the data used in normal distribution. In this study, normal distribution is used on the residual regression model where the model said to be good if the residuals of the normal distribution model and vice versa. Normality test is done by looking at the normal probability plots on SPSS output, if the values of the data distribution located around the diagonal straight line normality requirements are met or normal distribution.
Multikolinearitas showed a linear relationship was perfect or near perfect among some or all of the variables that explain from the regression model or between independent variables with each other independent variables in the regression model are correlated linearly. If there is a relatively perfect multikoliniaritas then through a least squares estimator or OLS (Ordinary Least Square) became erratic and variance and standard deviation becomes undefined. To find out if the data does not qualify multicolinearity is to look at the SPSS output coefficients table if VIF (variance inflation factor) under the number 10 means no multicollinearity. (John, 2011).
Heterokedastisitas means a variant variable is not the same for all observations. Heterokedastisitas means is a state where the data contain elements of cross section data and has a variant that is not the same. To determine whether the data used in the regression model to meet the assumptions do not occur heterokedastisitas is to look at the SPSS output on the scatterplot dependent variable, if the values of the distribution of the data there is no clear and points spread above and below the number 0 on the Y axis, it is not occur heterokedastisitas (Imam Ghozali: 2011)
This method is used to get the influence of independent variables on the dependent variable. In general, multiple linear regression equation is given as beriku;
RESULTS AND DISCUSSION
Respondents in this study are members KSRN SMEs located in Jember. Of the entire questionnaire obtained there are several descriptions that can be used to describe the sample used. Table 3 is a description of the respondents by sex. Note here that most of the respondents are women, and only a few were men. This suggests that members of the SME respondents KSRN dominated by the female sex.
Progress Conference Volume 2 Number 2, August 2019| 72 Table 3 Description of Respondents
No. Gender amount %
1. Male 23 46%
2. woman 27 54%
amount 50 100%
Source: Research Questionnaire Results
Furthermore, Table 3 shows the description of the respondents based on roles that are executed in this KSRN SMEs. From this table it is known that the majority of the resellers, while a small part is the Seller. The difference between them is in the ownership of the product, if the Seller is a manufacturer and Reseller only sell the goods. But both have the same opportunity to promote the product in several e-commerce platform.
Table 4 Description of Respondents According to a role in SME KSRN
No. Gender amount %
1. seller 10 20%
2. Reseller 30 60%
3. Seller & Reseller 10 20%
amount 50 100%
Table 5 Description of Respondents by Age
No. Gender amount %
1. 20 to 30 years 5 10%
2. 30 to 40 years 30 60%
3. Over 40 years 15 30%
amount 50 100%
Table 6 Description of Respondents by Education
No. Gender amount %
1. SMP 0 0%
2. High School 37 74%
3. Bachelor 13 26%
amount 50 100%
Further Tables 5 and 6 show on a description of respondents by age and education by SMEs this KSRN. From this table it is known that the majority of aged diatara 30 to 40 years, as well as school education (SMA), while a small part aged 20 to 30 years and above 40 years and are educated to degree is very minimal, so should the effort to help them manage more both SMEs KSRN order to increase sales volume.
CollectionData research done by giving questionnaires to 50 respondents. For further data that has been analyzed with the aid of a computer program SPSS for Windows Release 24.0. Validity test is done to determine the extent to which a proposed questionnaire to collect data or information required. Validity test results be valid if r count larger than r tables in this study amounted to 0.2306 r table with number n = 50 respondents and obtained the test results for each variable as follows:
Table 7 Validity Testing Results No
.
variables questionnaires r count
Sig result 1 Product
Variations (X1)
a. Statement 1 (P1) b. Statement 2 (P2) c. Statement 3 (P3) d. Statement 4 (P4) e. Statement 5 (P5)
0450 0.666 0.728 0.763 0,711
0,001 0,000 0,000 0,000 0,000
valid valid valid valid valid
2 Posting and
Updating (X2)
a. Statement 1 (P1) b. Statement 2 (P2) c. Statement 3 (P3) d. Statement 4 (P4) e. Statement 5 (P5)
0673 0.644 0.791 .780 0.762
0,000 0,000 0,000 0,000 0,000
valid valid valid valid valid 3 Keyword (X3) a. Statement 1 (P1)
b. Statement 2 (P2) c. Statement 3 (P3) d. Statement 4 (P4) e. Statement 5 (P5)
0780 .784 0,836 .857 .864
0,000 0,000 0,000 0,000 0,000
valid valid valid valid valid 4 Sales volume (Y) a. Statement 1 (P1)
b. Statement 2 (P2) c. Statement 3 (P3) d. Statement 4 (P4) e. Statement 5 (P5)
0788 .788 .814 .638 0,711
0,000 0,000 0,000 0,000 0,000
valid valid valid valid valid Source of data: The results of the questionnaire with SPSS data processing
Results of testing the validity of the questionnaire in this study indicate that the majority of items in the statement of each variable its count r> r table with a significant level below 5%. Reliability test is performed to measure the extent to which the questionnaires can give different results by using Cronbach Alpha formula. Obtained the test results for each variable as follows:
Table 8 Reliability Testing Results No
.
variables Cronbach's Alpha
All Variables
result
1. Product Variations (X1) .713 reliable
2. Posting and Updating (X2) 0.776 reliable
3. Keyword (X3) 0,880 reliable
4. Sales volume (Y) 0.805 reliable
Source of data: The results of the questionnaire with SPSS data processing
Results of testing the reliability of the questionnaire in this study indicate that the majority of highly reliable variables shown in the results of Cronbach's Alpha> 0.6 means realibel. So we can conclude all the concept of measuring each of the variables of the questionnaire used in this study is a reliable questionnaire.
A good regression model should be free from the problem of deviation from the classical assumptions or basic assumptions. Here are the results of testing against the classical assumption in the regression model.
Progress Conference Volume 2 Number 2, August 2019| 74 There are three multiple linear regression model used in this study in accordance with the objectives that have been formulated. The following regression models were generated using SPSS. The first regression model is a regression model is generated by using motivation as the dependent variable.
This model is a test that is used to address the effect of each independent variable on the dependent variable motvasi. Following the model in question.
Y = -0.017 + 0,362X1 + 0,340X2 + 0,266X3
From the results of the multiple linear regression equation can be described as follows namely constant value of -0.017 indicates that motivation will be equal to -0.017 if the independent variable coefficients equal to 0. Then the coefficient of variation of the product (X1) of 0.362 (positive showing unidirectional relationship) states that each increment of 1 (one) value of direct compensation would increase the motivation of 0.362 and conversely any decrease of 1 (one) value will decrease the motivation of 0.362 assuming other independent variables constant or fixed. And the posting and updating coefficient (X2) of 0.340 (positive showing unidirectional relationship) states that each increase of 1 (one) value of indirect compensation would increase the motivation of .340 and vice versa every decrease of 1 (one) value will decrease the motivation of 0, 340 assuming other independent variables constant or fixed. As well as the coefficient of keywords (X3) amounted to 0,266 (positive showing unidirectional relationship) states that each increase of 1 (one) the value of non-financial compensation will increase the motivation of 0,266 and conversely any decrease of 1 (one) value will decrease the motivation of 0,266 assuming the other independent variables constant or fixed
Normality Tests conducted on the regression residuals. Testing is done by using the graph Probability Plot. Normal data is data that form dots that spread not far from the diagonal line, if the values of the data distribution located around the diagonal straight line normality requirements are met. (Singgih Santoso, 2012: 361).
Probability Plot graphs for the regression equation states that hOutcome data normality test showed normal graph pattern in which the dots are not far from the diagonal line, this means that the regression model is already normal distribution. The image above to the same conclusion with Probability normal distribution plot wherein the data points follow a diagonal line. To be more confident we tested with the Kolmogorov-Smirnov test whose results claim that residues of this regression model with a normal distribution Asymp. Sig. (2-tailed) of 0.200> 0.05.
A variable symptoms multikolinieritas can be seen from VIF (Variance Inflation Factor) is high on the independent variables of a regression model. VIF greater than 10 showed symptoms of multicollinearity in the regression model. (Sugiyono, 2009: 139). So for the data is said to be free of multikolinieritas if VIF is under 10. The test results are shown in collinearity statistics to find VIF is presented as follows:
Table 10 Testing Results Multicollinearity No. variables tollerance VIF result 1. Product Variations
(X1)
.846 1,182 Non Multicollinearity 2. Posting and Updating
(X2)
0.847 1,180 Non Multicollinearity 3. Keyword (X3) .992 1,008 Non Multicollinearity Source: The results of the questionnaire with SPSS data processing
Table 11 Results Multicollinearity Coefficientsa
Model
Coefficients unstandardized
standardize d
Coefficients
T Sig.
collinearity Statistics B
Std.
Error beta tolerance VIF
1 (Constant) -, 017 , 936 -, 018 , 986
Product X1_Variasi 362 , 170 , 283 2,130 , 039 , 846 1,182
X2_Posting and
Updating
, 340 , 136 , 333 2,506 .016 , 847 1,180 X3_Keyword , 266 , 119 , 273 2,230 , 031 , 992 1,008 a. Dependent Variable: Sales Y_Volume
The test results showed that all of the variables used as predictors of the regression model showed VIF were quite small, all of which are under 10 and more than 0.1 tollerance value. This means that the independent variables used in the study did not show any symptoms of multicollinearity, which means that all independent variables in this study are mutually independent variables.
Heteroskedastisitas testing done using Scatter Plot. If there is a significant variable, it can be concluded that the absence of problems in the heteroscedasticity. The test results showed no heteroscedasticity clear pattern of the dots. This shows that the regression model did not have symptoms of heteroscedasticity, which means that no significant interference in this regression model.
The purpose of the test is to test the hypothesis t partially or individual for each variable through model testing. The test results are shown in Table 5.9. From this it is known that each independent variable partially significant effect on the dependent variable in this study. So that the tentative conclusions obtained from this test is variable variation of the product (X1) significant effect partially to variable sales volume (Y) with a significance value of 0.035, and a variable posting and updating (X2) significant effect partially on sales volume (Y) with sig. 0,000, as well as the keywords field variables (X3) partially significant effect on sales volume (Y) with a significance value of 0.000.
Table 12 Regression Equations t test results Coefficientsa
Model
Coefficients unstandardized
standardized Coefficients
t Sig.
B
Std.
Error beta
1 (Constant) -, 017 , 936 -, 018 , 986
Product X1_Variasi 362 , 170 , 283 2,130 , 039 X2_Posting and
Updating
, 340 , 136 , 333 2,506 .016
X3_Keyword , 266 , 119 , 273 2,230 , 031
a. Dependent Variable: Sales Y_Volume
Table 12 shows the results of the t test for multiple linear regression equation in which the variable variation of the product (X1), posting and updating (X2), and keyword (X3) affect the volume of sales (Y) partially. al is indicated by the value of the significance of each independent variable is less than 0.05.
Progress Conference Volume 2 Number 2, August 2019| 76 For this regression equation the result is that Sig. 0,001 less than 0.05, we conclude that there is significant influence between the variables of product variations (X1), posting and updating (X2), and keyword (X3) on sales volume (Y) simultaneously.
Table 13 Anova SPSS Calculation Results ANOVAa
Model
Sum of
Squares Df mean Square F Sig.
1 Regression 4.282 3 1,427 7.010 , 001B
residual 9.366 46 , 204
Total 13.649 49
a. Dependent Variable: Sales Y_Volume
b. Predictors: (Constant), X3_Keyword, X2_Posting and Updating, X1_Variasi Products
Adjusted R Square of 0269 is relatively small which indicates that the regression is not patterned linearly as sales volumes not only influenced by the fundamentals of the company but also influenced by external conditions.
Table. 14 Results SPSS Model Summary Model Summaryb
Model R R Square
Adjusted R Square
Std. Error of the Estimate
1 , 560a , 314 , 269 , 45 124
a. Predictors: (Constant), X3_Keyword, X2_Posting and Updating, X1_Variasi Products
b. Dependent Variable: Sales Y_Volume
The estimation results obtained from the regression model illustrates that linearly only able to approach reality or the phenomenon by 26.9% due essentially the determining factor that affects the sales volume itself heavily influenced by factors internal and external to the company, natural disasters, social relationships , company policies, including monetary policy, and other information that develops community-acquired predicted to affect the sales volume itself.
The residual amount of statistically related to the adjusted R Square or adj-R2 in Table Model Summary. The R2 is formulated with: "A minus (divided Residual Sum of Squares Sum of Squares Total" or:
Sum of Square Residual R Square = 1 -
Sum of Square Total
Results obtained according to formulas and tables Model Summary, namely:
9366
R Square = 1 - = 0.314 13 649
Based on residual formulation linkage and R2 show that a high residual amount will reduce the amount of R2. Residual obtained from the sum ei2, where ei = (Yi - Yi estimate). With this formula, the residual high as the results of this study are automatically meperkecil value of R2.
High Residual reflect that the magnitude of the difference between the result data pengatan depedent variable with dependent variable estimates corresponding multiplication result of the regression equation with independent variable observational data.
The grounds of measurement accuracy, then adjustments formulation R2 or adjusted R Square (adjusted-R2) with the formula:
The results obtained according to the formulations and Table Model Summary, namely:
Adjusted R Square = 1 - {(1-0,314) (50-1) / (50-3-1)} = 0.269
From the research data above we can see that respondents are mostly resellers where they do not produce themselves the results of SME, but merely as an intermediary, it indicates peru training for many aggota ukm ksrn to be a seller or produce goods and sell them yourself in order the value of goods sold to bigger profits.
In terms of age known to most of them aged between 30 and 40 years who are need a lot of assistance in the process all the results of SME, while in terms of the education they mostly berpenddikan high school in this case they basically had enough imu but still need assistance of the SMEs who have more experience and a master of science in information technology in order to balance industry competition 4.0.
This discussion relates to the results of testing the first hypothesis stating that the product variations significantly influence the sales volume. Proof of this hypothesis is done with the data analysis using multiple linear regression. The analysis showed that the variation of the product did not significantly affect sales volumes partially. This is shown by the results of the t test where the Sig.
by 0,0,039> α (0.05).
This discussion relates to the test results of the second hypothesis which states that the posting and updating a significant effect on the sales volume. Proof of this hypothesis is done with the data analysis using multiple linear regression. The results show that post and updatig significant effect on the sales volume partially. This is shown by the results of the t test where the Sig. amounting to 0,016> α (0.05).
This discussion relates to the test results of the third hypothesis stating that keyword a significant effect on the sales volume. Proof of this hypothesis is done with the data analysis using multiple linear regression. The analysis showed that keyword significantly influence sales volumes partially.
This is shown by the results of the t test where the Sig. amounted to 0.031> α (0.05).
This discussion relates to the test results of the fourth hypothesis which states that the product variety, posting and updating, as well as keyword significant effect on the sales volume. Proof of this hypothesis is done with the data analysis using multiple linear regression. The analysis showed that the product variety, posting and updating, as well as keyword significant effect on the sales volume. This is indicated by the F test results in which the value of Sig. 0,001> α (0.05).
CONCLUSION
From the explanation above there are several conclusions that variabel variety of products (X1) partially significant effect on the sales volume variable (Y) with a significance value of 0.039, and the posting and updating variables (X2) partially significant effect on sales volume (Y) with sig.
amounted to 0,016, including keywords field variables (X3) partially significant effect on sales volume (Y) with a significance value of 0.031. It is also the same as the final results of the variable product variations (X1), Posting and Updating (X2), and keyword (X3) affect the volume of sales
Progress Conference Volume 2 Number 2, August 2019| 78 (Y) simultaneously with significant value 0,001. From the above results it can be concluded that sales to account e-commerce organizations were able to increase sales volumes with many aspects to consider, that is by producing multiple variations of a product to sell, this is because consumers or prospective buyers tend to always see a wide selection of existing products before decided to buy one of the items they want and as often as may be posted and updated every product that is owned, it is intended that every item that we have and ready to sell always exist in your organization's account, first make prospective buyers do not be disappointed if the desired item was in accordance with what is sought, this includes the importance of the use of keywords or keyword that is relatively easy and a trend search should also be considered,considering that every consumer is unlikely look for keywords or keyword with an uncommon word.
REFERENCES
Lestari, L. (2007). Pengaruh E-Commerce Terhadap Volume Penjualan Pada Online Shop Aneka Kemeja Pria Bekasi. Skripsi. Universitas Pakuan.
Miftah, S., Maulana, h. S., & Riyadi. (2015). Implementasi E-Commerce sebagai Media Penjualan Online (Studi Kasus Pada Toko Pastbrik Kota Malang). 13(3), 123-134.
Pradiani, T. (2008). Pengaruh Sistem Pemasaran Digital Marketing Terhadap Peningkatan Volume Penjualan Hasil Industri Rumahan. Jurnal Ilmiah Bisnis Dan Ekonomi Asia, 11(2), 46-53.
Rangkuti, F. (2009). Dalam Strategi Promosi Yang Kreatif dan Analisis Kasus Integrated Marketing Communication. Jakarta: PT Gramedia Pustaka Utama.
Rerung, R. R. (2018). E-Commerce Menciptakan Daya Saing Melalui Teknologi Informasi. Dalam Deepublish. Yogyakarta.
Sholihin, M. R., Arianto, W., & Khasanah, D. F. (2018). Keunggulan Sosial Media Dalam Perkembangan Ekonomi Kreatif Era Digital Di Indonesia. Proding Ekonomi Kreatif Era Digital, (hal. 149-160). Jember.
Siregar, S. (2009). Dalam Metode Penelitian Kuantitatif. Jakarta: PT Fajar Interpratama Mandiri.
Sudaryanto, R., & Wijayanti, R. R. (2012). Strategi Pemberdayaan UMKM Menghadapi Pasar Bebas ASEAN. E-Jurnal UIN Jakarta, 4(10), 1012-1044.
Sugiyono. (2009). Metode Penelitian Kuantitatif, Kualitatif dan R&D. Dalam Alfabbeta. Bandung.
Widagdo, H. (2011). Analisis Pengaruh Kualitas Layanan Dan Promosi Terhadap Keputusan Konsumen Membeli Komputer Pada PT XYZ Palembang. Jurnal Ilmiah STIE MDP, 1(1), 1-10.
Wijaya, M., & Jasfar, F. (2014). Pengaruh Rancangan Situs, Harga, Kepercayaan dan keamanan Terhadap Pembellian Produk Fashion Melalui Online Shopping. Jurnal Manajemen Dan Pemasaran Jasa, 7(2).
