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Vilnius Gediminas Technical University, Vilnius, Lithuania

Received 02 March 2019; accepted 26 March 2019

Abstract. Digital retail (online retail or e-commerce) sector is continuously expanding its stake in the global economy each
year. According to the statistics, online retail share of the total global retail sales takes approximately 11.9% in 2018 and is
expected to reach 17.5% at the end of 2021. The same pattern of rapid growth was noticed more than 18 years ago when
a burst of dot-com bubble crashed many of the internet-based online shopping companies. “Growth over profits” mental-
ity and overestimated perception of the magnitude of online sales resulted in a superficial understanding of the business’
financial performance. Because of that, it is highly necessary to analyze and adequately evaluate the financial performance
of digital retail companies. Thus, the purpose of this article is to investigate the top 4 digital retail companies’ financial
performance by applying multiple criteria decision analysis (MCDA) TOPSIS and SAW methods to demonstrate that sales
turnover is not the only and the prime measure to evaluate the successful company’s financial performance.

Keywords: financial performance, digital retail, digital transformation, online retail, e-commerce, MCDA, TOPSIS meth-
od, SAW method.


Digitalization or digital transformation is a significant
trend in nowadays business world. Digital transforma-
tion and transition to digitalization are nearly in all the
services of our globalized economy. One of the fields that
are affected by digital transformation the most is the retail
industry. E-commerce sales have been growing rapidly in
the past couple of years. According to the statistics, digital
retail sales increased from 1 336 billion USD in 2014 to
2  304 billion USD in 2017 (72% growth) and is expected
to grow up to 4 878 billion USD by the end of 2021 (265%
growth) (Statista, 2018a, 2018b).

The growing share of this business sector and the po-
tential future impact to the economics emphasises the
need for proper investigation and evaluation of the finan-
cial performance of this sector players. Sales turnover is
a widely used financial indicator of the company’s per-
formance and the magnitude of the business. However,
this traditional performance determinant might not be the
most adequate measurement to evaluate digital company’s
success due to the vastly increasing digital transformation
process and its impact to the business. Thus, identifying

adequate success factors is a crucial matter to evaluate suc-
cessful business performance.

This scientific article aims to assess the top 4 digital re-
tail companies financial performance using 2 of the multic-
riteria decision analysis (MCDA) methods  – TOPSIS and
SAW – in order to demonstrate that sales turnover is not
the only and the prime measure to evaluate the success-
ful company’s financial performance and to determine
which one is the most successful business.

The research conducted in this scientific article is lim-
ited to digital (or e-commerce) retail companies. E-com-
merce company is defined as a company that does most
of its business on the Internet. It excludes Internet service
providers or other information technology companies.

1. Theoretical background

1.1. Digital transformation effect to the business

Digital transformation (digitalization or digitization) is
a trending process of integration of digital technologies
into all areas of a business. This transformational process

Economics and Management
Ekonomika ir vadyba

Mokslas – Lietuvos ateitis / Science – Future of Lithuania
ISSN 2029-2341 / eISSN 2029-2252

2019 Volume 11, Article ID: mla.2019.9737, 1–9

*Autorius susirašinėti. El. paštas [email protected]

K. Urbonavičiūtė, N. Maknickienė. Investigation of digital retail companies financial performance using multiple criteria…


and its effect to business have been widely investigated by
many authors in their scientific papers. Jürgen Meffert and
Anand Swaminathan (2018) agreed that companies that
adopt digital technologies in their business would retain
their leadership and leverage their strengths. Companies
that want to digitalize successfully can either improve their
current business model and processes, add new streams
of revenue to their business model, or replace their old
business models with the new ones. However, since digital
transformation is a complicated process, authors C. Matt,
T. Hess and A. Benlian in their scientific article Digital
Transformation Strategies (2015) argue that increasing
digitalization of business processes makes it necessary to
develop a better understanding of digital business trans-
formation strategies. It is essential to set a clear approach
and assign adequate responsibilities for implementation of
such conversion change in the business. One of the core
elements which helps the company to differentiate itself
from the competitive environment and to create addition-
al value, according to S. Mithas, A. Tafti and W. Mitchell
(2013), is an investment into general information technolo-
gies and IT outsourcing. Moreover, according to a survey
which was conducted by MIT Sloan Management Review
and Capgemini Consulting to investigate how businesses
succeed or fail in using digital technology to improve busi-
ness performance it was revealed that 78% of respondents
admitted that achieving digital transformation will be-
come critical to their organization within next two years.
The results of the survey indicated that managers believe
that digital technology will bring transformative change to
business (Fitzgerald, Kruschwitz, Bonnet, & Welch, 2013).

1.2. Online retail or “e-tail” concept

One of the fields that are affected by digital transforma-
tion the most is the retail industry. A term online retail
or “e-tail” actually covers retailing using a variety of dif-
ferent technologies or media (Chen & Leteney, 2000). Ac-
cording to the World Trade Organization (World Trade
Organization, 2018), e-commerce concept is described as

“commercial transactions that are digitally-ordered and
either digitally or physically delivered.” Many retail firms
that have traditionally operated solely in the store channel
(or offline) have been transforming their business process-
es to engage with customers in the online channel. This
strategic realignment is triggered by the rapid increase in
online retail sales that has grown at a faster rate than in-
store sales (Ishfaq, Defee, Gibson, & Raja, 2016).

The online channel is an information-wealthy and
cost-effective channel for product placement. It provides
consumers with detailed product information worldwide
(Rapp, Baker, Bachrach, Ogilvie, & Beitelspacher, 2015).
Consumers are provided by the availability to reach the
online site and search for product information anywhere
without being bordered by time and place. The most sig-
nificant advantage against offline channels is that consum-
ers can more easily compare information between various
products on the Internet (Zhu, Goraya, & Cai, 2018). Due
to its many distinctive advantages, online retail continues
to grow. Darrell Rigby (2011) also agrees that digital retail-
ing will continue to grow fast because of the vast selection
of goods, the reasonable prices, the convenience of shop-
ping from home, and the access to product reviews and
recommendations. Comfortable shopping, 24/7 conveni-
ence, reducing dependence to visit physical stores, travel
costs savings, reasonably quick delivery, secure payment,
a wide range of products and personalization services,
are only a few of many reasons why consumers choose to
shop online over traditional retail options.

1.3. Online retail market overview

The online retail market overview is restricted to digital
(or e-commerce) retail companies only. E-commerce com-
pany is defined as a company that does most of its busi-
ness on the Internet. It excludes Internet service providers
or other information technology companies. According
to revenue (total sales turnover), the top 4 online retail
companies are Amazon, Inc,, Inc, Alibaba Group
Holding Ltd and eBay, Inc (Table 1).

Table 1. Top 8 digital retail companies1 in the world according to turnover (in millions US$)2

Company Country 2017 2016 2015 2014

Amazon, Inc USA $177 866 $135 987 $107 006 $88 988, Inc China $55 641 $37 167 $27 880 $18 537
Alibaba Group Holding Ltd.3 China $22 965 $15 686 $12 293 $8 463
eBay, Inc USA $9 567 $8 979 $8 592 $8 790
Rakuten, Inc Japan $8 407 $7 123 $5 896 $5 690
Zalando SE Germany $5 377 $3 834 $3 232 $2 691
ASOS plc UK $2 595 $1 777 $1 706 $1 515
B2W Companhia Digital Brazil $2 148 $2 641 $2 308 $ 2 964

1 Compiled by the author according to financial data from the official firms’ annual reports.
2 In cases were financial information was stated in other currency than US Dollars (USD), the figures were converted using the year-

end-date FX exchange rate stated in
3 Financial year of Alibaba Group Holding Ltd. ends as of 31st March.

Mokslas – Lietuvos ateitis / Science – Future of Lithuania, 2019, 11, Article ID: mla.2019.9737


The top 1 place is firmly occupied by e-commerce
company Amazon also known as Founded
in 1994 in Seattle by Jeff Bezos, Amazon has become a
household name when it comes to online shopping. This
internet company today has the most substantial revenue
and is considered as the biggest employer of all the internet
companies with a workforce of more than 566 thousand
employees. Jingdong or is an e-commerce company
operating in Beijing. Jingdong has well over a quarter of a
billion registered users as of 2018. It was founded in 1998
and started trading online six years later. Alibaba is the
biggest e-commerce company in Asia with headquarters in
Hangzhou; China has more than a billion users worldwide.
Jack Ma, the founder of Alibaba, was rejected from more
than 30 job posts in the early 1990s when he started mak-
ing websites for companies with his wife and a friend. The
business grew exponentially, and in the year 1999, Alibaba
Group was founded. Alibaba has two major portals that run
under it, Alibaba and AliExpress. eBay is an e-commerce
company which was founded in 1995 by a computer pro-
grammer named Pierre Omidyar. It was one of the first suc-
cessful dot-com bubble companies that epitomized online
shopping. Its most distinctive feature is the online auction
feature, alongside a conventional buy-it-now shopping op-
tion. These top 4 digital retail companies will be further
assessed in this scientific article.

1.4. Factors affecting a firm’s financial performance

Successful financial business performance can be de-
scribed in various ways; it depends on a company’s strat-
egy and management goals. However, some widely used
performance indicators truly reflect a positive and a lead-
ing to success business. The main factors that affect a firm’s
financial performance and that are going to be analyzed
in this paper are:

– The company’s stock price growth
– Revenue
– Gross Profit margin
– Net Profit margin
– Return on Assets (ROA)
– Return on Equity (ROE)
– Return on Sales (ROS)
– Cost of investment in digital technologies
– GDP per capita of the country of the headquarters
– Total ESG score

The stock price is one of the main factors of the suc-
cessful company’s financial performance. Fluctuations in
a stock price not only brings gain or loss to the investors
but also can indicate triggers in business performance.
The stock market wholly and quickly incorporates public
information into the stock price. Thus, the evaluation of
the company’s market share price growth can indicate the
success of a business in a given market condition.

Ratio analysis is perhaps the most commonly used tool
in financial analysis. Financial ratios allow to assess and
analyze the strengths and weaknesses of a given company
about such measures as liquidity, profitability, perfor-

mance, and growth and compare them to other companies
in the market or an industry standard (Hitchner, 2011).
Additionally, profitability ratios are also widely used by
the investors of the company since they help to measure
and evaluate the ability of a company to generate income
(or profit) relative to revenue, assets, operating costs, and
shareholders’ equity during a specific period. Ratios in-
dicate how well a firm utilizes its assets to produce profit
and, thus, create value to shareholders (Corporate Finance
Institute, 2018).

Profitability ratios that will be used in this paper to
analyze the previously identified research object are:

1 00%

Gross Profit
Gross Profit Margin x

= ; (1)

1 00%

Net Profit
Net Profit margin x

= ; (2)

( ) Operating profitReturn on Sales ROS

= ; (3)

( )

Net Profit
Return on Assets ROA

Total Assets
= ; (4)

( ) Net ProfitReturn on Equity ROE
Owners Equity′

= . (5)

Gross Profit margin compares gross profit to sales
revenue. It shows how much a business is earning,
considering the needed costs to produce its goods and
services. Net Profit margin is the bottom line and takes
everything into account. It provides the final picture of
how profitable a company is after all expenses including
interest and taxes. Return on Sales (ROS) is an opera-
tional efficiency ratio. This measure provides informa-
tion on how much profit is being generated per dollar of
the company’s sales. Return on Assets (ROA) expresses
a percentage of the company’s net revenue in relation
to the total assets. ROA ratio shows how much after-tax
profit a company generates for 1 dollar of assets it has.
Return on equity (ROE) indicates the rate of return of 1
dollar that the company’s shareholders have invested in
the business.

As revenue is used in the majority of profitability ra-
tios and traditionally is one of the primary measures when
evaluating a company’s magnitude and selling power, it
will also be assessed separately as one of the influencing
factors of business financial performance.

Majority of different companies are focusing on dig-
italization and trying to find a way forward to develop
business cases for such technology adoption. Mithas, Tafti,
and Mitchell (2013) in their article How a Firm’s Com-
petitive Environment and Digital Strategic Posture Influence
Digital Business Strategy discuss how the competitive in-
dustry environment shapes the way that digital strategic
posture influences firms realized digital business strategy.
According to the authors, increasing digitization of busi-
ness processes, products, and services makes it impera-
tive to develop a better understanding of digital business
strategies. Digital strategies such as investments in general

K. Urbonavičiūtė, N. Maknickienė. Investigation of digital retail companies financial performance using multiple criteria…


information technology and IT outsourcing are significant
elements of overall business strategy. However, consider-
able investments in the digital environment and the devel-
opment of online platforms might lead to high operating
expenses. Hence, digital companies might provide lower
prices to the customer and significantly increase sales
turnover but suffer in operating expenses regarding digi-
tal technologies maintenance costs. Because of that, cost
of investment in digital technologies is a crucial factor to
evaluate successful business performance.

Another influencing factor of the company’s finan-
cial performance on the level of economic development
measured as Gross Domestic Product per capita (GDP per
capita). Tim Jackson (2009) in a book Prosperity Without
Growth. Economics for a Finite Planet discuss the GDP per
capita and its value to economic growth. According to the
author: “The GDP is broadly speaking a measure of “eco-
nomic activity” in a nation or region. The GDP counts the
economic value of goods and services exchanged on the
market. If we’re spending our money on more and more
commodities, it’s because we value them. We wouldn’t
value them if they weren’t at the same time improving our
lives. Hence a continually increasing per capita GDP is a
reasonable proxy for rising prosperity”. GDP per capita
identifies a citizen ability to consume more, and higher
incomes mean increased choices, more prosperous lives,
improved quality of life for those who benefit from them.
Thus, a successful business adds its part to a better GDP
per capita measure and vice versa – high GDP per capita
can also influence a better performance of a company in
the country that it operates in.

The last criteria that could be analyzed as one of the
indicators of a successful company’s performance is the
environmental, social and governance (ESG) rating. Be-
ing a sustainable business, which also shows a social re-
sponsibility is necessary in the business world. ESG rat-
ings measure how well companies proactively manage the
environmental, social and governance issues that are the
most material to their business and provide an assessment
of companies’ ability to mitigate ESG risks. The ESG rating
is a quantitative score on a scale of 1–100 and is catego-
rized across five risk levels: negligible, low, medium, high
and severe. Most international and domestic public (and
many private) companies are being evaluated and rated
on their ESG performance. Institutional investors, asset
managers, financial institutions and other stakeholders are
increasingly relying on these reports and ratings to assess
and measure company ESG performance over time and as
compared to peers. Hence, ESG rating is also one of the
factors that could influence a company’s success (Sustai-
nanalytics, 2018).

The criteria mentioned above will be further used for
application of multiple criteria decision analysis methods
to assess which one of the selected top 4 digital retail com-
panies’ is operating the most successfully.

2. Multiple criteria decision analysis (MCDA)

To assess the criteria and their effect on a project (or alter-
native) multiple criteria decision analysis (MCDA) meth-
ods (or multicriteria methods) are widely used. TOPSIS
and SAW methods will be further analyzed and applied
in this article to evaluate the financial performance of the
top 4 digital retail companies.

2.1. TOPSIS method

TOPSIS (technique for order preference by similarity to
ideal solution) method is a popular approach to multiple
criteria decision analysis (MCDA) developed by Hwang
and Yoon. TOPSIS has been widely used to rank the pref-
erence order of alternatives and determine the optimal
choice (T. Y. Chen & Tsao, 2008). The positive ideal solu-
tion is a solution that maximizes the benefit criteria and
minimizes the cost criteria, whereas the negative ideal
solution maximizes the cost criteria and minimizes the
benefit criteria. The best alternative is the one with a value
which is closest to the positive ideal solution and has the
farthest distance from the negative ideal solution (Wang
& Elhag, 2006).

The TOPSIS method can be applied using several cal-
culation steps: the first step is gathering the performance
values of the alternatives according to the criteria set. The
second step is performance values normalization. Then,
normalized values need to be weighted (multiplied by the
weights of the criteria), and the distances to the positive
ideal and negative ideal solutions are calculated. Lastly, the
relative closeness to the positive ideal solution is indicated,
and ranking of the alternatives is performed (Ishizaka &
Nemery, 2013).

These steps are detailed explained below:
1. Construct the decision matrix and determine the

weight of criteria:

( ) ijx x= . (6)
2. Normalize the decision matrix:


ij m





. (7)

3. Calculate the weighted normalized decision matrix:

ij j ijw nν = (8)

for 1, , ; 1, , .i m j n= … = …
the weight of the th criterion.jw j− −

4. Determine the positive ideal and negative ideal so-

( )1 2, , , max , min ; n ij ij

V j I j J+ + + +

    = ν ν … ν = ν ∈ ν ∈   
   


Mokslas – Lietuvos ateitis / Science – Future of Lithuania, 2019, 11, Article ID: mla.2019.9737


( )1 2, , , min , max ,n ij ij
i i

V j I j J− − − −
   = ν ν … ν = ν ∈ ν ∈    
    


where I is associated with benefit criteria and J with the
cost criteria, i = 1, …, m; j = 1, …, n;

5. Calculate the separation measures from the positive
ideal solution and the negative ideal solution:

( )2

, 1, , ;

i ij i

S i m+ +

= ν −ν = …∑ (11)

( )2

, 1, 2, , .

i ij i

S i m− −

= ν −ν = …∑ (12)

6. Calculate the relative closeness to the positive ideal

. ii
i i



− +


7. Rank the alternatives according to the relative close-
ness to the ideal solution. The bigger the Pi, the bet-
ter the alternative. The best alternative is the one
with the highest relative closeness to the ideal solu-

TOPSIS is considered as a very understandable and
straightforward method. However, the drawback of this
method is that the extreme value of the criteria might be
preferred more than the compromise one and it might
provide illogical results. Thus, another MCDA method is
being used to assess and rank the top 4 digital retail com-
panies financial performance results.

2.2. SAW method

Simple Additive Weighting (SAW) method is often also
known as weighted summing method. The basic concept
of SAW method is to find the weighted sum of perfor-
mance ratings on each alternative on all attributes. The
SAW method requires the process of normalizing the de-
cision matrix (Xij) to a scale comparable to all existing
alternative ratings (Anggraeni, Huda, Maseleno, Safar, &
Jasmi, 2018).

1. The sum Sj of the weighted normalized values of
all the criteria is calculated for the j-th object. The
alternatives are then ranked according to the calcu-
lated values Sj from the largest value to the lowest
one. The largest value of the sum Sj reflects the best



j i ij

S w r

= ∑


wi – weight of the i – th criterion
normalized th criterion’s value for th object; 1, , ; 1, , ijr i j i m j n− − − = … = … – normalized i – th criterion’s value for j – th object;

i = 1, …, m; j = 1, …, n
m – the number of the criteria used
n – the number of the criteria used (alternatives)

2. One of the limitations of the SAW method is that it
can only be used when all the criteria are maximiz-
ing. Thus, if the criteria are minimizing, this can be
implemented by converting the criteria to maximiz-
ing ones using the below the formula. In this way,
the minimal criteria value minij ij

r r= acquires the

largest value equal to unity:





= (15)

rij – i – th criterion’s value for j – th alternative,
min the smallest th criterion’s value for all the alternatives comparedij

r i− −

– the smallest i – th criterion’s value for all

the alternatives compared,
denotes the converted valuesijr −– detonates the converted values.

3. Normalization of the initial data is performed in
order to the largest maximizing value of the criteria
value would get the largest value equal to unity. The
formula used for maximizing criteria is below:





= (16)

max the largest th criterion’s value of the alternatives compared. ij

r i− −– the largest i – th criterion’s value of the
alternatives compared.

4. Another drawback of the SAW method is that all
criteria values rij should be positive. In cases when
there are negative values of the criteria used, these
values are transformed into positive ones using the
formula below (Podvezko, 2011):

min 1 .ij ij ij

r r r= + + (17)

SAW method is a very easy-to-apply method and is
very commonly used in alternatives ranking. However,
due to its drawbacks mentioned above and usage of trans-
formational steps might distort the ranking results.

3. Investigation of top 4 digital retail companies’
financial performance

3.1. Criteria

Ten criteria determined as factors influencing a company’s
financial performance will be further used in applying the
selected methods (Table 2).

Criterion C8 will be set as a cost criterion because
investment in digital technologies, product and content
development, and online platforms requires a lot of ex-
penses. Even though it brings efficiency to a company’s
processes and more qualitative services, however, the
company should be focused on how to reduce such ex-
penses since it affects the profitability of a business. The
remaining nine success factors will be set as benefit crite-
ria because usually a firm would concentrate on the maxi-
mization of these determinants and the higher the result
of the factor, the better performance of a company. Factor
C10 was measured according to the ranking of ESG rat-
ings, applying for the position number according to five

K. Urbonavičiūtė, N. Maknickienė. Investigation of digital retail companies financial performance using multiple criteria…


risk levels: 1  – negligible, 2  – low, 3  – medium, 4  – high
and 5 – severe (Sustainanalytics, 2018).

3.2. Application of the TOPSIS method

First, the decision matrix was constructed of the select-
ed alternatives (top 4 digital retail companies: Amazon.
com,,, and and 10 fac-
tors (or criteria) identified in the previous section. More-
over, weights of the criteria were calculated. In order to

have a fair and not subjective view, the weights to the
criteria were equally set to 0,1 summing up to a total of
1 (Table 3).

Secondly, a normalized decision matrix was computed
(Table 4). Normalization was performed by dividing each
criteria value by the square root of the squared sum of the
total criteria values.

Then the weighted normalized decision matrix was
calculated (Table  5) by multiplying identified weights by
each value of the normalized decision matrix cell.

Table 2. Data of the top 4 digital retail companies4 (source: Alibaba Group Holding Ltd., 2019; Amazon, Inc., 2019;
eBay, Inc., 2019;, Inc., 2019; The World Bank, 2019; Yahoo Finance, 2018)1


The company’s stock price growth in 2017 (%) 55% 60% 95% 27%
Revenue (millions $) 177 866 55 641 22 965 9 567
Gross Profit margin (%) 37% 14% 62% 77%
Net Profit margin (%) 2% 0% 28% –11%
Return on Assets (ROA) (%) 2% 0% 30% 24%
Return on Equity (ROE) (%) 2% 0% 9% –4%
Return on Sales (ROS) (%) 11% 0% 16% –13%
Investment in digital technologies & product development
(millions $)

22 620 1 022 2 479 1 224

GDP per capita in country of the headquarters in 2017 ($) 59 928 8 827 8 827 59 928
Total ESG score 45 (2) 43 (2) 49 (3) 64 (4)

Table 3. Weights of the criteria and performances of the alternatives

Benefit Benefit Benefit Benefit Benefit Benefit Benefit Cost Benefit Benefit

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
Weights 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Amazon 55% 177 866 37% 2% 2% 2% 11% 22 620 59 928 2
JD 60% 55 641 14% 0% 0% 0% 0% 1 022 8 827 2
Alibaba 95% 22 965 62% 28% 30% 9% 16% 2 479 8 827 3
eBay 27% 9 567 77% –11% 24% –4% –13% 1 224 59 928 4

Table 4. Normalized decision matrix

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

Amazon 0.431 0.946 0.347 0.066 …

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