Customer preference and multi-brand online stores: A...

Customer preference and multi-brand online stores: A

decision making using AHP

Dr. Sundar Raj Vijayanagar

Professor, IFIM Business School, Bangalore

[email protected]

Abhishek Kumar

PGDM Student, IFIM Business School, Bangalore

[email protected]

Sneha Goenka


Siddhartha Ray


[email protected]

Madhusudhanan


[email protected]

Prof. Namrata Nanda*

Research Mentor, IFIM Business School, Bangalore

[email protected]

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Volome 8, Issue 12, December 2019 430

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mailto:[email protected]

http://ifim.edu.in/

ABSTRACT

Purpose: The basic objective of the research is to study the customer perspectives on the different parameter

of their need to measure the performance of multi-brand online retail store. It attempts to investigate which

company out of the three e-commerce players, i.e, Amazon, Flipkart and Snapdeal leads when compared with

the preference needs of customer that is on five parameters which are product range, product delivery, ease of

use, empathy to customer and payment options.

Design/ Methodology/ Approach: Primary data were collected using a structured questionnaire. The study

uses Analytical Hierarchy Process (AHP) technique to analyse the data after screening 250 datasets in

accordance with Microsoft excel.

Findings: The result highlighted that company Amazon was leading in terms of performance and the

parameters considered for determining the performance of the online stores showed product delivery as the

dominant factors among the rest five factors while making a decision.

Practical Implications: The usefulness of this research could be derived by the new players in this e-

commerce field or existing players who are at a steep loss in their run. Considering these parameters, which is

in the order of priority as per the need of customers the players can modify their format to suit the customer’s

expectation and thereby inculcating those, they may can come across a giant change in the performance of their

store.

Originality/ Value: The novelty of this paper is the priority order of customers’ preference parameter which

can be undertaken by the multi-brand online store keepers while delivering the products.

Keywords: Analytical Hierarchy Process (AHP), Multi-brand, Online retail, Priority Matrix, Customer

preference.

INTRODUCTION

In simple words, the term multi-brand refers to “range of brands”. The stores, portals or any form of retail

outlet dealing with multi-brand products is considered as the multi-brand store. In this research paper, a

comparative analysis on the performance measurement of “online multi-band stores” i.e. the e-stores was done,

which sell the products to their customers without the intervention of any intermediary, over the internet. The

performance of a company can be determined by taking both history and future projections into consideration.

At the first glance, the performance of a company can be visualized by the attainment of certain goals as its

core objectives and secondly through future prospects because the decisions that one takes today creates a

strong impact on the outcome tomorrow. On the whole, one can say that the performance is not only related to

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the past or present actions but also on all the future strategic decisions taken by the company. Thus,

Performance measurement can be defined as the collection, examination and interpretation of the company’s

future plans, programs and objectives and check its ability towards the achievement of desired results as per

the plan. Banker (2004), studied On the basis of the analysis, further steps are taken towards minimizing the

loopholes which affect the efficiency of the Company and deteriorate its performance

Online multi-brand stores are a part of the retail segment of the Supply chain which is in the direct touch of the

end consumer. This segment basically sells the products in terms of goods and services to the end consumer

which includes electronics, apparels, groceries, books, music, mail-order, mixed assortment and others.

Although, in this era of revolution the E-commerce industry is a rising star it faces a lot of problems. As per

the data collected by Statista.com, the worldwide sales in E-commerce industry is showing an upward trend

which clearly shows that the e-commerce industry is very powerful in terms of both revenue and profit.

(Insert Figure 1)

Despite the reason that E-commerce industry is showing an upward trend in terms of revenue does not

necessarily means that there are no glitches and all the firms in E-commerce Industry are making money.

Mostly all the companies in the industry whether big or small have to face multiple challenges on regular basis.

The problem may vary from firm to firm depending on its size and market share. Some may face technical

issues like website maintenance and some may face normal issues like Customer Service.

This paper conducted a comparative analysis between Amazon, Snapdeal and Flipkart on the basis of six

dimensions to measure the present performance of these market leaders and have suggested the ways to deal

with loopholes. Those six dimensions are ease of use, empathy to the customer, Product Range, Product

Delivery & Payment Option.

Ease of use signifies how friendly the online platform of the multi-brand retail store is. Empathy to the

customer means how much the platform cares about the feeling of a customer, how affectionately they take

into their complaints and concerns Product range accounts for the availability of the stock/merchandise in

terms of its width and depth that the customers are looking for. Product Delivery services directs to how

hassle-free the delivery happens for the ordered product under the given time & no loss to the product.

Payment Option refers to the secured payment gateway & different options of payment that the customer may

be in need of like COD (Cash on Delivery) services, free delivery services.

For the above-stated parameters, the study has followed the Analytical Hierarchy process to reach to the

desired result. AHP is the structured technique which was developed by Thomas L. Saaty, in the 1970s for the

purpose of decision making. It helps the decision makers to find out the best decision out of all the available

alternatives to fulfill the alternatives (Misra & Panda, 2017).

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REVIEW OF LITERATURE

In this research paper, the role of performance Measurement is implied through the strategic control which

essentially evaluates the behavior of people with the predetermined strategy of a company.

Tuomela (2005), Performance measurement plays a vital role in the decision-making process of any

organization which further influences the course of actions that must be taken in that respect. As per this

research paper, the effectiveness and efficiency of any Organisation majorly depend on its historical

performance which can be studied through the collection, accession, categorization, examination, elucidation,

and distribution of relevant data. The paper also focuses on the evolution of Performance Measurement

techniques over a period of time which can be divided into two stages. In the first stage, Performance was

measured on financial and productivity performance of a company and in the later stage, a multidimensional

set of Performance measurement has been introduced which consists of financial and non- financial metrics.

Marketos & Theodoridis (2006), researched BI which comprises the strategies and technologies used by

organizations for data analysis of the collated business information is not sufficient to measure the performance

of the business. The BI focuses mainly on historical data which can only be helpful in the tactical and strategic

decision making in an organization which does not help a business ion broader spectrum. For the real-time and

process-oriented organization, not only strategic and tactical decision making is required but also efficient

operational decision making is required. Hence the best way to maximize the efficiency of an organization

each Level of Management team should have the right to access the required information at right time in order

to meet the objectives efficiently and effectively.

Ballantine (2005), explained, the performance of an online retail store can be analyzed by the way of two

variables namely, level of interactivity and the amount of information received by an online shopping

environment. The research has basically proved the linear relationship between both independent variables.

As per the study, the increase in the level of satisfaction and amount of information leads to the increase in the

level of satisfaction. It is also believed that the information should not be provided more the required as it

creates a sense of confusion and may reduce customer satisfaction. Yoo & Donthu (2001), according to a well-

designed online shopping website plays a vital role in attracting the customers and affects their shopping

decisions Aladwania & Palvia (2001), has discussed the impact of web quality on consumer’s decision. As the

internet has made the world a global village, people are mostly using the web for the purpose of interacting and

interfacing. The author has analyzed and introduced new web quality which not only focuses on description

and narration but also targets empirical evaluation to measure user-perceived web quality. Chaudhuri (2001),

On the basis of three separate surveys from brand managers and consumers, the authors tried to examine the

role of brand loyalty linking with purchase loyalty and attitudinal loyalty.

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As per the report, Purchase loyalty and attitudinal loyalty gets affected by brand trust and brand effect when

the product and brand related variable are controlled. Dellaert & Ruyter (2004), by the way of Technology

Acceptance Model, the authors have researched on the parameters that influence customers to shop online.

This research paper focuses on creating a framework which helps the Organizations to understand the factors

that motivate customers to shop online. As per the report factors like situations, consumer traits, product

specialties, prior experiences in aligned with ease of use, availability and enjoyment plays a vital role in

deciding the preference of customers for online shopping. Childers, Carr & Carson (2001), the authors

experimented on technology acceptance research and web behavior result of which shows the importance of

both new interactive environment through social media and conventional efficient incitement.

The research shows the evidence that both the aspects play an equal and significant role in predicting the

attitudes of consumers towards online shopping. Bai et al. (2008), performed a research on Chinese markets to

measure the satisfaction level of online consumers and evaluate their intentions towards online shopping. The

research indicates the direct positive impact of web quality on online consumer’s behavior. Despite the

Chinese online market is huge in size has holds instances of becoming a global market leader in coming

decades it still has lots of discrepancies in terms of providing better service quality, satisfaction and brand

loyalty to consumers. The research shows that the demand for online tourism products is huge hence providing

a great chance for the tourism and hospitality industry to work in this direction and implement innovative steps

in this area. Rosenbloom (2005), examined the unseen factors which affect the customer’s preference for

shopping from one e- retailer over another. These factors are the perceived value of the product, service

attribute-level performance and increase in the level of satisfaction over the time.

The main idea of this whole research is to check the customer retention level by the way of mentioned

parameters. On the basis of the study, it is shown that customer retention does not only depend on at- checkout

satisfaction, but also depends on the after sales services provided to the customers. The link between

perception and satisfaction decompose rapidly as the satisfaction level all stages of purchases vary based on the

price and quality perception of the consumer. Hence a suggestion is laid down that the E-retailers should work

on pre and post sales services with an equal amount of dedication and enthusiasm to keep the customer

retention rate on a positive track. Banker et al. (2004), has discussed the usefulness of scorecard which is the

graphic presentation of the performance over a period and lays a framework to take necessary measures in

terms of both financial and operational area. It helps an organization to build strategies and based on the

performance measurement.

This research suggests the organization’s to not only take common measures to enhance the performance but

should also take strategic measures to deal with internal problems which affect the performance of the

company drastically. Hejiden & Verhagen (2004), discusses the influence of online store image on consumer’s

buying decisions. The study shows that if the organizations focus on the factors that affect the image of online

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stores it can help them drastically, provided that the customer is regular and loyal towards them. Secondly, the

research suggests that store familiarity and store style have an inverse relationship with online purchasing. No

matter how much money is invested in store familiarity and store analysis it will not increase the sales and

enhance the performance.

The main area of focus is making the websites enjoyable, attractive and easy to use to attract more and more

consumer to buy online. They should also focus more on building trustworthiness, reliability and goodwill of

their stores which shows the direct positive impact on the performance of multi-brand online stores. Jun, Yang

& Kim (2004), have discussed the consumers’ perceptions of online multi-brand stores and level of satisfaction

towards it. They have performed research on six important dimensions of performance measurement and

evaluated their effect on consumers’ preference.

Those six dimensions are prompt responses, access, ease of use, attentiveness, security and credibility. The

study shows that more the effort has been put on the improvement of websites, better is the result in terms of

customer appreciation and retention. The research shows the evidence of the direct relationship between the

quality of services provided to consumers and their satisfaction level. In the era of this cut-throat competition

only on the basis of cost – leadership strategy any organization cannot attain the maximum level of consumer

appreciation. To get the best of results the organizations should largely focus on product – differentiation

techniques and better service quality which will not only increase the level of customer satisfaction but also

help in building strong customer bases. Zahedi et al. (2002), on the basis of disconfirmation and expectation

approach, the authors tried to measure the satisfaction level of the web – consumer. In their research work they

segregated the website quality into information quality and system quality and on the basis of research, they

suggested nine key parameters for web customer satisfaction. Peterson et al. (2004), suggested that the

information system helps the E- retailer to get the insights of web consumers’ satisfaction.

RESEARCH METHODOLOGY

The survey using an AHP-based questionnaire was distributed to the customers of E-commerce chain who

were using this service for over years. It was circulated throughout. The selection of the cities is based on the

coverage of a mixed population and convenience in sampling. Random sampling technique was used as

appropriate for this study because of the scale used. The Linear 5-point scale was used in the survey

questionnaire where for every question the reference competitor was ranked as 3 and following that the rating

was taken into consideration. As the performance of any industry or company is measured by the value it can

deliver to its customers, so our research study totally drives to the customer insights, customer reviews and

customer feedback anatomy. It is a full-fledged primary data-based research where the data was collected in

the form of a research survey by passing the Google form.

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The structured questionnaire consisted of 18 Questions in total with 6 sections having 3 questions in each with

the 1st section based on the personal information of the respondents and all the consecutive section after that

was based on the Questions to compare the 3 players of E-Commerce i.e.,

• Amazon

• Flipkart

• Snapdeal

They were evaluated as previously discussed on 5 parameters i.e.,

• Product Range they present to their customer.

• Payment Options they offer.

• Ease of use they provide.

• Product Delivery Efficiency.

• Empathetic to Customer they are.

With the Sample size target of 200 Respondents, Random Sampling Technique was used. By Random

Sampling technique, the Online survey form was spread all over India received 230 response and when

checked for its validity, all were Valid.

It received responses from various age groups ranging from 15-56 years from the different Indian States.

After receiving the data, the research underwent AHP ANALYSIS TECHNIQUE, as a model to support the

decision theory (Misra & Panda, 2017).

(Insert Figure 2)

Misra & Panda (2017), explained the AHP process as:

• The received data were initially converted to the scale of 1-9 from 1-5 to get the precise value for

comparison, with the comparer’s value as 5.

• To get the ratio of comparison the comparer’s value was then changed to 1 and then every value was

compared in reference to 1 as of its compare’s value.

• The received ratio was then processed with Reference table to structure the comparison in the tabular format

and then Priority Matrix was formed to check the Mean value out of it for Local Weights initially.

• After that by calculating the Lambda value & Consistency Index, the Consistency ratio was calculated.

• Consistency Ratio, when calculated keeping Random Index table’s Value in consideration with the number of

criteria and consistency Index value reflected that the data is Consistent (Being <10%)

• Consistency Ratio for Every 5 parameters to compare 3 companies was used to Rank the company.

• Similarly, on another side when the weight of each parameter was calculated in decision making, the same

degrees of calculation was performed for them and ranked the parameters to check its dependency in decision

making and recommending to the low performers.

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DATA ANALYSIS & INTERPRETATION

Each Respondent’s weights were processed utilizing Microsoft Excel 2010. Applying the AHP strategy to the

performance of multi-brand online stores for 1 level of progression. In the view of the customers, the matrix

shows the number (in the form of a scale of 9 points).

(Insert Table 1)

As indicated below by the scale that utilized comparison from 1 to 9 to compare one company over another as

a part of the undertaken study where 9 makes a company towards which the scale lies as extremely important;

8 as very important to extremely important; 7 as very important; 6 as moderately important to very important;

5 as moderately important; 4 as somewhat important to moderately important; 3 as somewhat important; 2

equally important to somewhat important; 1, equally important when measurement is in comparison with the

another company. The methodology discussed above is mention below in Table 1.

(Insert Table 2)

(Insert Table 3)

The received responses were utilized as inputs to perform the two paired correlation matrix first for

“Parameters” which was used during the ranking of parameter which affects the Performance and then the two

paired correlation matrix was formed for the “Multi-brand online retail company” so as to use while Ranking

the company as per customer shopping preference store (as in Table no. 2 and 3).

(Insert Table 4)

(Insert Table 5)

The priority Matrix tables (as in table 4 ,5) is a decision matrix table that measures the individual ranking of

each attributes to the overall attributes. It measures the weights of each criteria over its all the other

alternatives.

Table 4 measures the weight of every parameter of comparison which would determine the performance at the

end over all the other parameter of comparison. Out of applied formulae the result received at the end

Prioritizes Product Delivery Services as a most deciding parameter for the performance measurement of the

company following to which the deciding position is bagged by Empathy to customer & then Ease of use and

then Product range & Payment option as the least important deciding factor out of all the parameters of

Measurement.

Table 5 similarly compares the weight of each company over all the other companies for every parameter of

consideration. Out of 5 sub-tables of Table 5, the first sub-table ranking shows that when Product Range is the

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considered as the measure for Performance “Flipkart” is the leader for Product Range availability followed by

Amazon & Snapdeal. Rest for all the other parameters “Amazon” leads the position amongst its competitors.

(Insert Table 6)

The paired correlation matrix for the “Companies” & “Parameters” as shown in Table 6 measures the impact of

every parameter to each company to determine the local weight of each parameter as well as local weight of

each company, The derived value is the result of Relation between each parameter & and the way they affect

the company’s performance as graded by the customers.

FINDINGS AND DISCUSSION

Out of the data collected containing customer feedback and ratings, every parameter’s weight was multiplied

by the weight of each Company to measure the effect of each parameter in determining its performance (as in

Table 7).

(Insert Table 7)

The local weight of each parameter was measured by multiplying the mean value of each parameter of the

priority table in the following way:

• Parameter over Parameter

• Each Parameter over the company.

The local weights of every parameter when multiplied individually by the local weights of every company,

yielded the global weight of the combination.

Table 8 demonstrates the significance ranking of the attributes of the putting the value of the global weight of

every parameter when calculated to the weights of the company. The outcome demonstrates that the customer

respected “Amazon” as the most outstanding, Multi-brand online retail store. Out of correlation analysis, it was

found that “Product Delivery” as the most important factor for the measurement of their performance.

Customers also responded that “Empathetic to the customer” being the second most vital factor, “Ease of Use”

as the third important, “Product Range” Availability as the fourth parameter & “Payment Options” as the last

most deciding factor for the performance of any Multi-Brand online retail store. (As in Table 7)

When the same measurement was carried out to check the leader in performance factors for online multi-brand

retail store the response of customers made “Amazon’ as the leader of the chain, with Flipkart being the

runner-up in the competition & Snapdeal the worst performer amongst them.

(Insert Table 8)

PR: Product Range, PO: Payment Option, EOU: Ease of use, PD: Product Delivery, ETC: Empathy to

Customer

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MANAGERIAL IMPLICATIONS AND RECOMMENDATIONS

The majority of the multi-brand online retail companies in today’s date which is not able to succeed with their

forecasted sales & profit fails due to mismatch of what they want to deliver to the customers in respect to what

customers want out of them in Need order. This Survey, which gathered the reviews of customers out of the

Comparative analysis of three multi-brand online retail chains, having five parameters into consideration, the

paper highlights that “Amazon” leads the race being standing to the expectation of customers in “Product

delivery service giving timely & efficient delivery services. With the next most impacting parameter, i.e.,

Empathetic to the customer, again Amazon leads the race here. The Report signifies that the rest two

companies, i.e., Snapdeal & Flipkart to compete the leader “Amazon” need to upgrade themselves in the

performance, determining the parameter, i.e., Product Delivery, Empathetic to Customer, Ease to use, Product

range & Secured Payment options in this flow.

CONCLUSION

As the trend of E-Commerce and Multibrand online retail is slowly capturing the modern trade market, it

becomes vitally important for any company to measure the need of company. Running by the company’s own

policies without caring the need of customers at times proves to be fatal and ultimately lead to the loss of the

company. With the emerging development towards e-commerce sector, a significant rise in the numbers of this

e-tailing format of a star is seen in the coming time. Research shows that the drift towards e-tailing is shifting

as from 2.5% of e-tail current market-share to double folds every year.

The survey, conducted by the researchers in the papers as directed the comparative mode of study, just not

studied the ranking of an individual company over another, but at the same time measured the importance of

each parameter for which they were ranked to check their performance.

The comparative results show that out of 5 parameters of comparison what customers want out of their need of

preference is the perfection in terms of “Product Delivery” services which can be expected to be on time and

hassle-free ensuring no breakage and no deterioration in the quality of the product.

The next parameter that became the second most important need of the customer after getting the needed kind

of product & its On-time Delivery is how much the online store serves being “Empathetic to customer” in

knowing the feeling of the customer, acknowledging their need, how they hold their complaints and after sales

feedbacks from their customers.

Another Parameter of importance becomes how “Easy to use” is their online portal to the customer. This refers

to how easily they are able to locate or find the product they are looking at or searching for. It also stands for

the technology associated with that online platform in creating the shopping hassle-free.

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“Product Range” Availability though perceived to be the most crucial factor what customers actually looked

for but out of the result gathered becomes the fourth important factor which is valued by the customer &

“Payment options” grab the position of being the least important factor to determine the performance of any

multi-brand online retail store.

Out of all these parameters discussed the survey result when got analyzed out of Analytical Hierarchy Process

revealed that the leader who maintains all these parameters is Amazon. The second position is Bagged by

Flipkart & Snapdeal being the Non-performer in most of these categories.

This research becomes highly important for any blooming multi-brand online retail store to consider the factors

apart from many other parameters that have not been taken into the study, which remains the gap of the study

that can be taken into the study as the next level of study for this paper. Having studied and analyzed the 5

basic parameters, it becomes an immense source of importance for all the companies to focus on the area of

improvisation which may impact them by an increase in their performance, sales & Profitability.

In this study, not only the new companies but also if the existing losers adapt them into their operation, it may

become the points of importance for them.

ACKNOWLEDGEMENT

The satiation and euphoria that accompany the successful completion of this research would be incomplete

without the mention of the people who made it possible. We thank the research team of Accendere Knowledge

Management Services, CL Educate Ltd. for their unflinching guidance, continuous encouragement and support

to successfully complete this research work.

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Appendix A (Figures)

Figure 1 E-commerce worldwide sales

Figure 2 Analytical Hierarchy Process (AHP) Technique

OBJECTIVE

Product Range Ease of use Payment Option Product Delivery Empathetic to

Customer

Amazon Flipkart Snapdeal

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Appendix B (Tables)

Table 1 Saaty’s response matrix

Table 2 Reference table

Product

Range

Payment

Option

Ease of Use Product

Delivery

Empathy to

Customer

Product

Range 1 1.010352 0.486164 0.490086 0.510318

Payment

Option 0.989754 1 0.486164 0.490086 0.510318

Ease of Use 2.056918 2.056918 1 0.598994 0.623722

Product

Delivery 2.040457 2.040457 1.669464 1 1.041282

Empathy to

Customer 1.959561 1.959561 1.603277 0.960354 1

Table 3 Reference table

Product Range Amazon Flipkart Snapdeal

Amazon 1 1.24312 0.843111

Flipkart 0.804427 1 1.370972

Snapdeal 1.186083 0.72941 1

Table 4 Priority Matrix of parameters determining performance

1-9-point scale/relative importance level in terms of Payment options availability of e-commerce company

Flipkart 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Amazon

Flipkart 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Snapdeal

Amazon 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Snapdeal

Product

Range

Payment

Option

Ease Of

Use

Product

Delivery

Empathy

to

Customer

MEAN RANK

Product Range 0.124275 0.125241 0.09269 0.138461 0.138461 0.123825 4

Payment Option 0.123001 0.123957 0.09269 0.138461 0.138461 0.123314 5

Ease of Use 0.255623 0.25497 0.190655 0.16923 0.16923 0.207942 3

Product Delivery 0.253577 0.25293 0.318292 0.282524 0.282524 0.277969 1

Empathy to Customer 0.243524 0.242902 0.305673 0.271323 0.271323 0.266949 2

ADALYA JOURNAL


ISSN NO: 1301-2746


Table 5 Priority Matrix of companies

Table 6 Correlation table

Table 7 Local & Global variables

Influencing parameters

Local weights Companies Local weights Global weights

Product Range 0.123825 Amazon

Flipkart

Snapdeal

0.338304

0.343986

0.31771

0.04189

0.042594

0.03934

Payment options 0.123314 Amazon 0.347897 0.042901

Product

Range

Payment

Option

Ease of

Use

Product

Delivery

Empathy to

Customer

Sum Local

weights

of

Parameter

Amazon 0.041891 0.042901 0.071946 0.098916 0.093953 0.349606 0.349606

Flipkart 0.042594 0.041664 0.070914 0.093505 0.089826 0.338503 0.338503

Snapdeal 0.039341 0.03875 0.065082 0.085549 0.08317 0.311891 0.311891

SUM 0.123825 0.123314 0.207942 0.277969 0.266949

Local

weights of

Company

0.123825 0.123314 0.207942 0.277969 0.266949

ADALYA JOURNAL


ISSN NO: 1301-2746


Flipkart

Snapdeal

0.337868

0.314235 0.041664

0.03875

Ease of use 0.207942 Amazon

Flipkart

Snapdeal

0.34599

0.341029

0.312982

0.071946

0.070914

0.065082

Product delivery 0.277969 Amazon

Flipkart

Snapdeal

0.355852

0.336385

0.307763

0.098916

0.093505

0.085549

Empathy to consumer 0.266949 Amazon

Flipkart

Snapdeal

0.35195

0.33649

0.311559

0.093953

0.089826

0.08317

Table 8 Global Ranking

SUM GLOBAL RANKING

PR PO EOU PD ETC

Amazon 0.041891 0.042901 0.071946 0.098916 0.093953 0.349606 1

Flipkart 0.042594 0.041664 0.070914 0.093505 0.089826 0.338503 2

Snapdeal 0.039341 0.03875 0.065082 0.085549 0.08317 0.311891 3

SUM 0.123825 0.123314 0.207942 0.277969 0.266949

RANK 4 5 3 1 2

ADALYA JOURNAL


ISSN NO: 1301-2746


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