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TRANSCRIPT
Citation: Kaushik, V., Khare, A., Boardman, R., Blazquez, M. (2020). 'Why Do Online Retailers Succeed? The Identification and Prioritization of Success Factors for Indian Fashion Retailers', Electronic Commerce Research and Applications, 39, Jan-Feb 2020: https://authors.elsevier.com/c/1aDXr5aO-oZX3X
Why do online retailers succeed? The identification and prioritization of success factors
for Indian fashion retailers
Abstract:
The exponential growth in online fashion retailing in India has brought several structural
changes. Changing consumer needs and value propositions have made it imperative for
online retailers to identify factors that are critical for its growth. The current study attempts to
identify factors that influence consumers’ preference for online fashion retailers (OFR). The
factors were identified and their weights were evaluated using the AHP methodology.
Furthermore, the potential alternatives were ranked using VIKOR technique in R. Initially, 40
factors were identified, spread across 7 categories and in line with earlier research on online
retail and views of retail experts from Indian online fashion retail sector. The analysis
revealed that the category ‘webstore-image’ is most preferred and that the factors ‘online
shop recognition’, ‘price’ and ‘reputation of stores’ were of importance. The study identified
new factors that are important for the success and growth of online fashion retailing that are
specific to India.
Keywords: Online fashion retailers; Online shopping; India; VIKOR; Decision making in
online shopping
1.Introduction.
E-commerce is defined as ‘all electronically mediated information exchanges between an
organisation and its external stakeholders’ (Chaffey, 2015). The e-commerce sector in India
is worth USD 19.5 billion and has grown substantially in different parts of the country due to
increasing internet penetration (KPMG, 2018). Furthermore, online retailing in India is
expected to increase 250 percent in the next 3 years, with 35-40 percent of the market being
attributed to the apparel segment (CRISIL, 2018). Indeed, the Indian Institute of e-commerce
posits that by 2020, India will be generating USD 100 billion through online retailing and $35
billion of that will be through online fashion retailing (Khosla, 2017). This highlights the
rapid growth and rising importance of online fashion retailing in India and why it is a key
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area for academic research.
The success of OFR such as Myntra, Jabong, Limeroad, Shopperstop, Craftsvilla, and Koovs
further highlight the acceptance and prevalence of online shopping in India, as well as the
highly competitive nature of it. The intense competition means that online fashion retailers
must ensure that they offer a superior online shopping experience and an all-round more
attractive proposition than their competitors. Although research has identified individual
factors required for the success of online retailers in general (Chiu et al., 2013; Kalelkar et al.,
2014; Dey et al., 2015; Ghatak et al., 2016; Gupta and Dubey, 2018; Srivastava et al.; 2018)
and investigated the acceptability of online stores (Hsu et al., 2010; Kabir et al., 2012; Liu et
al., 2015; Kahraman et al., 2017; Rouyendegh et al., 2018) to the author’s knowledge, no
study has focused specifically on what attributes make online fashion retailers successful and
how they should be prioritised. The present study will fill this gap. Online fashion retailing is
a complex and unique industry in comparison to other retail sectors as the inability to
physically examine and try on a product is a considerable barrier to making informed
decisions (Ha et al., 2007). On average, 42% of clothes that are bought online are returned
(Drapers, 2017) which is a major problem for online fashion retailers, emphasising the need
for the identification of factors that can make their online offering more appealing to
consumers.
Extant research has investigated the importance of individual attributes on fashion retailers’
websites, such as aesthetics (Ganguly et al., 2010; Wang et al., 2011; Luo et al., 2012; Hasan,
2016), post-purchase service (Lawrence and Tar, 2010; Roy Dholakia and Zhao, 2010;
Hasan, 2016; Cao et al., 2018), product presentation (Blanco et al., 2010; Song and Kim,
2012; Verhagen et al., 2013; Boardman and McCormick, 2019), product attributes (Flanagin
et al., 2014; Kempen et al., 2015; Fang et al., 2016; Chen et al., 2016), hendonic aspects
(Scarpi et al., 2014; Blazquez, 2014, Ahmad et al., 2017) and the reputation of the store (Utz
et al., 2012; Kim and Lennon, 2013). The present study will build on this literature in order to
see which attributes should be prioritised for fashion retailers in order to create a successful
business. Thus, this study aims to investigate online fashion retailing holistically, classifying
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all the attributes identified in the literature in terms of their importance for a successful online
retail strategy using online fashion retailing in India as a key new context.
The research questions that the study aims to answer are as follows:
RQ1: What are the critical success factors for online fashion retailing in India?
RQ2: How important are these success factors in generating ROI for retailers?
RQ3: What factors should online fashion retailers prioritise?
2. Literature Review
E-commerce has radically changed the dynamics of the business environment and the way in
which people and organisations are operating (Chaffey, 2015). The main challenges faced by
online retailers today are the provision of a superior customer service experience (Cao et al.,
2018), reducing the perceived risks of online shopping in the mind of consumers (Flanagin et
al., 2014), providing an effective website design (Ganguly et al., 2010; Hasan, 2016) and
reducing the number of returns (Oghazi, 2018).
In order to overcome these challenges, online fashion retailers need to identify the key areas
that they need to focus on in order to employ a successful business strategy. Extant literature
has identified the following factors as important attributes to consider for online retailers,
summarised in Table 1.
Table 1. Online Fashion Retailer’s Attributes (Source: Author’s own)
Category Factors Code References:
Webstore Aesthetics (WA)
Visual Design WA1 Ganguly et al., 2010; Wang et al., 2011; Luo et al., 2012; Hasan, 2016;
Navigation Design WA2 Ranganathan and Ganapathy, 2002; Ganguly et al., 2010; Luo et al., 2012; Hasan, 2016;
Information Design WA3 Ganguly et al., 2010; Luo et al., 2012; Hasan, 2016; Responsive Web Design WA4 Mohorovičić, 2013; Heath, 2017Home page design WA5 Kluge et al., 2013; Yoo and Kim, 2014E-store niche WA6 Experts opinion
Post order convenience (POC)
Good customer support POC1 Roy Dholakia and Zhao, 2010; Luo et al., 2012; Hasan, 2016; Cao et al., 2018.
Order tracking facility POC2 Roy Dholakia and Zhao, 2010; Hasan, 2016On-time delivery POC3 Lawrence and Tar, 2010; Roy Dholakia and Zhao, 2010;
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Category Factors Code References:Hasan, 2016; Cao et al., 2018.
Flexible return policy POC4 Chang et al., 2013; Oghazi et al., 2018Easy order cancellation POC5 Experts opinion
Product attributes (PA)
Product availability PA1 Roy Dholakia and Zhao, 2010; Hasan, 2016
Quality product PA2 Flanagin et al., 2014; Kempen et al., 2015; Fang et al., 2016; Chen et al., 2016
Wide range and categories PA3 Chen et al., 2016; Huyghe et al., 2017; Nisar and
Prabhakar, 2017Latest trend PA4 Experts opinionBranded products PA5 Experts opinion
Price PA6 Kim et al., 2012; Luo et al., 2012; Hasan, 2016; Chen et al., 2016
Webstore facilities (WF)
Across alternatives comparison WF1 Ranganathan and Ganapathy, 2002; Noh et al., 2013;
Loureiro and Breazeale, 2016; Bargain WF2 O’Brien, 2010Customization WF3 Cho and Fiorito; 2009; Cho, 2012.Style guide WF4 Experts opinion
Low cost shipping WF5 Roy Dholakia and Zhao, 2010; Luo et al., 2012; Hasan, 2016
Multiple payment options WF6 Experts opinionVarious delivery options WF7 Roy Dholakia and Zhao, 2010; Laudon and Traver, 2013
Tactile Information (TI)
Multiple Enlarged images TI1 Kim and Lennon, 2008; Blanco et al., 2010; Song and Kim, 2012; Verhagen et al., 2013; Kim and Lennon, 2010;
Modelled product TI2 Kim and Lennon, 2008; Kim et al., 2009; Boardman and McCormick, 2019
Visual Merchandise TI3 Ha and Lennon, 2010; Baek et al., 2015
Extensive product details TI4 Ranganathan and Ganapathy, 2002; Roy Dholakia and Zhao, 2010; Kim and Lennon, 2008; Blanco et al., 2010
Product video TI5 Xu et al., 2015; Boardman and McCormick, 2019
Hedonic motivations (HM)
Online store layout. HM1 Wu et al., 2013; Izogo and Jayawardhena, 2018Celebrity endorsement HM2 Experts opinion
Discounts or cashback HM3 Park et al., 2012; Ballestar et al.,2016; Zhou et al., 2017; Vana et al., 2018
Shopping fun and enjoyment HM4 Scarpi et al., 2014; Ahmad et al., 2017
Special sales and deals HM5 Experts opinionPrevious experience with the shop HM6 Experts opinion
Webstore image (WI)
Reputation of store WI1 Utz et al., 2012; Kim and Lennon, 2013Gender/Age-group specific Online shop WI2 Kim et al., 2011; Lim and Yazdanifard, 2014;
Online shop recognition WI3 Park and Kim, 2003; Luo et al., 2012online ratings and review WI4 Wei and Lu, 2013; Engler et al., 2015; Wang et al., 2018; Secure Transaction and personal information. WI5 Ranganathan and Ganapathy, 2002; Thakur and
Srivastava, 2015; Blut et al., 2015; Chen et al., 2016
These criteria will be discussed in detail in the following section.
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2.1.1 Webstore Aesthetics
Both the fashion industry and the internet are predominantly visual environments in which
items are presented, explained and promoted entirely through visual imagery and information
(Wong et al., 2014). Therefore, the aesthetics of a retailer’s website plays a vitally important
role in attracting consumers. The importance of aesthetics in an online shopping environment
and web-store design has been stressed in extant research (Wang et al., 2011; Blut et al.,
2015). Indeed, website aesthetics can create a positive attitude towards the website that will,
in turn, encourage the intention to repurchase and produce positive word-of-mouth (Carolson
and O’Cass, 2011). The main design parameters for an online store are visual design,
navigation design and information design (Ganguly et al., 2010; Luo et al., 2012; Hasan,
2016). For OFR, these design parameters enhance the aesthetics of the stores and thus play a
major role in its success. In particular the homepage design impacts the customer’s decision
to shop at a particular online store (Yoo and Kim, 2014) and so it is very important for
retailers to design their homepage to a high standard. Moreover, a responsive web design is a
feature in which a particular web-store adapts its pages and products across different online
platforms (tablet, mobile, desktop) aesthetically to enhance the user experience
(Mohorovičić, 2013; Heath, 2017).
2.1.2 Post order convenience
Retailers need to provide adequate support once the purchase takes place and make it as
convenient as possible for the customer (Roy Dholakia and Zhao, 2010; Hasan, 2016).
Customer support can be enhanced by providing a toll-free number or online link where all
queries can be resolved directly. Furthermore, the inclusion of an order tracking facility has
been found to be a significant factor for satisfaction and repurchase intentions in online
shopping (Roy Dholakia and Zhao, 2010). Also, timely delivery is a quintessential parameter
for customers satisfaction and loyalty (Blut et al., 2015; Cao et al., 2018). Customers prefer
online retailers that have multiple delivery options like fast delivery, instant delivery, normal
delivery and location-specific delivery (Roy Dholakia and Zhao, 2010; Laudon and Traver,
2013) as well as low priced delivery (Luo et al., 2012; Hasan, 2016). Moreover, a flexible
return policy has been used as a tool to improve business performance by offering easy
returns in order to enhance customer trust and repeat purchase behavior (Blut et al., 2015;
Oghazi et al., 2018.
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2.1.3. Product Attributes
Research has identified that the availability of the products and the quality of a retailer’s
products plays a vital role in enhancing customer-based brand equity (Flanagin et al., 2014;
Hasan, 2016). Therefore, it is essential that retailers provide a broad range of products and
product categories in order to aid customers in their decision-making process and make them
feel like they have a sufficient choice available to them (Huyghe et al., 2017; Nisar and
Prabhakar, 2017). Competitive pricing has also been identified as a key strategy to achieving
better positioning across online retailers as customers prefer to pay reduced prices online
(Hasan, 2016; Chen et al., 2016). Also, the customization of the products as per one’s desire
has been found to be a preferential factor when choosing which online retailer to shop at (Cho
and Fiorito; 2009; Cho, 2012).
2.1.4. Webstore facilities
Webstore facilities is an umbrella term in which different website features have been
emphasised. Research has identified that the features on a retailer’s website can affect the
buying behaviour of customers (Noh et al., 2013; Loureiro and Breazeale, 2016). For
instance, people enjoy shopping when they can bargain as it enhances the shopping
experience (O’Brien, 2010). Style guides are now often provided for consumers on retailer’s
websites in order to aid them in their decision-making by recommending styles that can be
coordinated to make outfits. According to experts, style guides will become an essential part
of the future of online fashion retailing. To add to the webstore facilities, experts suggest that
the provision of multiple payment options are also critical factors for online fashion retailers
as they reduce the hassle of the checkout stage for consumers. Similarly, the availability of
different delivery options is also important to provide. Customers prefer webstores that have
multiple delivery options such as fast delivery, instant delivery, normal delivery and location-
specific delivery (Roy Dholakia and Zhao, 2010; Laudon and Traver, 2013). Furthermore,
customers prefer online stores that offer minimal shipping charges (Luo et al., 2012; Hasan,
2016).
2.1.5. Tactile Information
Due to the inability of being able to touch and feel a product in an online setttings, tactile
information plays a major role in helping consumers to visualize the product, enhancing the
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sensory experience (Morton, 2018; Liu et al., 2017). Increased visual product information
produces more positive emotions, decreases product evaluation difficulty and positively
affects purchase intention (Jai et al., 2014). There are various ways to improve the tactile
experience and the first is by introducing multiple enlarged images on webstore (Song and
Kim, 2012; Verhagen et al., 2013). Employing an enlargement tool for the garment images
can help minimise users’ perceived risk of shopping online as it provides them with a more
detailed look at the item (Kim and Lennon, 2010). Product image enlargement is essential for
users when they are examining online products as it allows them to see the fabric structure
and quality in detail (Kim and Lennon, 2010). Indeed, Kim and Lennon (2010) found that the
enlargement of images had an effect on a user’s pleasure when shopping online and may also
contribute to providing a more enjoyable shopping experience for users. Online retailers can
minimise users’ perceived risk of shopping online by using human models to display the
garments, so that users can see how the clothes fit (Kim and Lennon, 2008; Kim et al., 2009;
Kim and Lennon, 2010). Previous research has found that users looking at a 3D model
experimented increased levels of enjoyment and involvement in their shopping process and
had a more positive perception of the online store compared to participants who saw lower
levels of image interactivity, such as simply clicking to enlarge the image (Kim et al. (2007,
Blazquez et al., 2014). Furthermore, images of humans increase the likelihood of producing
an emotional response from on an online user (Cyr and Head, 2013). Hence, fashion retailers
should display their clothes on models and mannequin as they are the most influential in
terms of consumer decision-making, followed by an effective and detailed zoom-function and
close-up of the fabric (Boardman and McCormick, 2019).
Research also suggests that the tactile information can be enhanced by elaborating on the
product details such as the manufacturing processes, fabric, style, washing instructions,
precautions and other attributes (Zhao, 2010; Kim and Lennon, 2008; Blanco et al., 2010).
Karimov et al. (2011) stress the importance of information content, recommending that
retailers deliver detailed and comprehensive product information in order to make
consumers’ purchasing decisions easier, such as good quality pictures and product
characteristics. Indeed, Ballantine (2005) found that the greater amount of product detail
provided, the higher user satisfaction was and Blanco et al. (2010) found that when a product
had an image and text appearing together, then consumers remember more information about
it. In particular, the presentation of product information can invoke positive responses from
users (Blanco et al., 2010). Indeed, the product acquisition process is improved if users have
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access to more detailed information about the product, a comparison of prices, availability
and the product’s overall value (Brown, Pope and Voge, 2003). Moreover, many retailers
include product videos on their website in order to create interest and involvement. Product
videos are capable of transmitting information about the product style, colour and fabric and
they can boost customer confidence in the product (Xu et al., 2015), increasing their level of
trust in the product and in the online fashion retailer (Karimov et al., 2011).
2.1.6. Hedonic motivations
Consumers expect to have a more social and interactive online experience in which shared
intelligence can be accumulated and used to aid the decision-making process (Huang and
Benyoucef, 2015). As a result, hedonic aspects need to be considered when designing a
fashion retailer’s website. Online shopping can be viewed as a pleasant experience associated
with fun and enjoyment and so stores that strive to make shopping a fun-filled experience are
visited more often (Scarpi et al., 2014; Ahmad et al., 2017). Indeed, Cyr and Head (2013)
found that users spent more time looking at the hedonic sections of a fashion retailer’s
website than the utilitarian sections. Karimov et al. (2011) also stress the importance of
including social features on retailers’ websites in order to increase users’ trust. Some on the
ways in which Karimov et al. (2011) suggest retailers could do this is by including videos of
sales assistants to help users with their shopping experience, avatars, recommendations and
live chat functions. Research has also found that perceived playfulness has a positive effect
on online shopping adoption (Celik, 2011). Thus, online retailers should add a touch of
humour and fun to their websites to make them more enjoyable and increase consumer
satisfaction.
2.1.7. Webstore image (WI)
Brand image can be defined as the “perceptions about a brand which the brand associations in
consumers’ memory reflect” (Keller, 1993). Research indicates that the purchase intentions
of customers have been influenced by online store image (Verhagen and Van Dolen, 2009).
Webstore image is directly linked with the reputation of that store among customers (Utz et
al., 2012) and can be conditioned by portraying the merchandise according to gender or age
group specification (Lim and Yazdanifard, 2014). Webstore image can be enhanced by
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improving the online store recognition factors, areas that depend on marketing strategy and
brand-building (Park and Kim, 2003; Luo et al., 2012). Another measure that enhances
webstore image are online ratings and reviews (Engler et al., 2015; Wang et al., 2018). High
online ratings and reviews have a positive impact on brand and product acceptance. Lastly,
secure transaction and personal information protection improve the online store image and
loyalty towards that store (Thakur and Srivastava, 2015; Blut et al., 2015; Chen et al., 2016).
2.2 The use of MCDM techniques in online retail
In terms of online store selection, different techniques and methodologies have been adopted
by researchers and practicioners, summarized in Table 2. However, the majority of research
applying decision-making approaches in order to analyze e-stores and their prioritisation, has
focused on e-commerce holistically. There is a lack of research related to understanding
customers’ behaviour towards specific market sectors such as electronics, fashion, home
décor and others. The MCDM (Multicriteria Decision Making) approach has been used
extensively to provide a comprehensive solution for the problems in online retailing, as
shown in Table 2.
Table 2 Summary of studies that have assessed online stores using MCDM techniques (Source: Author’s own)
Researcher (year) Methodologies Issues discussedHsu et al. (2010) AHP Online shopping platform comparison in
Taiwan.Kabir et al. (2012) AHP-TOPSIS Online retail performance among 5 alternatives.Chiu et al. (2013) DANP-VIKOR To improve the business of online E-stores.Kalelkar et al. (2014) AHP To understand the dynamics of main factors in
top Online shops of India.Dey et al. (2015) AHP-TOPSIS Online retail evaluation model developedLiu et al (2015) fuzzy AHP E-commerce alternatives have been rankedGhatak et al. (2016) AHP Online retail patronage in IndiaKang et al. (2016) TOPSIS A case study on B2C E-commerce verified by
sensitivity analysis.Masudin et al. (2016) AHP-TOPSIS Two websites were compared for usability
factors.Valmohammadi and Dashti (2016)
Fuzzy ANP and ISM Barriers to implementation of online shopping.
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Researcher (year) Methodologies Issues discussedSingh et al. (2016). ANFIS-AHP Assessing top E-store in India.Kahraman et al. (2017) fuzzy AHP Selection of B2C marketplace and modeling.Liu et al. (2017). AHP-TOPSIS Evaluation of success factors of Online shops
with examplesSingh et al. (2017) TOPSIS Ranking of critical success factors in online
retailingGupta and Dubey (2018) AHP Adaptability of Indian E-commerce websites.Rouyendegh et al. (2018). AHP-FTOPSIS The performance of three online stores has been
evaluated.Srivastava et al. (2018)
Liang et al., 2017
Ji et al., 2018
Fuzzy AHP and TOPSIS
SVTN-DEMATEL
Regret theory and QUALIFLEX
Discussed online store attributes and buying behavior across gender.
Evaluation of E-commerce website using a case example and checking the robustness of the model using sensitivity analysis
A fuzzy DSS model for item comparison of a case E-commerce
The integrated AHP-VIKOR methodology has been used in diverse research areas due to its
decision-making capabilities, as demonstrated in Table 3.
Table 3. Recent research using integrated AHP-VIKOR methodology
Authors & Year Topic JournalWang et. al., 2019 Selecting sustainable energy conversion technologies for agricultural
residues: A fuzzy AHP-VIKOR based prioritization from life cycle perspective
Resources, Conservation and Recycling
Arar et al., 2019 Office location selection by fuzzy AHP and VIKOR. International Journal of Information and Decision Sciences
Awasthi et. al., 2018 Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach.
International Journal of Production Economics
Emeç and Akkaya (2018)
Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem.
Journal of Enterprise Information Management
Yu et al., 2018 Multi-Criteria Decision Making for PLM Maturity Analysis based on an Integrated Fuzzy AHP and VIKOR Methodology.
Journal of Advanced Manufacturing Systems
Chander et al., 2018 Design of Decision-Making Techniques Using Improved AHP and VIKOR for Selection of Underground Mining Method.
Recent Findings in Intelligent Computing Techniques
Chatterjee et al., 2017
Unified Granular-number-based AHP-VIKOR multi-criteria decision framework.
Granular Computing
Soner et al., 2017 Application of AHP and VIKOR methods under interval type 2 fuzzy environment in maritime transportation.
Ocean Engineering
Digkoglou et al., 2017
Using AHP and VIKOR to evaluate the hotel industry of eight European countries.
Balkan Region Conference on Engineering and Business Education
Singh et al., 2016 Strategy selection for sustainable manufacturing with integrated AHP-VIKOR method under interval-valued fuzzy environment.
The International Journal of Advanced Manufacturing Technology
Prasad et al., 2016 Supplier Selection through AHP-VIKOR Integrated Methodology. International Journal of Industrial Engineering
Tian et al., 2016 An integrated AHP and VIKOR approach to evaluating green design 2016 IEEE 13th International
10
alternatives. Conference on Networking, Sensing, and Control (ICNSC)
Salehi, 2016 An integrated approach of fuzzy AHP and fuzzy VIKOR for personnel selection problem.
Global Journal of Management Studies and Researches
Tadić et al., 2015 Ranking of logistics system scenarios using combined fuzzy AHP-VIKOR model.
International Journal for Traffic and Transport Engineering
Zhu et al., 2015 An integrated AHP and VIKOR for design concept evaluation based on rough number.
Advanced Engineering Informatics
Rezaie et al., 2014 Evaluating performance of Iranian cement firms using an integrated fuzzy AHP–VIKOR method.
Applied Mathematical Modelling
Bhosale and Kant, 2014
Selection of best knowledge flow practicing organisation using hybrid fuzzy AHP-VIKOR method. International Journal of Decision Sciences, Risk and Management.
International Journal of Decision Sciences, Risk and Management
Shokri et al., 2013 An integrated AHP-VIKOR methodology for Facility Layout design. Industrial Engineering and Management Systems
Dincer ans Hacioglu, 2013
Performance evaluation with fuzzy VIKOR and AHP method based on customer satisfaction in Turkish banking sector.
Kybernetes
Previous literature highlights that studies have been conducted on individual attributes of
fashion retailers’ websites. The present study will build on this literature in order to see which
attributes should be prioritised for fashion retailers to create a successful business. This study
will contribute to knowledge by creating a holistic approach to online fashion retailing,
classifying all the attributes identified in the literature in terms of their importance for a
successful online retail strategy.
2.3 Research Gap
Extant research has identified issues relating to the selection of online stores (Kabir et al.,
2012; Liu et al., 2015; Rouyendegh et al. 2018), however there is no study to date that has
classified which attributes are the most important in terms of making an online fashion
retailer successful. Furthermore, there is a paucity of research invesitigaing the Indian fashion
retail sector (Mann and Byun, 2011), an aspect addressed in this research in order to
investigate whether there are any other attributes that need to be considered in this context.
Thus, there is a need to develop a framework and identify criteria and sub-criteria for the
prioritization of online fashion stores. The literature review clarifies that most of the studies
on the prioritization of online stores have employed an AHP (Analytical Hierarchy Process)
methodology (Saaty, 1988) and a hybrid approach, AHP-TOPSIS has been the preferred
approach. In an attempt to identify and evaluate the online store factors efficiently, an
integrated AHP-VIKOR framework has been used in this study. VIKOR considers individual
regret minimization and group utility maximization (Liu and Wu, 2012) and it can solve
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discrete decision-making problems with conflicting criteria so as to provide a near-to-ideal
solution (Liu et al., 2013).
3. Research Methodology and Framework
The study used an integrated AHP-VIKOR framework for OFR selection. The study adopted
AHP to obtain weights of the factors while VIKOR has been used to select the most feasible
OFR. AHP is one of the popular MCDM (multi-criterion decision making) methodologies
(Satty, 2013) amongst managers because of its subjective opinion (Ishizaka and Labib, 2011).
AHP can be used alone but more reliable results can be achieved by integrating AHP with
other MCDM techniques (Mangla et al., 2016). VIKOR is a compromise solution technique
and an effective tool to rank alternatives (Chatterjee et al., 2010). When compared to similar
technique such as TOPSIS (technique of order preference by similarity to ideal solution),
VIKOR follows individual regret minimization and group utility maximization (Liu and Wu,
2012) and it can solve discrete decision-making problems having conflicting criteria so as to
provide a solution that is close-to-ideal (Liu et al., 2013).
The proposed research framework is used for identifying the criteria of OFR prioritization
and selection. The study used the AHP-VIKOR approach as shown in Fig. 1. There are three
phases in the study and the resulting framework will help decision-makers to select the most
feasible OFR. The research aimed to achieve the following objectives:
a. Determine the criteria for OFR assessment based on the literature and experts’
opinions.
b. Evaluate the factors on which OFR can be prioritized by assessing the relative
importance weight of each factor.
c. Provide the most feasible OFR selection across the available alternatives.
Phase 1: Identification of the criteria, sub-criteria and alternatives for OFR.
The main aim of phase 1 was to finalize the factors that are important for OFR prioritization.
In this phase, an extensive literature review was conducted in order to identify the factors that
are important of OFR selection. Moreover, experts’ opinions have been gathered in order to
assess whether there were any additional factors that were not covered in the literature.
Finally, in Phase 1, the available alternatives for OFR were also identified. From the
literature review and experts’ suggestions, a total of 40 factors were identified and
categorized into 7 dimensions. Thus, criteria were identified based on secondary research
based on extant literature and primary research gained from the opinions of experts.
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3.1 AHP methodology
AHP has been a widely used methodology for online retail, supplier selection, and vendor
evaluation (Bandyopadhyay, 2018; Zafar et al., 2018). Steps adopted in AHP methodology
(Saaty, 2013) have been discussed below.
Step 1: Research problem identification. The aim of the research is the identification of OFR
by interpreting various factors that lead customers to choose an online fashion store.
Step 2: Questionnaire construction for data collection: a pairwise comparison between the
factors related to the prioritization of OFR has to be done by experts on a nine-point scale as
given by Saaty (1990), shown in Table 4.
Step 3: Identification of normalized weights for supplier selection criteria. After
normalization of weights, ranking for criteria and dimensions has been done.
Step 4: Consistency evaluation. The consistency ratio (CR) is evaluated for the legitimacy of
the pair-wise comparison. CR is calculated using the mathematical expression CR = CIRI
(Saaty, 2013), where RI represents the Random Index . CR value authenticates the validity of
a pairwise comparison of the decision matrix. Consistency index (CI) is shown in Table 5 and
can be calculated using the formula CI = λmax−nn−1
, where λmaxis Principal Eigen value that
corresponds to highest Eigen value (Saaty, 1990)
Table 4. Significance of score in AHP
Score Definition1 Equally Important3 Moderately important
5 Strongly important
7 Very strongly important
9 Extremely important
2,4,6,8 Intermediate value between two adjacent judgements
Source: Saaty (1990)
Table 5. Random Consistency Index Table
Source: Saaty(1990)
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Order of matrix (n) 1 2 3 4 5 6 7 8 9 10Random index (RI) 0.00 0.00 0.52 0.89 1.11 1.25 1.32 1.41 1.45 1.49
Phase 2: Application of AHP to compute evaluation criteria, sub-criteria and dimension
weights.
After the approval of the structure of criteria and sub-criteria, the decision-makers are asked
to assign their ratings to estimate the weight of factors involved in the study. On factor
identification, the experts from the case companies assigned ratings in a pairwise comparison
matrix format to find the weights.
3.2 VIKOR methodology
VIKOR stands for VIseKriterijumska Optimizacija I Kompromisno Resenje, which means
Multicriteria Optimization and Compromise Solution. It emphasize on non-commensurable
and conflicting criteria in which the decision-maker looks for a solution that is closest-to-the-
ideal (Opricovic, and Tzeng, 2007). VIKOR has been a preferred choice of this study because
of its advantages. The alternatives determined by the VIKOR technique is closest to the ideal
solution and away from the negative ideal solution having the maximum group utility for
decision-makers (Tong et al., 2007). Also, VIKOR methodology is useful in conditions where
the decision-makers are unable to express their preferences in the beginning of the system
design (Opricovic and Tzeng, 2004). It has been used in the study to evaluate alternatives of
OFR. Various steps involved in the methodology have been discussed below.
Step 1: Linguistic variables (Table 6) used in the study has been rated for the criteria of OFR
selection. Table 4 discusses the scale used for rating linguistic variables so that pairwise
comparison of alternatives can be done.
Step 2: Decision matrix has been identified on the basis of the ratings of experts using Eq.
(1). A = 1k ∑k =1
k
A k (1)
Using Eq. (1) normalization process of decision matrix is done
Step 3: To identify f b¿ and f b
−¿¿ values using Eqs. (2) and (3) to find the best f b¿ and the worst
f b−¿¿ values.
f b¿ = Max (f ab) (2)
f b−¿¿ = Min (f ab) (3)
In the study f b¿ is the positive ideal solution and f b
−¿¿ is the negative ideal solution for the bth
criteria.
14
Step 4: Calculation of Sa and Ra for a = 1, 2,…,m using Eqs (4) and (5).
Sa = ∑b=1
n
W b[( f ¿¿b¿− f ab)/¿¿¿ (4)
Ra= Maxb[W b [( f ¿¿b¿−f ab)/¿¿¿] (5)
Sadepicts the rate of distance of alternative (ath) to the positive ideal solution; Radepicts the
rate of distance of alternative (ath) to negative ideal solution in the compromise programming
method that aids in determining the compromise solutions based on negotiated preferences of
decision makers and W brepresents the weight of each criteria which was calculated using
AHP.
Step 5: Aggregate index, Qa identification for a = 1, 2,….,m by using equation (6) and
finally an alternative that has minimum Qa value for a=1,2,3,…m is the best alternative. This
compromise solution is stable.
Qa = v[(Sa−S¿) / ¿)] +(1−v ) ¿] (6)
where S−¿=Max a Sa S−¿¿ ¿; S¿=Mina Sa S¿; R−¿=Max a R a R−¿¿¿; R¿=Mina Ra R ¿ and the solutions deduced
from max Sa depicts the “maximum group majority” and from min Ra “minimum individual
regret” of the alternative.
Step 6: On the basis of Qa values alternatives are ranked
Step 7: The minimum value of Q is identified on the basis of two conditions as given below.
Condition 1: If Q(OFR(2)) – Q(OFR(1)) >= 1
n−1 then the alternative Q(OFR(1)) depicts an
advantage where OFR(1) and OFR(2) are the alternatives; n is number of alternatives.
Condition 2: When Sa and Ra are top-ranked then the alternative Q (OFR(1)) is constant and
stable in the prioritization process.
Step 8: The best alternative is noted by selecting (A(m)) as the most preferred compromise
solution with the minimum value of Qain regards to the mentioned conditions. m here
indicates the alternatives selection (OFR1, OFR2,.. OFR5).
Table 6. The linguistic scale used to develop pair-wise comparisons.Importance intensity Linguistic Variables 1 Equally Important (EI) 2 Moderately Important (MI)3 Strongly Important (SI)4 Very Strongly Important (VSI) 5 Extremely Important (EXI)
Source: Saaty(1990)
15
3.2.1. VIKOR application using R
In the study, VIKOR has been used to rank the available alternatives for OFR. The majority
of previous studies using VIKOR as a methodology, calculate it manually using Excel sheet
or Libre software. In the present research, R programming software (Team, 2018) has been
used to apply VIKOR using the R package “MCDM” (Ceballos Martin, 2016). The software
reduces manual calculations and thus the chances of error is eliminated to a great extent.
Phase 3: VIKOR is implemented to select the most efficient OFR amongst alternatives.
In the last step, phase 3, the alternatives are evaluated for the most preferred retailer. This
was done by acquiring the ratings from the decision group. With the use of the VIKOR
technique, a priority ranking of the alternatives has been obtained.
16
Figure 1. Research framework for OFR prioritization (Source: Author’s own)
4. Application of proposed research framework for OFR prioritization
4.1 Finalization of factors that decides the selection of OFR.
The study targets OFR in India and emphasizes the factors that are important for online store
selection. The study is based on an extensive literature review as discussed in the previous
section and on the base of expert (enterprise manager or decision maker) opinions.
Enterprise managers (taken as experts in the study) can take a better decision on the basis of
17
customer’s review and feedback (Xu et al., 2011). Hence, an experienced expert’s opinion
indirectly reflects the customer’s feedback. In the initial phase, a decision group was formed
that consisted of 8 experts from case companies of online fashion stores in India (a detailed
profile is provided in Table 7) and 3 experts from academia with experience in fashion and
technology. The academic experts have bachelor’s and postgraduate degrees in subjects
related to fashion and marketing, and currently teach and research topics related to online
retailing and apparel. The opinion of the experts was obtained using a matrix comparison and
other details such as the year of the establishment of the OFR and designation were asked. A
individual face-to-face interview was conducted whereby all the details and matrix
comparison on the factors were acquired. In order to understand the status of the OFR from
which the experts have been selected, an Alexa ranking is identified from
http://www.alexa.com/ (Olteanu et al., 2013). See Table 6.
Table 7. Detail of experts from the case companies in the study.
OFR Address OFR rank in India (2018,
August)
Domain Established Designation (Experts)
Experience (Experts)
Myntra.com 66 Fashion 2007 Product Manager 5
Ajio.com 468 Fashion and space 2016 Merchandise manager
9
Shoppersstop.com 1177 Fashion and accessories
1991 Vendor Manager 8
Limeroad.com 1227 Fashion and accessories
2012 Sales Manager 5
Koovs.com 2198 Fashion and Lifestyle
2010 Retail Planner 5
Craftsvilla.com 2272 Ethnic wear 2010 Vendor Manager 6
Zivame.com 2954 Women's Intimate wear
2011 Category Manager
7
Voonik.com 2991 Women's Fashion 2013 Manager- Buying 9
OFR rank source: "alexa.com"
The experts from industry and academia have proficiency in decision-making for online and
fashion retail due to their experience in the domain. The experts’ opinions and the literature
review were synthesized, resulting in the creation of seven criteria and forty evaluation
factors (sub-criteria). Five alternatives for OFR were considered for prioritization. A
structural hierarchy for the problem is shown in Figure 2. The structure has three levels:
Level 1 discusses the goal of the study. Level 2 discusses the main criteria of the research
18
agenda. Level 3 discusses the exhaustive factors (sub-criteria) and Level 4 discusses the
available alternatives. For details see Figure 2.
Figure 2. Framework for online fashion store (OFR) prioritization among the alternatives
OFR1–OFR5. (Author’s own work)
19
4.2 AHP application to compute the local weight and global weight of factors.
AHP has been used to compute the weight of factors and categories identified in this study. A
pairwise comparison of the main categories for OFR selection has been conducted by 11
experts. An average value of all 11 opinions by experts for each pairwise comparison has
been taken in the matrix format, as shown in Table 8.
Table 8. Pairwise comparison of main category for OFR selection.
WA POC PA WF TI HM WI Weight RankWA 1.000 0.530 0.240 0.260 3.200 0.480 0.164 0.049 5POC 1.887 1.000 0.232 0.460 5.300 2.740 0.210 0.088 4PA 4.167 4.310 1.000 2.400 6.800 7.200 0.530 0.258 2WF 3.846 2.174 0.417 1.000 5.800 5.400 0.210 0.151 3TI 0.313 0.189 0.147 0.172 1.000 0.524 0.114 0.025 7
HM 2.083 0.365 0.139 0.185 1.908 1.000 0.152 0.047 6WI 6.098 4.762 1.887 4.762 8.772 6.601 1.000 0.382 1
Principal Eigen Value (λ) = 7.4361, CI = 0.0727; CR=5.51% (Source: Author’s own)
After the pairwise comparison, it was indentified that the categories: Webstore image (WI),
Product attributes (PA) and Webstore facilities (WF) were ranked as the top three in terms of
priority. Although, all the main criteria are important considerations for OFR selection, a
ranking in term of importance can help retailers to focus on the most preferred one.
Furthermore, the seven individual categories were compared pairwise for their factors. An
average of the ratings provided by all eleven experts has been taken in the pairwise
comparison matrix as shown from Table 8.1 to Table 8.7.
A pairwise comparison of all the sub-criteria within the main criteria “Website Attribute”
reveals that the sub-criteria E-store niche (WA6), Homepage design (WA5) and Visual
Design (WA1) are the top three factors. Also, the CI is 0.0613 which is less than the max
limit of 0.1 and CR is 4.94%. Therefore, the results obtained by the pairwise comparison are
consistent and reliable.
Table 8.1. Comparison matrix for the factor “Website Attribute”.
WA1
WA2
WA3
WA4
WA5
WA6
Weight
Rank
WA1
1.000
4.300
2.300
4.650
0.480
0.260 0.147 3
WA 0.23 1.00 0.35 0.46 0.16 0.12 0.034 6
20
2 3 0 0 0 4 4WA
30.43
52.85
71.00
03.20
00.22
00.21
4 0.085 4
WA4
0.215
2.174
0.313
1.000
0.153
0.142 0.044 5
WA5
2.083
6.098
4.545
6.527
1.000
0.310 0.249 2
WA6
3.846
8.065
4.673
7.042
3.226
1.000 0.442 1
Principal Eigen Value (λ) = 6.3064, CI = 0.0613; CR=4.94%(Source: Author’s own)
The factors related to “Post order convenience” have been ranked in Table 8.2. The sub-
criteria Flexible return policy (POC4), On-time delivery (POC3) and Easy order cancellation
(POC5) are ranked in the top three positions. CI = 0.0752 and CR=6.72% and thus, the
results are reliable and consistent.
Table 8.2. Comparison matrix for the factor “Post order convenience”. PO
C1POC2
POC3
POC4
POC5
Weight
Rank
POC1
1.000
3.200
0.220
0.164
0.310 0.071 4
POC2
0.313
1.000
0.210
0.118
0.250 0.039 5
POC3
4.545
4.762
1.000
0.320
3.200 0.257 2
POC4
6.098
8.503
3.125
1.000
4.200 0.497 1
POC5
3.226
4.000
0.313
0.238
1.000 0.136 3
Principal Eigen Value (λ) = 5.301, CI = 0.0752; CR=6.72%(Source: Author’s own)
The main category “Product attributes” has six sub-criteria out of which the top three sub-
criteria according to the AHP methodology are Price (PA6), Product availability (PA1) and
Wide range and categories (PA3). The Consistency Ratio (CR) is less than 10% and CI is
also less than 0.1 so the decision-making using AHP is consistent and valid.
Table 8.3. Comparison matrix for the factor “Product attributes”. PA1 PA2 PA3 PA4 PA5 PA6 Weight Rank
PA1 1.000 4.300 2.20
0 6.500 5.400 0.470 0.265 2
PA2 0.230 1.000 0.45
0 4.600 2.800 0.240 0.095 4
PA3 0.450 2.220 1.00
0 6.200 4.600 0.260 0.160 3
PA 0.150 0.220 0.16 1.000 0.340 0.12 0.029 6
21
4 0 0PA5 0.190 0.360 0.22
0 2.940 1.000 0.163 0.051 5
PA6 2.130 4.170 3.85
0 8.330 6.130 1.000 0.401 1
Principal Eigen Value (λ) = 6.3017, CI = 0.0603; CR=4.87%(Source: Author’s own)
There are seven sub-criteria in the main category “Webstore Facilities”. An AHP
methodology was applied to make a pairwise comparison. The results of the application are
consistent and reliable as the Consistency Index (CI) is 0.0931. The top three sub-criteria
identified in the study are Low cost shipping (WF5), Multiple payment options (WF6) and
Various delivery options (WF7).
Table 8.4. Comparison matrix for the factor “Webstore Facilities”. WF1 WF2 WF3 WF4 WF5 WF6 WF7 Weight Rank
WF1 1.000 4.300 0.524 3.200 0.162 0.264 0.254 0.067 5
WF2 0.233 1.000 0.260 0.310 0.119 0.144 0.160 0.024 7
WF3 1.908 3.846 1.000 4.100 0.280 0.246 0.250 0.089 4
WF4 0.313 3.226 0.244 1.000 0.143 0.162 0.264 0.039 6
WF5 6.173 8.403 3.571 6.993 1.000 2.300 3.800 0.368 1
WF6 3.788 6.944 4.065 6.173 0.435 1.000 2.300 0.241 2
WF7 3.937 6.250 4.000 3.788 0.263 0.435 1.000 0.171 3
Principal Eigen Value (λ) = 7.5585, CI = 0.0931; CR=7.05%(Source: Author’s own)
Comparison matrix for the main criteria “Tactile Information” is provided in Table 8.5. The
top three sub-criteria of the AHP pairwise comparison by decision-makers are Multiple
Enlarged images (TI1), Modelled product (TI2) and Extensive product details (TI4). The
comparison is consistent and reliable as the value of C.I is less than 0.1
` Table 8.5 Comparison matrix for the factor “Tactile Information”. TI1 TI2 TI3 TI4 TI5 Weight Rank
TI1 1.000 3.200 7.800 4.230 6.800 0.500 1TI2 0.313 1.000 5.700 3.400 5.800 0.274 2TI3 0.128 0.175 1.000 0.246 0.320 0.038 5TI4 0.236 0.294 4.065 1.000 2.400 0.120 3TI5 0.147 0.172 3.125 0.417 1.000 0.068 4Principal Eigen Value (λ) = 5.2876, CI = 0.0719; CR=6.42%(Source: Author’s own)
22
In the main category, “Hedonic motivation”, the three top sub-criteria are identified as
Special sales and deals (HM5), Discounts or cashback (HM3) and Previous experience with
the shop (HM6). The matrix is made using a pairwise comparison AHP methodology by
decision-makers. CI = 0.0589; CR=4.75% which shows that the matrix is reliable and
consistent.
Table 8.6. Comparison matrix for the factor “Hedonic motivations”. HM
1HM
2HM
3HM
4HM
5HM
6Weig
htRan
kHM
11.00
00.52
00.26
03.20
00.14
40.26
0 0.058 5
HM2
1.923
1.000
0.360
4.300
0.170
0.560 0.091 4
HM3
3.846
2.778
1.000
5.600
0.510
3.100 0.244 2
HM4
0.313
0.233
0.179
1.000
0.121
0.176 0.030 6
HM5
6.944
5.882
1.961
8.264
1.000
4.800 0.439 1
HM6
3.846
1.786
0.323
5.682
0.208
1.000 0.137 3
Principal Eigen Value (λ) = 6.2945, CI = 0.0589; CR=4.75% (Source: Author’s own)
Finally, the main factor “Webstore image” has also been compared pairwise and the matrix is
reliable as CI < 0.1. The top three sub-criteria are identified as Online shop recognition WI3,
Reputation of store WI1 & Online ratings and review WI4.
Table 8.7. Comparison matrix for the factor “Webstore image”. WI1 WI2 WI3 WI4 WI5 Weight RankWI1 1.000 4.300 0.320 3.200 6.800 0.267 2WI2 0.233 1.000 0.162 0.530 3.100 0.074 4WI3 3.125 6.173 1.000 3.800 8.200 0.489 1WI4 0.313 1.887 0.263 1.000 6.800 0.136 3WI5 0.147 0.323 0.122 0.147 1.000 0.034 5Principal Eigen Value (λ) = 5.2656, CI = 0.0664; CR=5.93% (Source: Author’s own)
After obtaining the weight of the categories and factors, the global weight can be calculated
by multiplying the category weight and factor weight. Based on the global weight, the
ranking of the factors can be obtained as shown in Table 9.
Table 9. Global weight and rank for the factors of OFR selection.
23
Category for OFR selection
Relative weight of category
Factors
Relative weight of factors
Relative Rank
Global weight
Global Rank
WA 0.049
WA1 0.147 3 0.0072 25WA2 0.034 6 0.0017 38WA3 0.085 4 0.0041 31WA4 0.044 5 0.0021 36WA5 0.249 2 0.0122 20WA6 0.442 1 0.0216 14
POC 0.088
POC1 0.071 4 0.0062 28POC2 0.039 5 0.0034 33POC3 0.257 2 0.0226 13POC4 0.497 1 0.0437 7POC5 0.136 3 0.0120 21
PA 0.258
PA1 0.265 2 0.0685 4PA2 0.095 4 0.0245 12PA3 0.160 3 0.0412 8PA4 0.029 6 0.0074 24PA5 0.051 5 0.0130 17PA6 0.401 1 0.1033 2
WF 0.151
WF1 0.067 5 0.0102 23WF2 0.024 7 0.0036 32WF3 0.089 4 0.0135 16WF4 0.039 6 0.0059 29WF5 0.368 1 0.0555 5WF6 0.241 2 0.0363 9WF7 0.171 3 0.0258 11
TI 0.025
TI1 0.500 1 0.0127 19TI2 0.274 2 0.0070 26TI3 0.038 5 0.0010 40TI4 0.120 3 0.0031 34TI5 0.068 4 0.0017 37
HM 0.047
HM1 0.058 5 0.0027 35HM2 0.091 4 0.0043 30HM3 0.244 2 0.0116 22HM4 0.030 6 0.0014 39HM5 0.439 1 0.0208 15HM6 0.137 3 0.0065 27
WI 0.382
WI1 0.267 2 0.1020 3WI2 0.074 4 0.0284 10WI3 0.489 1 0.1866 1WI4 0.136 3 0.0519 6WI5 0.034 5 0.0128 18
Source: Author’s own
4.3 VIKOR application to prioritize the online fashion store.
24
After obtaining the weights of each factor in terms of their importance for online fashion
store selection, the research attempts to estimate the best possible alternative. The
prioritization and selection of the most feasible alternatives are done using the VIKOR
method. The data for the VIKOR has been collected from experts and the average value of all
pairwise comparison has been used in the study. The aggregate of expert opinions (see Table
10) are calculated using the equation 1.
Table 10. Aggregate comparison matrix for alternativesAlternative
s WA1 WA2 WA3 WA4 WA5 - - WI3 WI4 WI5
OFR1 3.2 3.5 1.25 3.2 3.24 - - 3.4 2.6 2.5OFR2 4.1 1.75 3.5 2.8 2.2 - - 3.4 2.4 2.4OFR3 2.6 4.2 2.8 3.8 4.1 - - 3.6 3.8 2.2OFR4 4.2 2.25 4.2 1.5 1.5 - - 1.6 3.6 3.6OFR5 3.6 3.6 3.4 2.4 2.8 - - 3.6 2.8 3.8
(Source: Author’s own)
On obtaining the weight for factors and the comparison matrix for alternatives, the VIKOR
methodology using R was applied. A screenshot of the result of the VIKOR analysis as
calculated in R is shown in Figure 3 and the results are rewritten in Table 11.
Figure 3. Screenshot of R output for VIKOR analysis.
Table 11. VIKOR analysis for ranking of alternativesAlternatives S R Q Ranking
OFR1 0.5241 0.1033 0.4773 4OFR2 0.4662 0.1020 0.4112 3OFR3 0.2328 0.0685 0.0326 1OFR4 0.7072 0.1866 1.0000 5OFR5 0.3488 0.0603 0.1222 2
(Source: Author’s own)
In the study, “S” depicts the positive ideal solution, “R” tells the negative ideal solution and
“Q” indicates the optimal compromise solution. On VIKOR application the descending order
25
of ranks has been obtained for OFR which are as follows: OFR3 > OFR5 > OFR2 > OFR1 >
OFR4. OFR3 has been identified as the most feasible and efficient alternative.
5. Results and Discussion
The ranking of the main criteria and factors for OFR selection for the case companies are
displayed in Table 8. For online fashion store selection, the category Webstore image (WI)
got the maximum weight (0.382) followed by Product attributes (PA) (0.258), Webstore
facilities (WF) (0.151) and others. The factors that ranked first within their categories are E-
store niche (WA6) (0.442), Flexible return policy (POC4) (0.497), Price (PA6) (0.401), Low
cost shipping (WF5) (.368), Multiple Enlarged images TI1 (0.500), Special sales and deals
(HM5) (0.439) and Online shop recognition (WI3) (0.489).
The top five factors identified for case companies in the analysis are Online shop recognition
(WI3), Price (PA6), Reputation of the store (WI1), Product availability (PA1) and Low-cost
shipping (WF5). Also, the five factors that got the least priority for OFR selection are
Responsive Web Design (WA4), Product video (TI5), Navigation Design (WA2), Shopping
fun and enjoyment (HM4) and Visual Merchandise (TI3).
The “Webstore Image” dimension holds the first position for OFR selection. Research
suggests that webstore image influences the purchasing behavior of customers (Chen and
Teng, 2013; Van der Heijden and Verhagen, 2004). There were five factors within this
category and the factor “Online shop recognition” (WI3) got the topmost priority within the
group. Online shop recognition defines the customer’s ability to recall a particular online
store for a certain product category (Park and Kim, 2003). Store loyalty is an important factor
for the fashion industry so this factor plays a vital role in store selection. The second rank
factor in this category is “Reputation of the store” (WI1) which got the third rank in global
ratings. Consumer purchase intention are directly related to the reputation of the online store
(Kim and Lennon, 2013), therefore OFRs must work on store attributes that enhance the
reputation of their store.
The “Product attribute” category obtained the second rank in the study according to the
experts’ suggestion by case companies. Price (PA6) received the first rank in the category
and second rank in the global ratings. Price has been an instrumental factor in consumers’
26
choice of fashion store (Gugnani and Choudhary, 2017). The next factor on the rating scale
was “Product availability” (PA1) which has been ranked fourth on global scale rating. Due to
the diverse product range in fashion assortments, their availability at a store can significantly
improve the perception of customers towards that shop. Thus, a higher level of product
availability in fashion retailing increases customer demand (Namin et al., 2017).
“Webstore facilities” (0.151) was ranked as the third category for OFR selection. The most
preferred factor in this category was “Low-cost shipping” (WF5). In a study for online store
repurchase intention amongst customers, “shipping charges” was identified as one of the
most important factors that influence repurchase intention (Roy Dholakia and Zhao, 2010).
The second most important factor that defines webstore facilities is “Multiple payment
options” (WF6). Experts suggested that multiple payment options like cash on delivery
(COD), wallets, coupons and other formats are very important for customer convenience and
purchase intention.
The category “Post order convenience” ranked after Webstore facility on the rating scale and
experts also gave due importance to this category. The prime factor found in this category is a
Flexible return policy (POC4). Research highlights the importance of an easy return policy in
online fashion retailing (Chang et al., 2013; Oghazi et al., 2018). The next important factor
identified within the category is on-time delivery (POC3). Experts emphasize that fashion
purchases are usually time-pressured as customers purchase products for specific occasions.
Therefore, if the item is delivered late then it may no longer be of any use to the consumer,
resulting in the item being returned. Thus, research emphasizes the importance of on-time
delivery (Lawrence and Tar, 2010; Roy Dholakia and Zhao, 2010; Hasan, 2016; Cao et al.,
2018).
The fifth category that is important for online fashion store selection is “Webstore
Aesthetics” (WA). This category deals with the appearance and functionality of the webstore.
Research highlights the importance of webstore aesthetics for e-store success (Luo et al.,
2012). According to experts, E-store niche (WA6) is the most important criteria in webstore
aesthetics. E-store niche gives the customer an opinion about the choice of product they are
looking for. E-store niche can be built by designing the website to appeal to a specific gender,
age group, income class and product category. The second rank in this category is homepage
27
design (WA5). Homepage design plays an important role in retaining the customer as it
delivers the information sought by them (Kluge et al., 2013; Yoo and Kim, 2014).
The last two categories which are vital for OFR selection as per case companies are “Hedonic
motivations (HM)” and “Tactile Information (TI)”. Within both categories, the top factors are
“Special sales and deals” (HM5) and “Multiple enlarged images” (TI1). Experts suggested
that special sales and deals are linked to hedonic motivations (i.e value shopping) and that
they encourage customers to purchase (Arnold and Reynolds, 2003), an aspect that is very
important in Indian fashion retailing. Researchers have emphasized the importance of product
information provided by multiple enlarged images (Kim and Lennon, 2010; Song and Kim,
2012; Verhagen et al., 2013). Indeed, fashion shopping, the minute details of the product are
important to the customer as they enhance the tangible effects, thus, “multiple enlarged
images” improves the haptic information of fashion products.
Although it is difficult to determine and develop a list of factors crucial for an online fashion
retailer (OFR), the suggested research framework will be helpful for practitioners in order to
understand customer preferences as suggested by experts in a logical way. The study used the
integrated AHP-VIKOR methodology to clearly explain the factors and select the best
possible alternative amongst the five options. VIKOR analysis explained the rankings of the
most favourable OFR attributes whilst simultaneously acknowledging the importance of
factors identified using AHP. The decreasing order of alternatives is OFR3 > OFR5 > OFR2>
OFR1> OFR4, as shown in Table 10.
6. Sensitivity Analysis.
The outcome of the sensitivity analysis depicts the robustness and stability of the framework.
Sensitivity analysis helps to identify the variation in the rank of alternatives when the value
of maximum group utility is varied from v= 0.1 to v=1.0 (Luthra et al., 2017). The solution is
stable within a decision-making process, for voting by a majority rule v>0.5 is used, for
consensus v 0.5 and with veto’ v < 0.5. In the study v is the weight of strategy adopted in
decision-making, also known as ‘‘the maximum group utility’’ (Opricovic and Tzeng, 2004).
In the analysis, it can be seen that the rank of OFR1, OFR2, and OFR4 remain constant for all
the values of v (0.1 to 1.0). Also, OFR3 and OFR5 obtained a constant ranking of 1 and 2
28
respectively, for other than two values of v (0.1 and 0.2) as shown in Figure 4. This confirms
the stability of our model as it can be seen in Table 12.
OFR3, OFR5, and OFR2 are the preferred choices of OFR by customers as suggested by the
online fashion industry experts.
Table 12. Sensitivity analysis for online fashion store prioritization
Alternatives Q at v = 0.1 Rank Q at v = 0.2 Rank Q at v = 0.3 Rank Q at v = 0.4 Rank Q at v = 0.5 RankOFR1 0.368 4 0.395 4 0.423 4 0.450 4 0.477 4OFR2 0.347 3 0.363 3 0.379 3 0.395 3 0.411 3OFR3 0.059 2 0.052 2 0.046 1 0.039 1 0.033 1OFR4 1.000 5 1.000 5 1.000 5 1.000 5 1.000 5OFR5 0.024 1 0.049 1 0.073 2 0.098 2 0.122 2Alternatives Q at v = 0.6 Rank Q at v = 0.7 Rank Q at v = 0.8 Rank Q at v = 0.9 Rank Q at v = 1.0 RankOFR1 0.505 4 0.532 4 0.559 4 0.587 4 0.614 4OFR2 0.427 3 0.444 3 0.460 3 0.476 3 0.492 3OFR3 0.026 1 0.020 1 0.013 1 0.007 1 0.000 1OFR4 1.000 5 1.000 5 1.000 5 1.000 5 1.000 5OFR5 0.147 2 0.171 2 0.196 2 0.220 2 0.244 2
(Source: Author’s own)
29
v = 0.1 v = 0.2 v = 0.3 v = 0.4 v = 0.5 v = 0.6 v = 0.7 v = 0.8 v = 0.9 v = 1.00
1
2
3
4
5
Ranking of OFS1-5 for v = 0.1 to v = 1.0
OFS1 OFS2 OFS3 OFS4 OFS5
Axis Title
Rank
Figure 4. Sensitivity Analysis. (Author’s own work)
7. Implications
There are several key implications of this study. Existing research focuses on online retail
selection attributes and their comparison (Hsu et al., 2010; Liu et al., 2015; Kabir et al., 2012;
Masudin et al., 2016; Rouyendegh et al., 2018). The present study builds on this literature and
applies these attributes to the Indian online fashion retailing context in order to assess
whether there are any unique aspects to this market. Moreover, the present study goes further
by classifying and ranking all the different online retailing aspects to uncover the most
important areas for fashion retailers to focus on when building a successful online strategy.
Theoretically, the study identifies the factors for OFR selection which are different from
online stores in another domain. The findings can be used by academics and practitioners to
study online fashion retailing in order to gain the competitive edge required for success. The
study discovered new factors that are unique to online fashion retailers based on expert
interviews, and these factors can be tested in future research in order to investigate this
further. Factors suggested and validated by industry and academic experts for OFR selection
used in this study are e-store niche, easy order cancellation, the latest trend, style guide,
30
branded products, multiple payment options, celebrity endorsement, special deals and
previous experience with the store.
The study also has several practical implications, useful for online fashion retailers. Firstly,
the model evaluated in this study can be used by fashion retailers to enhance consumers’
online shopping experience. It can help them to upgrade their services and product attributes
in order to improve customer satisfaction and retention. Secondly, factors and their weight
identified in the study can be used by the management of existing online fashion stores in
order to make improvements to their webstore attributes. The heavily weighted factors can be
emphasized and worked upon to improve the status of the OFR. Thirdly, new entrants in
online fashion retail can use this model to prioritize factors and serve customers efficiently.
Further, the weights identified can be used to emphasize the most desirable factors for
customers in order to improve satisfaction towards OFR. Fourthly, market researchers and
trend analysts can use this model due to its robustness and stability to rank and prioritize the
OFR.
8. Conclusion
Studies have investigated online store attributes and buying behaviour (Srivastava et al.,
2018), assessed online stores and ranked them (Rouyendegh et al., 2018; Singh et al., 2016).
and identified and ranked online retail performance (Dey et al., 2015; Kabir et al., 2012).
The present study builds on the extant literature and provides the following novel findings:
8 new factors that are contributed by experts
8 new attributes of online fashion retail stores are identified based on expert
interviews: E-store niche (WA6); Easy order cancellation (POC5); Latest trend (PA4);
Branded products (PA5); Style guide (WF4); Multiple payment options (WF6);
Special sales and deals (HM5); Previous experience with the shop (HM6).
The seven main criteria: Webstore Aesthetics (WA), Post order convenience (POC),
Product attributes (PA), Webstore facilities (WF), Tactile Information (TI), Hedonic
motivations (HM) and Webstore image (WI) cover almost every aspect on the basis of
which OFR can be assessed. Previous research has missed some of these important
categories.
31
Moreover, in terms of Indian context this is the first study, to the author’s knowledge,
that provides data from decision-makers, exclusively from the prominent online
fashion retailers of India.
Table 13. provides a brief overview of the comparison of current study with respect to
previous studies.
Table 13. Comparison of current study with previous study in online retail
Authors (year) Issues discussed Methodologies
Current Study 40 sub-criteria are identified and prioritised for evaluating OFR and the most preferred alternatives of OFR are ranked AHP-VIKOR
Srivastava et al. (2018) Discussed online store attributes and buying behavior across gender. Fuzzy AHP and TOPSIS
Rouyendegh et al. (2018). The performance of three online stores has been evaluated. AHP-FTOPSIS
Liu et al. (2017). Evaluation of success factors of Online shops with examples AHP-TOPSIS
Kahraman et al. (2017) Selection of B2C marketplace and modeling. fuzzy AHP Singh et al. (2016). Assessing top E-store in India. ANFIS-AHPValmohammadi and Dashti (2016) Barriers to implementation of online shopping. Fuzzy ANP and ISM
Masudin et al. (2016) Two websites were compared for usability factors. AHP-TOPSISLiu et al (2015) E-commerce alternatives have been ranked fuzzy AHP Dey et al. (2015) Online retail evaluation model developed AHP-TOPSIS
Kalelkar et al. (2014) To understand the dynamics of main factors in top Online shops of India. AHP
Chiu et al. (2013) To improve the business of online E-stores. DANP-VIKOR Kabir et al. (2012) Online retail performance among 5 alternatives. AHP-TOPSIS
(Source: Author’s own)
Online fashion retailing has grown exponentially in the past two decades and is now starting
to gain huge traction in India. Yet the highly competitive nature of it means that fashion
retailers need to provide a superior online shopping experience for their customers in order to
be successful. This study identified a total of 40 factors, divided into seven categories, that
fashion retailers need to address in order to be successful, and then prioritized these in order
of their importance and significance. This has filled a gap in the academic literature as no
previous study has ranked all the factors relevant to creating a successful e-commerce store.
The model created using an integrated AHP-VIKOR methodology can be used by
practioners when developing their online fashion business in order to help them prioritize the
areas that are the most important in terms of a successful strategy.
32
There are several limitations to the study. The study is exclusive to the fashion industry
(apparel, fashion accessories, shoes) so the findings may not be generalizable to other product
categories such as electronics, books and automobiles. Although 40 factors have been
identified, there is a possibility that other relevant factors could have been missed.
Furthermore, the cases discussed are based in India and so the findings may be limited to the
Indian market. Future research could be conducted to compare these findings to other
countries and cultures.
Future research could also be conducted using different MCDM techniques like ISM and
DEMATEL to identify the interrelationship, strength of relationship and direction among the
factors. Further PROMETHEE and TOPSIS may be used for OFR selection and
prioritization. The fuzzy factor in the study has not been considered so fuzzy-based approach
can be applied in future. The factors that are similar could be consolidated to a fewer number
of factors using EFA (Exploratory Factor Analysis) and the model identified in the study can
be validated using statistical modelling techniques like SEM (Structural Equation Modelling).
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