Antecedents of Apparel Subscription Box Choice:
Understanding Predictors to Use Apparel
Subscription Service
Chapter 1.
Introduction
1.1. Global Fashion Retailing Overview
Retailing refers to a set of activities that facilitate the exchange of goods and services to
end consumer (American Marketing Association, 2018, p. 1). The retail industry significantly
influenced the economy of most countries and it has affected the way consumers have lived
throughout history (McCormick, et al., 2014; McGoldrick, 2002). In 2018, retailing was
projected to account for $24.86 trillion of the world’s monetary exchange (Statista, 2018). The
retail industry accounts for 31% of the world's Gross Domestic Product (GDP) and engages
billions of individuals throughout the globe. In the U.S. alone, retail sales totaled $ 1.27 trillion
in the third quarter of 2017 (U.S. Census Bureau, 2017).
A single point of contact between the retailer and consumer can be a physical retail
location, an internet shopping site, or even a cell phone (McCormick et al., 2014). In recent
years the retailing industry has undergone great change. Technology has opened the way for
new retail formats (eMarketer Report, 2017; Hewson & Stewart, 2016; Holte, Gao & Brooks,
2015). In 1995, the in-store or brick and mortar sales format accounted for $2.2 trillion, the
majority of retail sales, followed by the catalog format ($62 billion), cable TV shopping ($16
billion) and the World Wide Web ($1 billion) (Palmer, 1997). By 2017, e-commerce had gained
tremendous momentum offering retailers’ a new avenue of contact with consumers contributing
1
to the $22.74 trillion in retail sales (Statista, 2017a). This market is expected to continue with
steady growth projected to be at least $28 trillion by 2019 (eMarketer Report, 2017; Orendorff,
2017; U.S. Census Bureau, 2017). Worldwide retail e-commerce sales are predicted to increase
and surpass 16% by 2021, with sales hitting $4.48 trillion (eMarketer Report, 2017).
Globally, almost half of the population have access to the Internet (Statista, 2018). This
advancement of technology has created a new marketing landscape for various business entities
and has created new outlets for retailers (Hewson & Stewart, 2016; Holte et al., 2015). Online
business has gained tremendous momentum with the increase of Internet users (Blázquez, 2014;
McCormick et al., 2014). In North America, e-commerce retail sales saw a 15.6% increase in
2017, and reached $423.34 billion in sales. The region will see steady double-digit development
through 2020, fueled by expanded investment from existing advanced buyers, development into
modern categories such as basic needs, and developing e-commerce deals (eMarketer Report,
2017).
In 2018 apparel items comprised the largest sales segment of the global marketplace
(Statista, 2018). Fashion related items including apparel and accessories accounted for $525
billion in sales globally in 2018 (Statista, 2018). By 2021, a total increase of 19.2 percent from
2016 is projected in global clothing retailing (Statista, 2017b). Forecasters are expecting an
annual rate of growth of 10.7 percent by 2022 (Statista, 2018).
Fashion e-commerce includes the online purchase and sale of fashion products.
Goldsmith and Goldsmith (2002) predicted that among the various items purchased online,
apparel would become an increasing trend. A review of sales data in 2014 proved this forecast
correct. Apparel products had become the highest revenue generators amongst online product
2
categories in the USA, Europe, and Asia (McCormick et al., 2014; Srinivasan, 2015). In the
U.S.
alone, online apparel sales reached $ 63.29 billion in 2016 and are projected to reach $96.41
billion by 2021. In the period between 2015 and 2021, the CAGR for the revenues from
ecommerce fashion retailing in the U.S. are estimated at 7.83 percent. (Statista, 2018). The
online sales potential for apparel is impressive. However, retailers are developing various
business models in order to capitalize on the avenue’s potential and little is currently known
about consumer acceptance regarding these business models.
New business models are interested in changes taking place in consumer behavior
(Verhoef, Stephen, Kannan, Luo, Abhishek, Andrews & Hu, 2017). Today, consumers have
higher service and product expectations than ever before. Companies are finding it increasingly
difficult to differentiate their brand from their rivals. Therefore, the efficient provision of
products or services and gaining of brand loyalty are becoming increasingly important
(Maruping, Bala, Venkatesh, & Brown, 2017; Verhoef et al., 2017). This has become more
challenging for retailers because of the disruptive e-commerce platform.
The internet is shaping every aspect of society including the retail environment (Kim,
Lee, Mun, & Johnson, 2017). Clothing is the second most popular online purchase among U.S.
consumers (Statista, 2018). Due to the advancement of the electronic retailing over the last
decade along with the ability to compete outside the local marketplace there has been a surge in
online fashion merchandising (Dai, Forsythe & Kwon, 2014; Kim et al., 2017). E-commerce
has given consumers' endless options. Rather than only having access to the items carried in
3
local stores, consumers are now able to purchase products worldwide (Martin, Mortimer &
Andrews, 2015; Verhoef, Kannan, & Inman, 2015).
Baldwin, Forslid and Ito (2015) argued that because of the globalization of the
marketplace, value added services are becoming more popular. This shift has been called
‘servicification’ and refers to the retailing approach where businesses provide services to the
customer rather than products alone (Baldwin et al, 2015). The phenomena of ‘servicification’
has been under investigation by researchers for the past three decades (Baldwin et al, 2015;
Baumgartner & Wise 1999; Oliva & Kallenberg 2003; Vargo & Lusch, 2004). Simultaneously,
technological advancements in communication and manufacturing have paved the way for
manufacturers and brands to offer customized services and products to consumers (Yang, Lu,
Chau & Gupta, 2017). Advancing technology has also introduced the use of ‘Big Data
Analytics’ into the retail sector that refers to the holistic approach of the 5V’s (i.e. volume,
diversity, velocity, veracity and value) of data dimensions management, processing and analysis
(Wamba et al., 2017). To create sustainable value and competitive advantages, web-use
behavior of consumers can be assessed by Big Data Analytics and predictions can be made on
which services consumers prefer. This analysis allows firms to provide additional services
targeted to the individual consumer (Russom, 2011).
1.2. Subscription Services: New Landscape in Global Retailing
The Subscription Business Model (SBM) has become an important trend in global
retailing. In subscription-based retailing customers register for products as well as services that
can be delivered at regular or irregular intervals (Bischof, Boettger & Rudolph, 2019; Woo &
Ramkumar, 2018). Subscription services are paid in advance in lieu of payment for each
4
product or service over a period. In case of online retailing, Subscription based Online Service
(SOS) providers offer a customized merchandise box for weekly, monthly, or ‘need basis choice
(Bischof et al., 2019). These subscription providers deliver merchandise or services directly at
customers’ door (Woo & Ramkumar, 2018). Currently, more than 2,000 SOS firms are
operating in almost every category of products or services (Woo & Ramkumar, 2018). This
retailing format was named "little box retailing" by the Washington Post (Jayakumar, 2014). It
is being promoted as the solution to the challenge of making daily living easier and more
convenient for consumers (Belk, 2014; McCarthy, Fader, & Hardie, 2017). The Chief Industry
Analyst for the NPD Group, Marshall Cohen, predicted that subscription services will become a
retail alternative to electronic commerce or physical stores (Ewen, 2017). Dasteel, Hartman and
Legrande (2016) stressed the importance of addressing changing consumer needs through the
business model of subscription.
The subscription-based electronic retailing market has grown tremendously between the
years 2013 to 2018 (Chen, Fenyo, Yang & Zhang, 2018; Columbus 2018). Major retailers
reported that subscription sales exceeded $2.8 billion in 2018, compared to $60.0 million in
2013 (Kestenbaum, 2018; Woo & Ramkumar, 2018). Research has shown that most current
ecommerce subscribers are aged 25 to 44 years, have income from $50,000 to $100,000 a year,
and live in urban areas of the Northeast U.S. (Columbus, 2018). Fifteen percent of online
shoppers have subscribed to one or more subscriptions (Columbus, 2018). In 2018, Amazon®,
the Dollar Shave Club®, the Ipsy®, the Subscribe & Save®, the Blue Apron® and the
Birchbox® were the most popular subscription sites (Columbus, 2018).
5
Apparel subscription services focus on offering subscription based services to apparel
and fashion consumers. These services are designed to assist customers locate and purchase
desired apparel items. Consumers have a choice of membership level for a specific cost or
nocost and for a specified time period or no specified time period (weekly, 3 weeks interval,
monthly, two month interval, three month interval, or no specific intervals) (MSA, 2018). A
potential member will fill out a detailed questionnaire regarding their style preferences, age,
activities, size, fitting issues, and budget (Woo & Ramkumar, 2018). A professional clothing
stylist or curator is then assigned to work with the member to assist in clothing choices (MSA,
2018). Based on the membership level or choice of tier the customer will receive a box
containing a certain number of clothing or fashion items which have been specifically chosen for
them by their personal stylist (MSA, 2018). Members are only charged for the items they keep.
Any unwanted items can be returned at no cost in a prepaid shipping envelope provided by the
subscription service (Tao & Xu, 2018).
Examples of this type of subscription service (see Appendix A) include: Stitch Fix®,
Trunk Club®, Frank and Oak®, The Mr. & Ms. Collection®, MM.LaFleur®, Dia & Co. ®,
LeTote®, Rent the Runway®, Bombfell®, ThreadBeast®, Trendy Butler®, and Shirt cycle®.
Many of these providers offer their service to both men and women (Stitch Fix, Trunk Club,
Frank and Oak, The Mr. & Ms. Collection) whereas others carry items for only men or women.
Services are also available for narrowly targeted niche markets such as Dia & Co. which caters
to plus size women, and LeTote which sells maternity wear (MSA, 2018).
Currently, many innovative business entrepreneurs are successfully offering subscription
services in the context of apparel and fashion. Among these companies, one of the most
6
successful is Trunk Club, which was founded in 2009 and acquired by Nordstrom in 2014 (Trunk
Club, 2019). Another major apparel subscription service provider is Stitch Fix. Based on its
enormous success within a short span of time, in November 2018, it became the first apparel
subscription service to go public with its stock (Stitch Fix, 2018). The growth potential for
subscription services of both menswear and women’s items is very optimistic (Wolf, 2017).
Recently, subscription services for womenswear have been experiencing booming sales (Chen,
Fenyo, Yang, & Zhang, 2018). By offering clients an innovative way to obtain products,
fashion and beauty SOS companies are currently serving over 100 million subscribers (MSA,
2018; Svensson & Hölder, 2016).
Another form of online apparel subscription service is offered by the company Threadup.
Thread-up appeals to consumers interested in sustainability through recycling while providing
the latest fashion. Unlike the previously mentioned subscription services Thread-up offer fault
free, high end, second hand items, rather than new products. To use the subscription service,
consumers must register an account with the respective providers’ platform directly or through a
third party service (a "TPS") such as Amazon® or Facebook®. By enabling TPS access,
consumers are allowing companies to pass their login information to these service providers for
this purpose. In addition, consumers also need to allow them to access, make available, and
store any information, content, or other materials that consumers have provided to or have stored
in their TPS account.
1.3. Problem Statement
Gilmore and Pine claimed in 2007 that consumers care less about the product than about
the overall consumption experience. It is therefore vital for companies to not only value the
7
product, but also to provide services that add value. Woo and Ramkumar (2018) mentioned
society is becoming more and more individualized and at the same time digitalized. They stated
that to make companies attractive to consumers attention should be paid to individual factors
such as assortment, product, pricing and logistics. It is difficult for enterprises to be unique in a
marketplace given the increasing pressure for lower prices, market saturation, and homogeneous
products. One of the key market issues is that products individually tailored to a customer must
be sold at a mass production price point (Jayakumar 2014).
Analysts believe that one in every four malls across the U.S. may be closed by 2022,
having become victims of changed trends, increasing wealth discrepancies, and the ability to
shop online for anything from sockets to swing sets (Farhan, 2017). By July of 2017 there were
5,300 retail store closings, including stores of long time national retailers Sears®, Macy®’s,
JCPenney® and Kmart®. In March 2017, Kmart’s parent company Sears Holdings stated in a
press release that there was "a significant doubt" that it could continue to operate all its locations
and that it planned to close 300 stores by 2018 (Farhan, 2017; Sanburn, 2017; Sanicola, 2017;
Wang, 2017). The rise of online shopping and the blow of the Great Recession (December,
2007 to June, 2009) led to a drop in sales and foot traffic at retailers such as JCPenney and
Macy’s anchor stores for many malls in the U.S. (Sanicola, 2017). Over the following years, the
in-store sales declining trend continued, and mall visit traffic decreased by 50 percent during the
years 2010 to 2013 (Sanburn, 2017). Many U.S. customers no longer want to visit stores, rather
they feel more interested to shop online (Farhan, 2017; Sanburn, 2017; Sanicola, 2017; Wang,
2017).
The demise of the traditional retail store is attributed to electronic retailing (Thompson,
8
2017). The online retailer Amazon® has aggressively expanded its offerings with market size in
U.S. surpassed $80 billion in 2018 from $16 billion in 2010. In six years, Amazon grew three
times the size of the long-established retailer Sears (Thompson, 2017). More notably reports
claimed that more than half of the U.S. families are currently subscribers with Amazon Prime
subscription service (Thompson, 2017). Additionally, several clothing companies, including
well-established brands such as Lululemon®, Urban Outfitters®, American Eagle®, and Ralph
Lauren®, have found that their stock has fallen to new multi-year lows (Thompson, 2017).
Zhong, Newman, Huang and Lan (2016) discussed the new challenges facing retailing.
It was argued that e-commerce has left the consumers’ with endless choices from around the
world. They argued that with consumers no longer restricted to items available in local stores
retailer loyalty would be reduced. The online environment would make it easier to compare
competitive online stores at the best price possible for consumers. To compete against this price
shopping, Peng and Lu (2017) suggested that companies should provide value added product
and service packages to meet their customers' needs. While Paul, Sankaranarayanan, and
Mekoth, (2016) stressed that consumers expect additional services and are searching for
platforms that can ease the entire marketing experience and cater to their individualized needs.
Apparel subscription services are in a better position to meet these unique demands of today’s
consumers.
Although online apparel subscription services are becoming popular, and consumers are
accepting them, many consumers believe that an apparel purchase needs direct customer
involvement to assess physical and performance features prior to purchase (Lee & Moon, 2015).
Researchers found that individuals shop online for specific purposes and the value customers see
9
in it varies person to person (Dillon, Buchanan, & Al-Otaibi, 2014; Shin & Baytar, 2014).
Consumers are more concerned about the product value regarding quality, price, body fit, and
design of apparel than other items (Dillon et al., 2014; Shin & Baytar, 2014). Some consumers
feel that the purchase of online apparel is particularly risky compared to traditional shopping,
due to not being able to try the clothing to evaluate size, color and handle properties of fabric
while shopping online. This uncertainty about the product and the trustworthiness of websites
are a barrier to online apparel sales (Dai et al., 2014; Holte et al., 2015).
The willingness to see, have immediate access to the product, truly feel it and take
products home immediately ranks highest amongst the basic reasons why customers want to
shop in retail outlets versus online sites (Skrovan, 2017). The ability to see, touch and feel items
is important to 66% of female shoppers reporting that trying a garment on is a decisive factor in
stores versus online shopping (Skrovan, 2017). While seeing or testing products is also the most
important reason given by men (59 percent), instant gratification is almost as important.
(Skrovan, 2017).
Consumers look for two vital dimensions (e.g. physical characteristics and performance
characteristics) of apparel products when they make a purchase decision. Physical
characteristics deal with a product’s handle properties, design, and construction whereas
performance characteristics cover aesthetic and functional attributes (Brown, 2014). With
regard to shopping apparel, aesthetic attributes (color, style, pattern, fabric and match) and
trying on offer vital information regarding purchasing decisions (Eckman, Damhorst & Kadolph,
1990; Kim & Forsythe, 2008). Researchers reported that good fit is the second most important
value for money criterion when buying clothes (Hye Park & Stoel, 2002). The inability of
10
customers to try on clothes online prohibits them from assessing these physical and performance
features prior to purchasing. This issue of apparel evaluation aspect prior to purchasing can
influence business performance for companies like apparel subscription service providers.
Customers may decide not to purchase the item, they may get dissatisfied when they find the
size and appearance do not fit or they may place multiple orders for the same item with different
sizes and eventually returning all except the one that fits best. These returns create extra
logistical costs for companies as well as less loyal consumers as they are not satisfied with their
products (Hye Park & Stoel, 2002; Kim & Niehm, 2009). Prior research indicated that the
return rate may vary at different geographic locations (Goldsmith & Goldsmith, 2002). Online
sites like Fits.me and Statista hold some statistics that demonstrate around 25% of online apparel
purchases were returned in the UK, 20 % in Germany, and 25% in Denmark. Reasons for returns
included the clothes did not fit the customers properly, or that when they tried on the item the
customer simply did not like it.
As a result of returns, online retailers ' costs have risen, and consumers are unhappy (Holte et al.,
2015). The significant reason behind these events could be that consumer requests are not
addressed during the buying process or the purchasing process does not provide a suitable path.
Given these contemporary issues, SOS fashion and beauty are more popular among
consumers than any other subscription service. Fashion and clothing shopping significantly
differ in nature from any other products or service shopping as it involves both utilitarian and
hedonic shopping motivation. That is to say, fashion and beauty shopping involve hedonic
components such as exploration of trends in shopping for Beauty Products, comparison of
consumer products, meeting friends in shopping place and so on (Arnold & Reynolds, 2003).
11
However, purchasing apparel from a subscription service might have a different
motivation than these contemporary aspects of shopping motivation. Furthermore, fashion and
clothing products have a higher personal taste than any other product for individual consumers.
In this respect, many consumers find it interesting to choose their own clothing and cosmetics
rather than to have it chosen by someone else i.e. the personal stylist of the service. It is
therefore necessary to look at which consumers use SOS for clothing and fashion items, given
the fact that the shopping involvement for the customer in a subscription service is somewhat
indirect in nature.
Understanding consumers’ needs and wants during the entire buying process as well as
analyzing antecedents that consumers consider prior to purchasing apparel using an online
subscription services will provide valuable insights as well as help determine quality services
that will help develop loyal consumers. Given that apparel subscription services are a relatively
new business model in fashion, little research has been conducted to date.
1.4. Research Purpose
The purpose of this study is to investigate antecedents of online apparel subscription services,
and to better understand consumers’ attitudes towards online subscription service items in the
context of extended Unified Theory of Technology Acceptance and Usage (UTAUT2). This
study was designed to uncover: Factors that predict why a consumer will use online apparel
subscription services, the relationship between age, gender, and shopping orientation and a
consumer’s interest in subscribing to a fashion service.
1.5. Definition of Terms
Terms Definition
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Anchor Store “An anchor store is the big department store at the mall” (Shopify,
2018, p. 1).
Apparel “Clothing, not necessarily fashionable” (Frings, 2005, p. 355). In
this dissertation the words clothes, clothing, and garments are used
interchangeably with apparel as study participants were more
familiar with those terms.
Apparel
Subscription Service
Apparel subscription service refers to online retailing platform of
apparel business that offer a customized box of merchandise to its
subscriber directly to the consumer's home for a weekly/monthly or
customers’ need base instance which has been specifically chosen
for individual customers by their personal stylist (Woo &
Ramkumar, 2018).
Big Data Analytics The holistic approach to managing, processing and analyzing the 5
V data-related dimensions (i.e., volume, variety, velocity, veracity
and value) to create actionable ideas for delivering sustained value,
measuring performance and establishing competitive advantages
(Wamba et al., 2017, p 1).
Behavioral intention
The degree to which consumer has formulated conscious plans to
use or not use online fashion subscription services (Venkatesh,
Thong, & Xu, 2012).
Behaviorism Refers to a psychological approach which emphasizes scientific and
objective methods of investigation. The approach is only concerned
with observable stimulus-response behaviors, and states all
behaviors are learned through interaction with the environment
(Mcleod, 2017).
Behaviorist
approach of
consumer behavior
study
This approach views behavior as explained by external events, and
that all things that organisms do, including actions, thoughts and
feelings can be regarded as behaviors. The causation of behavior is
attributed to factors external to the individual (Bray, 2008).
13
Terms Definition
Brick-and-Mortar
retailing
A traditional street-side business that deals with its customers faceto-
face in a physical office or store that the business owns or rents
(Investopedia, 2018, p 1).
Consumer Behavior “The study of the processes involved when individuals or groups
select, purchase, use or dispose of products, services, ideas or
experiences to satisfy needs and desires.” (Solomon, Bamossy,
Askegaard, & Hogg, 2006, p 6).
Cognitive Approach
of consumer
behavior study
In this approach the individual is viewed as an information
processor, consumers actively seeking and receiving environmental
and social stimuli as informational inputs aiding internal decision
making (Bray, 2008).
Discount Stores A department store which offers its items at a lower price than many
other retail stores. Discount stores are often able to drop their prices
due to efficient distribution methods (Business Dictionary, 2018, p
1).
Department Stores Large retail establishment with an extensive assortment in variety
and range of goods, organized into separate departments. All
departments are housed under the same roof to facilitate buying,
customer service, merchandising, and control (Business Dictionary,
2018, p 1).
Economic Man
approach of
consumer behavior
study
This approach suggests, a consumer would have to be aware of all
the available consumption options, be capable of correctly rating
each alternative and be available to select the optimum course of
action (Bray, 2008).
Franchise store A franchise is a type of license that a party (franchisee) acquires to
allow them to have access to a business's (the franchiser) proprietary
knowledge, processes, and trademarks in order to allow the party to
sell a product or provide a service under the business's name
(Investopedia, 2018, p 1).
Habit The extent to which people tend to perform behaviors automatically
because of lifestyle (Venkatesh, et al., 2012, p 161).
14
Terms Definition
Hedonic motivation The fun or pleasure derived from using online fashion subscription
services (Venkatesh, et al., 2012).
Humanistic
approach of
consumer behavior
study
This approach seek to explore concepts introspective to the
individual consumer rather than describe generic processes (Bray,
2008).
General Stores A retail store located usually in a small or rural community that
carries a wide variety of goods including groceries but is not divided
into departments (Merriam Webster, 2018, p 1).
Generation Y The generation of people born during the 1980s and early 1990s.
The name is based on Generation X, the generation that preceded
them (Business Dictionary, 2018, p 1).
Mom-and-Pops
store
A small, independent, usually family-owned, controlled, and
operated business that has a minimum amount of employees, has
only a small amount of business volume, and is typically not
franchised, therefore open for business only in a single location
(Business Dictionary, 2018, p 1).
Multi-channel
retailing
Selling to customers through both traditional offline outlets — like
brick-and-mortar locations, mail order catalogs, and even direct-
toconsumer telemarketing — as well as online outlets — namely,
ecommerce sites, social media, email marketing, and marketplaces
(Smith, 2017, p 1).
Online Retailing The sale of goods and services through the internet. Also named as
Electronic retailing, or e-tailing, can include business-to-business
(B2B) and business-to-consumer (B2C) sales of products and
services, through subscriptions to website content, or through
advertising (Investopedia, 2018, p 1).
Online Subscription
Service
Subscription-based online services (SOS) refers to an e-business that
provides periodic delivery of a customized box of merchandise
directly to the consumer's home for a weekly/monthly subscription
fee (Woo & Ramkumar, 2018).
Omni channel
retailing
A fully-integrated approach to commerce that provides shoppers a
unified experience across online and offline channels (e.g.,
touchpoints) (Walker, 2018, p 1).
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Terms Definition
Perceived
Usefulness
The degree to which using online fashion subscription services will
provide benefits to apparel consumers (Venkatesh, et al., 2012).
Perceived Ease of
Shopping
The degree of ease associated with consumers’ use of online fashion
subscription services for apparel shopping (Venkatesh, et al., 2012).
Price Value Consumers’ cognitive tradeoff between the perceived benefits of the
fashion subscription services and the monetary cost for using them
(Venkatesh, et al., 2012).
Psychodynamic
Approach of
consumer behavior
study
According to this approach behavior is determined by biological
drives, rather than individual cognition, or environmental stimuli
(Bray, 2008).
Retailing A set of business activities that facilitate the exchange of goods and
services to the end consumer (American Marketing Association,
2018, p. 1).
Social Influence The extent to which consumers perceive that important others (e.g.,
family and friends) believe they should use online fashion
subscription service (Venkatesh, et al., 2012).
Subscription Service Subscription-based services refers to business that provides a
customized box of merchandise to its subscriber directly to the
consumer's home for a weekly/monthly or customers’ need base
instance which has been specifically chosen for them by their
personal stylist (Woo & Ramkumar, 2018).
Subscription
Business Model
(SBM)
The subscription business model is a business model in which a
customer must pay a recurring price at regular intervals for access to
a product or service (Bischof et al., 2019).
16
.
Chapter 2.
Literature Review
This chapter considers retailing in its various formats throughout history. A global view
of apparel consumers' purchase behavior, online apparel shopping, and the development of
online subscriptions services are discussed. A theoretical structure for investigating online
shopping subscription services and hypothesis development is reviewed.
2.1. A Brief History of Global Retailing and Apparel Retailing Formats
The history of today's retailing has its roots in ancient civilizations (Lamoreaux, Raff, &
Temin, 2003). Historically, the basic marketplace was the place where merchants and skilled
craftsmen sold their products to local individuals. Although basic in scope, ancient
marketplaces contributed significantly to the local economy (Kent & Omar, 2003). A common
belief is that modern retailing concepts originated during the industrial revolution, however,
Hudson (2014) argued that primitive retailing practices have a much longer history. In their
book "Readings in the history of American Marketing: Settlement to Civil War" Shapiro and
Doody (1968) provided insight into early retailing in America, from the 1600s to the 1860.
Based on historical records from the 1600s many cities including Philadelphia, Newport,
Charleston, New York, and
Boston started to attract people due to their established market centers (Shapiro & Doody,
1968).
As the industrial revolution facilitated an increase in the size of trade and markets,
skilled craft workers and those involved in trading, merchandising, and retailing began to band
together. The continuous development of transportation and communications enhanced the
overall merchandising and retailing systems. The convenience of transportation and shipping
17
along with communicational technology's advancement supported the merchandising businesses
and decreased their costs of keeping the inventories and stocks in stores, moreover, such modern
facilities altered the traditional layouts of stores as well (Monod, 1996).
As the global retailing formats have undergone revolutionary stages so has apparel and
fashion retailing. Throughout the various phases of retailing, apparel and fashion items have
been sold in specialty apparel stores, grocery stores, department stores, independent stores as
well as luxury stores and sports stores. Apparel and fashion retailers also use virtual retailing
platforms such as mail order retailing, catalog retailing, home TV shopping and telephone
retailing formats to sell their items (McCormick et al., 2014). As technology advances retailers
have adopted new technologies adapting them to the apparel and fashion market.
As time passed the technological revolution became a crucial factor in the expansion of
retailing, offering improved ways to manage inventory and logistics. Internet and
computerbased applications became popular among the leading retailing chains, as well as local
and international retailing chains and in supermarkets of developing countries for transferring
and imitating the relevant knowledge (Reardon, Timmer, Barrett & Berdegue, 2003). Computer
innovation continues to transform the business practices of retailers. Technology has resulted in
a new configuration of the supply chain, store's operation management, and widened the
methods of interacting with end-users. It has resulted in the establishment of a multi-channel
retailing environment, where consumers are given the highest priority by each and every
stakeholder (Kumar, 1997; McCormick et al., 2014; Smyyth, 2011).
18
.
2.2. Online Retailing: the Common Platform of Global Virtual Retailing
During the 1970s-80s, the launch of computer-based technologies and the internet were
available to only a small number of people (Senn, 2000). The major users of internet facilities
were governmental agencies, scientists and academic researchers. Computer technology
companies soon acknowledged the importance and necessity of tech-based facilities on the
commercial level and started to develop hardware and software to cater the need of the mass
population, and in this way swift expansion of e-commerce took place (Delen, Sharda, &
Hardgrave, 2011). The rapid growth of technological advancement gradually attracted investors
during the 1990s (Senn, 2000). In time, the term "e-business" emerged and became associated
with several retailing and technically adept businesses organizations. These businesses began to
develop their websites and internet portals to approach their target customers and offered
products online (Rayport & Sviokla, 1994).
Through the 1990s, the efficacy of the internet was broadly believed, because of its
flexibility of usage in terms of space and time, as well as its ability to reach the world (Benjamin
& Wigand, 1995). Regardless of the internet's potential, a segment of the industry was not
convinced of the pragmatic applications. At that time, the limitation of workforce skills with
newly developed technologies and lack of expertise in the area made the technology unviable for
many retailers (Doherty, Ellis-Chadwick, & Hart, 1999; Doherty & Ellis-Chadwick, 2010).
Further, some researchers suggested, internet facilities and their implementation in online
retailing should be limited to only tech-savvy organizations, capable of tackling the digital
intricacies (de Figueiredo, 2000; Forsyhte & Shi, 2003). Such limitations were suggested at
least until stability and maturity of market and internet technology was achieved. On the other
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hand, experts in the retailing industry recommended that a certain set of commodities had the
potential for expansion and competitive advantage if managed through online business
implications (de Figueiredo, 2000). In this regard, experts listed a few products including
concert and travel ticketing, computers, music-related products, and books, as products with
potential for retailing profits, if managed through a digital strategy. However, at that time it was
a common thought that apparel and other similar products did not have enough profit margins to
be displayed online for selling (de Figueiredo, 2000). Contrary to that belief, within a short
period of time, online shopping had transformed itself from a fad and become the fastest
expanding area of internet convention (Forsyhte & Shi, 2003).
In a short span of twenty years, the internet-based online marketplace has established
itself the most used retailing facility (Grewal, Motyka, & Levy, 2018). Its ability to directly
target customers, offering consumers the ability to shop through numerous channels including
the amalgamation of online presence, brick and mortar stores, call centers, and business to
consumer (B2C) sites has been attributed for its exponential growth (Rangaswamy & Van
Bruggen, 2005). The basic concept of incorporating multi-channel retailing is known as the
smartest strategy in today's' world of retailing business (Doherty & Ellis-Chadwick, 2010).
Weltevreden and Boschma (2008) observed that street-retailing business attained an additional
foothold through an online presence and have enhanced the overall connectivity and interaction
of retailers and customers, which in turn improved the promotional activities and accelerated
business.
Furthermore, it was suggested that leading retail businesses that developed good quality
websites could achieve competitive advantages through establishing an online platform
(Weltevreden & Boschma, 2008). The advent of internet-based technologies boosted the
20
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consumers' purchasing power by mitigating the asymmetrical information, transparent market
promotion strategies, and through clusters of consumers that in turn allowed end-users to prove
their power and have an impact on the prices and products altogether (Rezabakhsh, Bornemann,
Hansen, & Schrader, 2006).
2.3. E-Retailing Internationally
Both developed and developing countries acknowledged that the online market space
was an enormous and influential marketplace for international retailers (Macchion et al., 2017).
In this regard, China comes on the top of the list of the countries that benefitted the most by the
establishment of the online market. Between the time period 2011 to 2016, Chinese businesses
realized a profit of $271 billion through internet retailing (Dixit & Sinha, 2016). China has the
largest population in the world, and most of its population is engaged in online shopping. The
ratio of online shoppers is showing persistent growth as internet access is reaching to the rural
areas (Dixit & Sinha, 2016). Other factors such as developing infrastructures and increasing
living standards are assisting in establishing an extensive online customer-base throughout
China (Dixit & Sinha, 2016).
However, despite the opportunities for China, the United States is still moving ahead as
the forerunner of online retailing business. Countries in Europe although engaged in online
purchases had not reached the use of either the U.S. or China by 2016 (Dixit & Sinha, 2016).
On the other hand, Asia is demonstrating tendencies to surpass the total online sales of North
America. As time advances, internet shopping has become more accepted by consumers.
Additionally, the emergence of smartphones, Android and ioS applications has played a vital
role in proliferating online shopping (Dixit & Sinha, 2016). Developing countries such as Saudi
Arabia, Brazil, South Africa, and Russia are adopting more online-based practices, which is
21
evidently a by-product of augmented customer confidence, financial implications, increasing
internet accessibility, and well-developed logistical and transportation infrastructures (Dixit &
Sinha, 2016).
Traditional retailers are encountering a great deal of competition due to the continuous
expansion of online retailing practices (Wang, Minor, & Wei, 2011). In the first decade of the
21st century using the internet as a business tool was an option, but the contemporary business
strategies implies the incorporation of an internet and online presence is an inevitable device in
the game of aggressive competition (Taylor & Strutton, 2010).
During the initial stage of e-commerce, internet marketing and Business to Consumer
(B2C) retailing was in its development stage, therefore several retailers felt reluctant to
incorporate e-retailing for apparel and fashion products (McCormick et al., 2014). However, as
e-retailing grew, experts suggested apparel and fashion retailing stores should establish
numerous ways to expand their business and to promote the offered products (McCormick et al.,
2014). In recent years e-commerce virtual stores have taken the place of brick and mortar
outlets. Up to the 3rd quarter of 2018, statistics showed total sales of $131 billion acquired by
ecommerce activities throughout the United States. This constituted approximately 8.1 percent
of the total retail sales of the country (Statista, 2018c). Apparel and fashion are the largest
segment of e-commerce with estimated retailing sales of 63.8 billion dollars (Phadnis & Fine,
2017; Statista, 2018c).
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2.4. Subscription-Based Online Services (SOS): New Retailing Avenue
2.4.1. Brief History of Subscription-based Services
The subscription-based shopping model is not a novel concept. Subscription-based
delivery services were widely practiced throughout the 20th century. Early forms of
subscription services for products and services included the regular receipt of newspapers, fresh
edible and dairy products, as well as fashion catalogs. There products were subscribed to for a
designated timeframe. However, the implications of the online facility were not present in the
traditional subscription business model (Woo & Ramkumar, 2018). Take the instance of a
conventional mail order subscription service practiced by Columbia Record Club in the 1950s
(Woo & Ramkumar, 2018). The Columbia Record Club was formed in 1955 by Columbia
Records as an experiment to market music directly by mail. New members to the club were
enticed with a free record just for joining. This service hired more than a hundred thousand
individuals in order to make their subscribed consumers stay updated with the trendy pop music
(Woo & Ramkumar, 2018). Another type of subscription service which is still in practice today,
was used by cable and newspaper agencies allowing their customers to enjoy an uninterrupted
supply of morning paper and TV channels through monthly, weekly or annual subscriptions
(MSA, 2018).
This traditional practice of subscription has been transformed into the modern practice of
subscription service through which it is possible to indulge in live streaming music, or video
online. Apple Music, Spotify and Netflix are examples of such subscription services (Lovely,
2018; Statista, 2018d). By following the facilities and tendencies of the modern era, several
other industries are looking forward to establishing their subscription offers. For instance, some
23
contemporary food outlets are allowing their subscribed customers to benefit from an experts'
selected recipe and the ingredients be delivered every week to their consumer's home.
HelloFresh is an eminent name in this regard as its subscription service delivers recipes and
ingredients to its consumers throughout the Netherlands. This strategy benefitted HelloFresh
with increased profits for 2015, with quadrupled revenues from the previous year (Woo &
Ramkumar, 2018).
Moreover, a similar trend was followed by Birchbox, a cosmetics company that allows
the consumers to have a personalized delivery box comprised of several samples of everyday
beauty products (Birchbox, 2018). Birchbox, a monthly online subscription service, sends four
to five selected makeup samples or other related products to its subscribers. Products include
skin care products, fragrances, organic products and various other cosmetics (Birchbox, 2018).
It is headquartered in New York City, with operations in the United Kingdom, France, Spain,
and Belgium (Birchbox, 2018). Birchbox has been recognized as a pioneer of modern
subscription based online services (SOS) (Woo & Ramkumar, 2018). Birchbox is credited for
initiating an innovative creative business model and this approach named the "Birchbox effect,"
is recommended as an online retailing practice. It is believed that the Birchbox effect is efficient
at gratifying consumers as well as beauty brands simultaneously (Woo & Ramkumar, 2018).
According to the SOS of Birchbox, online retailers acquired a workable strategy to
provide something advantageous to its consumers to stay loyal, while they select different
beauty brands to promote their products through the subscription device (Woo & Ramkumar,
2018). Modern techniques have added additional excitement by including the hedonic aspect of
the online shopping experience. Woo and Ramkumar (2018) affirmed that through the
implication of this hedonic approach, retailers select random products on the suggestions of
24
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different experts and stylists so when customers receive the package there will be a pleasant
surprise waiting for them. This creative business model allowed Birchbox to gain 45,000
subscribers during the first year of business operations (Woo & Ramkumar, 2018). In just a
two-and-a-half-year time period from 2010-2013 Birchbox gained more than 800,000
subscribers, a 1,777% increase in business. Woo and Ramkumar (2018) noted that due to the
success of the Birchbox effect, more than 2,000 imitative online subscription-based business
sprouted, out of nowhere.
Woo and Ramkumar (2018) reported, there are several product categories online which
offer SOS services; such commodities include fair trade crafts as well as survival kits. By
observing the popularity of SOS services provided by online businesses, several brick-andmortar
outlets have taken initiative and entered the waters of SOS. In 2013, Target stores attempted to
issue a subscription beauty package. Target promoted the subscription service through online
channels and social media platforms. The flawless implementation and amusingly rewarding
outcomes of the subscription strategy tempted other retailers to follow the lead and
consequently, Sephora (beauty) as well as Allure Fashion (magazine) adopted the modernized
form of subscription services and launched their cosmetics and magazine and other accessories-
based subscription boxes to attract and retain potential customers and to maximize their profits.
Wal-Mart launched a pilot version in the form of a snack-box service named Goodies Co. All
these aspects of SOS enthusiasm among retailing businesses, in turn, became the stepping stone
for the commencement of the little-box retailing era (Woo & Ramkumar, 2018).
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2.4.2. Overview of Today’s Online Subscription Service
The global market for subscriptions continues to grow significantly, but at a slower pace
than in the past. The market has grown by 890% since 2014 (Kestenbaum, 2018). In Figure 2.1
it is noticeable that the sector experienced tremendous growth from 2014 to 2016, and has
maintained steady growth from 2016 to 2018.
However, the popularity of product category is shifting. The former number one market
leader in the subscription categories for food and beauty products had fewer site visits in the last
year than in the previous year. But in clothing the leaders are established: Stitch Fix runs away,
and other leaders have established themselves and continue to grow (Kestenbaum, 2018).
Figure 2.1. Monthly visits of U.S. subscription box industry (Source: Hitwise, 2018, p. 2)
26
.
Figure 2.2. Top visited subscription sites (Source: Hitwise, 2018, p. 4)
Figure 2.3. Product category wise subscription box sites visit/visitors
(Source: Kestenbaum, 2018, figure#5)
27
As can be seen in Figure 2.3, beauty products and food make up over 60% of the
subscription customers, closely followed by clothing. These categories are likely to change over
time (Kestenbaum, 2018). From Figures 2.2 and 2.3 it is evident that Ipsy, a beauty subscription
service, leads the board followed by Blue Apron and Hello Fresh both of which are subscription
service providers in the food industry. However, apparel subscription provider such as Stich
Fix, FabFitFun are getting more attention.
There are currently 18.5 million U.S. subscription box customers. In terms of
demographics, they are: younger millennia, college degrees, and college or hipster city dwellers,
female, household revenues over $100,000, regular contributors to online reviews, online regular
readers, and more likely to buy online than usual on-site, and generally an Amazon® shopper
(Kestenbaum, 2018).
The research team from McKinsey interviewed 5,093 American subscription service
receivers. In their survey it was found that 4,057 online shoppers used a subscription service
and spent at least $25 online in the past month. (Chen et al., 2018; Columbus 2018). Curated
subscriber services were 55% of all subscriptions making this category the most dominant in the
subscription economy in 2018. The dominance of curation-based subscriptions reflects the
demand of online customers for an ongoing series of personalized, high quality experiences
(Chen et al., 2018; Columbus 2018).
However, it can be observed from Figure 2.4 that women and men are inclined to use
different types of service providers. In both cases the Dollar Shave Club and Amazon®
Subscribe and Save are leading the list. For women they are followed by apparel subscription
services whereas for men they are following by food subscription services.
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Figure 2.4. Gender wise subscribers’ preference for subscription service
(Source: Chen, et al., 2018, figure#4)
2.4.3. Stitch Fix- The Story of the Most Successful Apparel Subscription Service
Stitch Fix, a personal shopping and subscription service in the United States, is a public
limited entity that was established in 2011 (Stitch Fix, 2018). The personal styling services of
Stitch Fix are designed to dispatch custom opted apparel and relevant accessories to its
customers, for a one-time styling charge. In order to follow the process, consumers must fill out
an online questionnaire regarding basic information of their preferences and penchant of style
(Cheng, 2018). Subsequently, the specifically recruited stylists of Stitch Fix selects five styling
and apparel items to send the individual in accordance with their preferred style. In order to
have extended information regarding the taste and lifestyles of the customer, Stitch Fix also
utilizes access to their social media accounts, such as Facebook and Pinterest (see Figure 2.5).
29
Figure 2.5. Data Science helps match the style and brand to customer
(Source: Stitch Fix, October, 2018, p.5)
The term "fix" can be explained as a scheduled date of package delivery, which is
determined by the customer. Moreover, the customers can either indulge their innovative style
or send the package back to the company within a period of three days. In due course, if a
customer is satisfied with his or her received styling package, the initial styling charges are
applied (Cheng, 2018; Stitch Fix, 2018).
Furthermore, if customers keep all five items in the package, they will earn a 25%
discount on the actual cost of the items (Stich Fix, 2018). For the convenience of the customer,
Stitch Fix has several shipping frequency options, such as once every two weeks, once a month,
or every two months. In order to observe the efficacy of their style-based services, Stitch Fix
affiliated with Pinterest and encourages their consumers to post their styled photos, so stylists of
Stitch Fix can observe the look (Cheng, 2018; Stich Fix, 2018).
30
.
To add value to their subscription services, Stitch Fix provides its subscribers with a
prepaid USPS envelope, so they can return the unapproved items. The shipping is free for any
type of exchange as well. The unique factor about the business model of Stitch Fix is not
merely the stylists' incorporation, but also the ease of opting into a budget, preferences, and
lifestyle. Fifty-five dollars are the average price per apparel item within the Men’s and Women’s
lines which feature a wide variety of different price ranges.
On the other hand, all the children’s' styling items start at $10 per item. The underlying
reason for the price range and other pricing strategies is to establish the customers' loyalty by
offering them a comfortable way of determining and paying charges. In turn, highly trained
personal stylists endeavor to dispatch the best suitable apparel and accessories to the customers
within their prescribed range and criteria. Cheng (2018) asserts that today's era of technological
advancement and globalization is laden with the subscription service providers, and all of them
are confronting competitive challenges. However, Stitch Fix has secured a rank among the top
ten online subscription retailers, and its profits and online traffic continues to grow. According
to a report of RBC Capital Markets business analyst, Stitch Fix has substantially revolutionized
the fashion industry (Cheng, 2018).
There are several features which enhance the efficacy of Stitch Fix's subscription service
and make it a priority for the subscription customers; data utilization being one of them.
According to Stitch Fix (2018) algorithms and data science are important aspects when
delivering personalized and individualized fashion subscription packages to customers (See
Figure 2.6).
31
Figure 2.6. Different data source for subscription service providers
(Source: Stitch Fix, 2018, p.6)
Stitch Fix started as a private company, but in 2017, it became a public limited company
by filing an S1 (Stitch Fix, 2018). In the filing, Stitch Fix explained that it had a data scientist
team that consisted of 75 Ph.D. degree holders. Throughout the filing, Stitch Fix addressed the
terms algorithms and data science more than 75 times, which underlines the significance of such
an implication with reference to the business’s success. Stitch Fix credits its data science and
functionality, which allow users to disclose their private body fitting and clothing preference
data to the business without hesitation (see Figure 2.7).
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.
Figure 2.7. Predictive algorithm helps stylist successfully serve clients
(Source: Stitch Fix, 2018, p.7)
Stitch Fix tracks customer purchases and returns and alters selected items according to
customer need (see Figure 2.8). This information is used to compare the efficiency of their
parttime stylists. More than 34,000 fashion designers and stylists have been recruited and
trained to deal with the customers to provide a more personalized experience. The personal
notes included with every subscribed package sprinkle a sense of humanity to the online and
virtual service
(Stitch Fix, 2018).
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Figure 2.8. Example of Clients’ portfolio management in Stitch Fix
(Source: Stitch Fix, 2018, p.22)
The unique and smartly devised amalgamation of personalized attributes, data handling,
algorithms, data science, and stylists proved to be a winning move for Stitch Fix. Angus (2018)
reported that Stitch Fix is a fashion, apparel, and shoe retailer that has established a considerable
market share in the e-commerce marketplace. Initial sales of $32 million in 2012, has grown to
approximately $1.2 billion in 2018 (Cheng, 2018, Stitch Fix, 2018). This is a similar rate of
growth which was once experienced by the #1 online business giant Amazon®. It exceeded the
expansion rate of both Macy's and Nordstrom (Cheng, 2018). As can be seen from Figure 2.9,
in
2018 Stich Fix topped the apparel subscription site visitors.
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Figure 2.9. Top apparel box sites (Source: Kestenbaum, 2018, figure#4)
2.4.4. Different Forms of Online Apparel Subscription Service
Although the original concept of a subscription service was to deliver services to its
customers at regular intervals for a specific fee, in the context of apparel subscription services,
these days the nature of subscription services has changed. To eliminate the fixed cost of a
membership fee, companies like Stich Fix are offering the opportunity to credit the subscription
fee amount towards the price of the items consumers receive and keep. Dia & Co., Trunk Club,
Rocksbox have followed the same model. On the other hand, Le Tote and Rent the Runway are
subscription services that offer designer clothing rentals for a varied membership fee (Cautero,
2018). They also offer rental opportunities for maternity clothes and/or other necessary items
for a specific subscription fee. In another form of service, a customer can subscribe and provide
their feedback with the service providers’ platform and they can order items whenever they are
needed (Bischof et al., 2019). In this way, service providers are offering customers the freedom
35
of not paying any monthly membership fee and availing the curated services on an as needed
basis (Bischof et al., 2019). MM.LaFleur, Dia & Co., ModaBox, Bombfell, and Trendy Butler
are a few names that are offering this type of flexible subscription service to their customers.
Even though, apparel subscription service companies vary in their business models and
product offerings, they do share some common characteristics such as, offering a curated
service, they have a Box service, they provide free return and shipping facilities and all these
companies cater clothing items.
2.4.5. The Values for Consumers of Subscribing to a Fashion Subscription Services
Subscription services were popular even before the advent of online businesses, as
people in past decades used to subscribe to different regular commodities such as newspapers,
dairy products, and cable channels (Tao & Xu, 2018). However, modern day advancements
make the subscription process more convenient and alluring for a myriad of customers who opt
for online shopping. Signing up for a subscription service adds value for customers as they get
products in a timely manner (Bischof et al., 2019; Tao & Xu, 2018). Moreover, the retailing
subscription services as Birchbox and Stitch Fix allow their customers to avoid the hassle of
roaming around in shopping malls, to find the latest trends or budget-friendly products
(Birchbox, 2018; Bischof et al., 2019; Cheng, 2018). Likewise, automatic shipments provide
customers a recurring package, and therefore they do not have to worry about purchasing items
for themselves on a monthly basis, from brick-and-mortar stores (Woisetschläger, Lentz, &
Evanschitzky, 2011).
Additionally, one of the biggest advantages of subscription services is that the members
know the exact amount the retailer is charging them for delivery. This practice establishes
36
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confidence for consumers, as they feel in control of their priorities and know their monthly
financial obligations (Tao & Xu, 2018). Members of online subscription services may also
obtain discounts and decreased charges, because retailers which extend subscription-based
services become expert in managing their stock, and know precisely the number of their online
subscribers (Bischof et al., 2019; Tao & Xu, 2018).
Furthermore, it has been suggested that subscription services strengthen the relationship
between consumers and retailers. The perpetual receiving and dispatching of packages makes
repeat and loyal customers, which in turn maximizes the profitability for the business (Bischof et
al., 2019; Woisetschläger et al., 2011). This relationship and interdependency allows retailers to
better understand the unique preferences and requirements of their customers through explicit
information (Tao & Xu, 2018). All these factors are interrelated and boost the efficacy of
subscription services, improve the lifestyles of consumers, and add value to the personalized
shopping (Bischof et al., 2019; Tao & Xu, 2018; Woo & Ramkumar, 2018).
2.4.6. The Feature of Subscription-Based Online Apparel Service
The subscription business model features a myriad of characteristics. According to
Kavadias, Ladas and Loch, (2016), curation and convenience are two basic elements which are
interwoven in the tapestry of the business model of successful subscription retailers. The two
advantages of subscription services are:
•Convenience: Convenience is an integral element of customers' services because the
recent era of technological advancement makes it easier for consumers to shop online
with expedience without concerning the hassle of crowded marketplaces.
37
•Curated Service: In online apparel subscription service, personalized stylist offer
customizable high level service to achieve individual customer satisfaction.
Along with convenience and curated service, subscription-based business models
underline the vitality of mass customization as well as personalization. Based on personalized
preferences and suitability, retailers can design a customized subscription box of a variety of
products along with a dash of personalization. These personalized services help retailers to
achieve loyal customers.
Loyalty is the most fundamental dimension of any subscription-based business model,
and because of loyalty, it is believed that current subscribers are looking forward to renewing
their subscription for the same services again. Zott, Amit and Donlevy, (2000) stated that
loyalty works as a building block in creating business value. Through repeat business, it
becomes easier for retailers to understand uniquely specific habits and preferences of the
subscriber that further assists in designing an exclusive package (Woisetschläger, et al., 2011).
This in turn, increases the revenues and profitability of the subscription-based retailer. The
SBM emphasizes establishing a loyal customer base, which in turn eliminates stock managing
costs (Woisetschläger, et al., 2011).
Another factor which seems rewarding by establishing a loyal customer base is the size
of orders, which keeps increasing with time. A research study regarding selling patterns of
apparel stores observed that after a loyal relationship of two to three years, subscribers become
more comfortable with retailers and their services, and consequently placed more subscription
orders (Reichheld & Schefter, 2000). Experts refer to loyalty as a well-developed and well-
defined conceptualization that encompasses consumers commencement to becoming a regular
38
.
and retaining subscriber (Dick & Basu, 1994; Kim & Niehm, 2009; Lee & Moon, 2015; Li &
Green, 2011).
2.4.7. Personalization, Mass Customization and Curation
Data acquisition, processing, and collection are crucial functions for an online business.
Through such data collection retailers gain insight into customer's preferences and their likes
and dislikes. Subscription-based business models bundle products creating a box full of
surprises for their subscriber. Such boxes can be defined as a miniature character profile.
According to Zeithaml, Lemon and Rust (2001), personalization is a practice which
shifts the expectancies under the influence of the online marketplace. The subject proposition
describes that technological advancement plays the role of a game changer, and affects the
consumer's expectations. It is also stated that for personalization, mutual filtering is not
sufficient, it requires being personal in a real sense. In due course, retailers as well as
manufacturers are expected to address diverse combinations of consumer's preferences and
unique needs.
Personalization has a myriad of definitions and its conceptualization features a multifacet
structure. Postma and Brokke (2002) stated that "personalization is a fragmented format of
communication that transmits different messages to different people of a group, on the basis of
their personal traits." In summation, personalization is an authentic mode of bridging the
communication with subscribers in a more profound personal manner, and therefore it is
recommended to subscription-based retailers to incorporate the element of personalization in
their business approach, and trigger their relation with the consumer through the same approach.
39
Moreover, personalization adds vibrancy to the overall market mix. Mass marketing has
been known as an effective device to target a large cluster of prospective buyers since the 1950s.
Goldsmith and Flynn (2004) stated that marketing improved and became increasingly
independently pivoted propositions to approach the customers. Therefore, retailers and
subscription-based businesses need to mull over the causation of personalization, such as it is
practiced with the 4Ps of place, promotion, price, and product (Li & Green, 2011).
Lee and Moon (2015) asserted that the underlying concept of personalization can be
affixed to the overall subscription process from ordering the package, to the creating of the
package, delivery, and post-service factors. This personalization is indispensable for the
involved personnel throughout the delivery of the subscribed package, product, and overall
procedurals through which a consumer attains and utilizes the ordered commodity.
Furthermore, the idea of personalization can be extended into the notion of customization of
fabricated products. In turn, consumers purchasing these products not only form a relationship
with the retailer or subscriber but, the purchasing experience becomes highly personalized.
Customization is evident throughout the production procedures of the automobile industry where
individuals thrive to modify the appearance of their cars. Likewise, Nike offers its consumers
customized sneakers in different colors, sizes and designs and the customer may even have their
name on their shoes (Lee & Moon, 2015).
Convenience
Convenience is integral to the idea of online shopping and subscription. Convenience
has several dimensions and comes in numerous formats. A product itself can be convenient,
take the instance of Betty Crocker's pancake mix that allows an individual to make instant
40
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pancakes without any hassle (Betty Crocker, 2018). Similarly, the place or distributional
arrangement can be convenient through which a customer can decide the mode of shopping
either at a nearby shopping area or placing an order online (Laurell & Sandström, 2016). It is
convenience that makes the online shopping a widely practiced, accepted, approved, and
appreciated mode of shopping (Horrigan, 2008; Jiang, Yang & Jun, 2013). It is easy to shop
online as the user does not need to be concerned with time, their schedule or appearance, and
there are no traffic issues because a simple website interface allows the shopping to occur
(Horrigan, 2008; Jiang, Yang & Jun, 2013). Moreover, control over registration, delivery time
and charges, price and a wide variety of options enhance the sense of controlling and
interdependency in a consumer (Farquhar & Rowley, 2009).
The recent trend and technological advancements have made convenience an imperative
component for online customers, which is further transformed in the practice of subscriptions
(Mahatanankoon, Wen, & Lim, 2005). Convenience in one's life is crucial and it is evident
consumers are willing to pay aggregated charges in exchange for premium services and product
convenience (Candel, 2001). The incorporation of a convenience-based ideology has generated
online businesses as well as food home delivery outlets. Even the Uber is a by-product of
adding convenience to one's life (Laurell & Sandström, 2016).
The convenience-based concept is multi-dimensional, yet no discussion or research is
available concerning a single or coherent definition of its meaning (Farquhar & Rowley, 2009).
Additionally, it is probable that a consumer never exploits all the dimensions of convenience
because they are interconnected and reliant on the convenience's category (Fenech & O’Cass,
2001). A list of dimensions of convenience in the context of SBMs is briefly stated below:
41
•Decision Conveniences: A contemporary styled online subscription business allows
subscribers to enjoy the thrill of having a subscription box selected by stylists on
behalf of consumers. This practice, in turn, eliminates the burden of decision making
from consumers, and therefore they found it alluring and interesting.
•Time: Online shopping either subscription-based or not, saves time. Subscribers may
place their orders, and receive the desired products on their doorstep, at relatively
reasonable prices in a short period. In this way, they do not have to plan time to
roam around the aisles of shopping stores. Online shopping and subscription
services are time efficient and convenient.
•Transaction Conveniences: Online shopping offers a wide variety of payment
methods. Automated transaction options make the payment method convenient both
for retailers and subscribers. In this way, subscribers never have to feel anxious
about counting change or calculating the dollar bills from their wallets.
2.5. Consumer Behavior and Consumer Behavior Study Approach
2.5.1. Consumer Behavior
Consumer behavior can be defined as "the study of the processes involved when
individual or groups select, purchase, use or dispose of products, services, ideas or experiences
to satisfy needs and desires" (Solomon et al., 2006, p 6). Consumer behavior research can be
elaborated as a scientific evaluation, through which, the behaviors a consumer demonstrates,
their selecting, securing, utilizing, and disposing of products and services, which gratify their
unique needs. Organizations can equip themselves only after comprehending the extent and
42
.
implication of consumers' attitude towards a specific product or service (Rucker, Galinsky &
Dubois, 2012; Stafford, Turan & Raisinghani, 2004).
According to researchers, understanding consumer behavior is the ultimate key to attain
success in a marketplace (Fullerton, 2013; Rucker et al., 2012). Studying consumer behavior
can be beneficial for numerous areas of expertise, including psychology, cultural, and societal
psychology, as well as genetic anthropology (Rucker et al., 2012). The origin of consumer's
decision-making research is almost 300 years old (Fullerton, 2013; Köksalan, Wallenius &
Zionts, 2013).
Fashion and apparel industries' marketing is merely based on the preferences of
consumers. According to Rath, Bay, Gill and Petrizzi (2014), fashion marketing is all about
planning and controlling, creating, distributing, communicating, producing, promoting, and
commercializing, in order to make consumers satisfied. In order to buy a product or service,
consumers go through an interconnected series of observations and actions. This
decisionmaking process of consumers is an area of interest for researchers (Fullerton, 2013).
The basic need of expanding the research scope on the subject matter was utterly
economic and pivoted the idea of purchase mechanism evaluation (Loudon & Albert, 1989).
Utility Theory is one of the prevalent models in this regard, the theory highlighted the
propositions about consumers' buying choices, and expected yields of their buying decisions
(Zinkhan & Braunsberger, 2004). In the illustration of such propositions, it was believed that
consumers are rationale buyers, with the only concern of self-interest when making purchase
decisions (Zinkhan & Braunsberger, 2004).
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2.5.2. Factors of Consumer Buying Behavior
Literature and published research reflect a myriad of factors that affect consumer buying
behaviors. These factors include the corporate and cultural values of an organization, perception
of quality and quantity of received and processed information, cultural, societal and
environmental aspects, as well as social communication elements (Batt & Dean, 2000; Essoo &
Dibb, 2004; Petrovici & Ritson, 2006; Rani, 2014). Kotler (2003) explained that psychological,
personal and socio-cultural as well as rational and psychological causations are the dominant
factors in impacting and shaping a consumer perception of purchasing. These factors are briefly
discussed below:
•Psychological Factors: interconnected with physical safety and protection-based
commodities. Such factors are also impacted by the idea of reliance, empathy or to
nurture the relationship with a seller as a friend (Hornik & Zakay, 1996). The most
significant point of psychological factors is the absence of price concerns, as
consumers make a purchase decision regardless of price discrepancies (McGuire,
1976).
•Socio-Cultural Factors: Highlight the influence of family, colleagues, societal,
communal and friends' circle. Through the impact and suggestions of these entities,
an individual attempts to recognize themselves and opt for appearances accordingly
(Samuel Craig & Douglas, 2006).
•Cultural Factors: People from different cultural backgrounds prefer to manage their
appearances differently from a clothing and accessories perspective which are
interwoven with their cultural perspective (Wan, Youn & Fang, 2001).
44
.
•Personal Factors: Several personal and individual factors including age, gender,
career, income standard, and lifestyles, shape the overall preference of a consumer,
and in turn prompt them to make buying decisions. Similarly, financial prosperity or
inferiority are personal matters as well and affect the decisions regarding lifestyles,
choices of textures, fabric, material, and brand to a great extent (Kassarjian, 1971).
It is observed that most buying behaviors are developed and altered through effective
advertising (Saleem & Abideen, 2011). Consumers evaluate and observe an advertisement
campaign, develop some personal connection or feeling toward the content of the commercial
and make buying decisions (Mulhern, 2009). Therefore, communication and marketing experts
suggest that an advertisements' effectiveness is entirely based on the depiction, presentation, and
accumulation of ideas, which in turn mitigate the hesitation of consumers towards making a
quick buying decision (Saleem & Abideen, 2011). The fashion and apparel industry employs
multiple channels for advertising including fashion magazines, catalogs, newspapers, fashion
shows, celebrity lifestyle coverage, internet, and sales promotions through posters and radio
etcetera (Mulhern, 2009).
2.5.3. Theoretical Approaches to the Study of Consumer Behavior
The impreciseness of studying consumers' behaviors further leads to several approaches,
which are widely adopted in evaluating the overall process of buyer's decision making. The
following are five major theoretical approaches of consumer behavior:
•Economic Man approach
•Cognitive Approach
•Humanistic approach
45
•Psychodynamic Approach
•Behaviorist approach
2.4.3.1. Economic Man
The primitive definition of "man" defines this creature as the most self-oriented and
rationale fellow on the planet (Bray, 2008). Therefore, man is subject to make decisions merely
on the potential of generating maximum outcome with minimum resources (Zeithaml, et al.,
2001). The approach implies that consumers need to be aware of all possible usages of a
product or service to accurately compare alternatives, in order to make the most rational and
optimum buying decision (Zeithaml, et al., 2001). However, recent trends assert that such
intensive procedurals are not effective, because the time limitations make it an unrealistic
account of making a buying decision. As a matter of fact, consumers seldom acquire the
required and complete knowledge of products or service' characteristics, and most of the time
they make their buying decisions based on values or social influences and recommendations
(Zeithaml, et al., 2001).
2.5.3.2. Cognitive Approach
According to the cognitive approach, observed behaviors are the foundation of the
intrapersonal cognitive process (Bray, 2008). Ribeaux and Poppleton (1978) stated that the
proposition is entirely opposite of traditional theory of behaviorism, and analyzes an individual
as a source of processing acquired information. The notion of intrapersonal causation evidently
poses a contrast to the external influences asserted by behavioral approaches. Nevertheless,
several environmental and external factors along with societal influences are rational in this
perspective (Plous, 1993; Wan, Lee, & Lee, 2007). The roots of cognitive approach are
46
.
interlinked from Socrates to Aristotle and Descartes. Further, Hebb (2005) developed the
stimulus-organism response, and the subject approach became a conventional theory in the field
of psychology, and incorporated in several métiers consequently. Afterward, the cognitive
approach became an integral part of behaviorism and a prominent model for researching
decision patterns (Furedy & Riley, 1987). In this regard, cognitive behavior modification
(CBM) defines the cognitive and arousing behaviors of consumers. CBM emphasizes the self-
incentives and observational experiences, instead of an explicit or implicit rewarding approach
(Meichenbaum,
1977). The underlying objective of the Cognitive behavior model is to either develop or
modifying the cognition of prospective consumers altering buying behaviors (Furedy & Riley,
1987). The cognitive approach offers a procedure, through which consumers discover different
behaviors by observing and evaluating the actions, responses, and consequences of other
individuals (Stewart, 1994). Therefore, advertisements and success stories are known as the best
devices in cognition, through which audiences recognize positive and desired outcomes.
Advertisements demonstrate different common people and celebrities using a product, and this
gesture encourages consumers to use the same product (Saleem & Abideen, 2011).
2.5.3.3. Humanistic Approach
Wan et al. (2007) stated that published content are laden with the efficacies of the
cognitive approach, because, according to the experts, this approach equips the researchers with
the decision-making process of consumers. Nevertheless, several academic authors underline
the limitations of cognitive approaches and believe that such approaches are not able to provide
insight into several interconnected matters of a consumers' buying behavior. Therefore,
researchers devised innovative approaches to address consumers' issues on a more individual
47
level instead of generically. Such approaches are known as humanistic approaches and highlight
the importance the behavior of an individual instead of an entire populace (Wan et al., 2007).
Previous propositions overlooked the essence of emotional facets, and only focused on
rationality. At the same time, the evolving theories of the humanistic approach dealt with the
examination of the conceptual framework of decisions and volition. In such studies, experts are
endeavoring to identify the gap between the buying intention of a consumer and the actual
purchasing decision. For this purpose, some concrete decision-making stages were devised. On
the other hand, a few studies are trying to enwrap the ideology of altruism and egoism in the
relation of consumer buying behaviors, however, to date, no correlation between philanthropy
and consumers' behavior is found (Wan et al., 2007).
2.5.3.4. Psychodynamic Approach
Sigmund Freud has a prominent position in researching classical psychodynamics (Freud
& Strachey, 1964; Wan et al., 2007). According to the psychodynamic theory, it is believed that
behaviors have intense influence on biological attributes, and such influences become instinctive
forces through which an organism thinks outside of conscious grounds (Cotte & Wood, 2004).
Freud and Strachey (1964) elaborated that three major dimensions of an organisms' psyche,
which are known respectively as Id, then Ego and the superlative form, Superego.
The psychodynamic theory of Freud underlines the significance of unconscious factors
which result in a purchase. Furthermore, Solomon (2006) highlights that a similar proposition
evaluates the reality principles, through which individuals affirm themselves regarding the
acceptability of the exterior world. Therefore, businesses explore the consumers' psyche to
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.
comprehend the subconscious motives, which motivate people to buy a product, brand or service
(Solomon et al., 2006). The psychodynamic approach is imperative in improving:
•Marketing tactics through an apt comprehension of psychological dimensions, which in
turn throw light on different behaviors such as how an individual contemplates reasons
or takes some actions.
•External environmental factors which have a substantial impact on an individual
•What attributes and features of a product or service can elevate the interest of the
consumer?
•Strategies which can be adopted by marketers to enhance the efficacy of the overall
marketing promotion. (Solomon et al., 2006)
2.5.3.5. Behaviorist Approach
"Little Albert" was groundbreaking research in the field of studying behaviors written by
John B. Watson, in 1920 (Beck, Levinson & Irons, 2009). The underlying posit of the
publication revolved around a leading character, a child named Albert. Albert was taught to
become fearful even of the most gentle of factors, because of their pairing with irritable and loud
noise. This study indicated that multiple behaviors can be shaped through the implication of
external influences and incidents (Bray, 2008).
According to the classical theory of behaviorism, it is stated that behaviors are explained
by external influences. The theory explains that all the actions and reactions of humans and
animals are the by-product of external events; such actions include all areas of attitudes,
including feelings, emotions, and thoughts (Bray, 2008). Watson, Pavlov, Skinner and Bandura
(1976) are eminent names in sifting through and refining of behaviorism, as Pavlov scrutinized
49
the aspects of the classical condition and Watson researched, experimented and rejected the
operant conditioning theory presented by B. F Skinner. All the developments of behaviorism
rely on rationality and the logic of positivism, and therefore such empirical approaches can be
utilized to study the underlying behavior of consumers' buying decisions (Bray, 2008).
2.6. Prior Research on Online Apparel Consumer Behavior
The clothing and textiles sector faced rigorous changes over its historic development era
(Hansen, 2004; Roach-Higgins & Eicher, 1992; Tortora & Eubank, 2010). Understanding
apparel consumer behavior with regards to rising online shopping is a big challenge. To better
understand consumers’ needs in this segment, researchers paid much attention over the past 20
years. This section summarizes relevant prior research and briefly describes their findings.
2.6.1. Research from 1999 to 2005
Internet shopping was still in its infancy during the late 1990’s and early 2000’s. The
original internet apparel shoppers were affluent, female, well educated, and under the age of 35
(Ebenkamp, 2000; Lee & Johnson, 2002). In a study carried out by Then and DeLong (1999),
university students represented the consumer sample. They suggested three major visual aspects
for successful online clothing shopping websites: images of the online product, which closely
reflect end use, displays of similar items, and views from different points of view, such as front
and back. Survey data showed that participants who purchased apparel online wanted website
that can realistically display products from a variety of perspectives. Participants of the research
study suggested that their purchasing decisions depended on the security of personal
information, convenience of shopping mode as well as the return policy. Additionally, findings
revealed that consumers are more inclined to purchase apparel products online that has no fitting
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.
issues. However, Kunz (1997) said consumers appreciate the quality of their products, product
variability and customer service when it comes to online clothing purchases.
In 2002, a survey was completed by 263 men and 303 women who measured their
buying attitude, behavior, and predispositions to online and offline clothing (Goldsmith &
Goldsmith 2002). The results showed that, for overall enjoyment of shopping, online buyers
were no different from non-buyers because of their belief that they can buy clothing online at a
reasonable price. Age, gender and race demographic variables were related to online clothing
purchases. An additional analysis showed that online buyers were more likely to buy online
than non-buyers (Goldsmith & Goldsmith 2002).
Further, Goldsmith and Flynn (2004) argued that online retailing needs attention by
marketers to the safety aspect, the relationship between real-world and virtual buying. They
stressed, the technology and promotion aspects of online platform were well investigated
whereas the consumers need aspect wasn’t so much explored. Furthermore, the lack of
confidence in medium-sized online shopping among online non-buyer needed attention. They
noted, various activities such as information gathering, communication, games, etc. are the core
of the online presence of the consumer, however they mentioned marketers needed to be
responsible for securing the entire e-commerce experience in online retailing. Some suggestive
measures such as product and service guarantees have been noted: payment on delivery rather
than order payments, with low risk items return policies, real consumer product endorsements,
testimonials and awards, free gifts and other order incentives (Goldsmith & Flynn, 2004).
In Ha and Stoel’s (2004) study, clothing products were classified as high-risk items and
clothing was associated with high perceptual risk due to the sensory and interactive nature of a
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purchasing process. In clothing shopping, consumers want to examine the products physically
to evaluate their color, size, design and fabric. Fitting was also very important for clothing
products (Ha & Stoel, 2004). Basic clothing products are successful online (Goldsmith &
Goldsmith, 2002). Because the touch and feel of basic clothing is very familiar and comparable
across brands, the buyer is less hesitant to buy (Goldsmith & Goldsmith, 2002). It was noted
more trendy items could be more risky for online purchase (Hammond & Kohler, 2000).
Ha and Stoel (2004) reported consumers with higher income, higher education, higher
risk propensities and a higher occupational level tended to shop online. An innovative person
may therefore use the Internet for clothing shopping, even if the buying of clothing on the
Internet was high risk (Ha & Stoel, 2004). Watchravesringkan and Shim (2003), on the other
hand, found that online clothing purchase intentions have been predicted by safe transaction
attitudes (e.g. Payment security, privacy of consumer information, return policy, minimum
return costs and product purchase warranties) and transaction process.
In Joines, Scherer and Scheufele (2003) the Fishbein model was used to examine the
intention of consumers to shop for clothes online. The study included mainly men (70%) aged
35-55, who used to be internet users nationwide in the U.S. They revealed that getting access to
various brands were the key criteria for shopping online. In addition to that high-quality
products with good money value were found to be desired features for shopping online. It was
also noted that individuals with positive attitude to shop online had higher behavioral intention
to buy apparel using online platform. The attitudinal components and subjective norms were
identified as significant predictor of the behavior (Joines et al., 2003).
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.
Yoh, Damhorst, Sapp and Laczniak (2003) explored the greater intention of Internet
purchasers who had a more positive attitude to Internet clothing. The buyers who buy clothing
in physical stores were confident that they could buy clothing over the Internet. They reported,
Theory of Reasoned Action explains the buying intentions for the shopping of clothes on the
Internet. The Theory of Diffusion of Innovation also justified consumer explanations of the
decision-making process for the adoption of internet shopping. As per their findings, the
touchfeel aspect will contribute to consumer interest and orientation for internet clothing
purchases, if added through visual experience. They recommended the marketers should also
take into consideration social implications via interpersonal channels and word-of-mouth
strategy (Yoh et al., 2003).
Johnson, Lennon, Jasper, Damhorst and Lakner (2003) showed interest to small
community consumers. They applied Rogers’ innovation decision-making model to examine
factors that facilitated the use of the internet by small community consumers to buy food and
fashion goods. Their study found that online food and clothing consumers in smaller
communities ranked internet-shopping as more profitable, less compatible, trial-based, more
observable (visible) and less risky than others (Johnson et al., 2003).
Young Kim and Kim (2004) have investigated fashion consumers’ online shopping
characteristics. Around three-hundreds adults who had home computers and Internet access in
the U.S. were surveyed. A mailing survey was conducted. The attributes perceived for online
shopping were cost of transaction, promotional activities of the retailers, design and interactivity
of the shopping platform. It was reported that consumers’ demographic attributes such as along
with above mentioned variables, individuals’ gender identity, family earnings, number of family
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members determined the behavioral intention of shopping fashion items online (Young Kim &
Kim, 2004).
The purpose of Goldsmith and Flynn’s (2004) study was to explore selected
demographic and psychological features that lead consumers to buy clothing online. The survey
examined 805 customers, who described their on-line purchases of clothing as well as how
innovative they were with respect to internet purchases and how much they were purchasing
clothes via catalogues. Although all those variables are related positively to the purchase of
online clothes, multiple regression analyses showed that online clothing purchasing was most
influenced by an adventurous online buyer and heavy catalog buyer. These findings meant that
online clothing purchases are more motivated by an internet innovation aspect rather than
clothing innovation. The study's three major implications were: First, a shopper with a history
of catalog shopping was more prominent in the context of online clothing. Secondly, the role of
innovation was important. Online clothing shopping will increase with the overall growth of
online shopping.
Thirdly, shopping in this category cannot provide additional shopping, irrespective of whether
shopping for clothing online. In clarifying web purchasing attraction, demographic variables
(age, gender and payment system) had a minor role (Goldsmith & Flynn, 2004).
Using the Theory of Reasoned Actions, Xu and Paulins, (2005) studied consumers’
attitude and behavioral intention to shop online for clothing items. It was found that individuals
who had a positive attitude towards online shopping demonstrated a behavioral intention to shop
for clothing items online. Results from the study indicated that prior internet use, professional
status, and whether an individual had a personal vehicle or not, significantly affected the
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.
attitudes of online clothing shopping (Xu & Paulins, 2005). Further, the effects of brand
knowledge, the amount of product information displayed on the website and prior experience in
shopping online for clothing on perceived risk and intention of purchase were discussed by Park
and Stoel (2005). Brand familiarity, perceived risk, previous experience was found to be
significant predictor of online clothing purchase intention whereas information did not play any
significant role (Park & Stoel, 2005).
2.6.2. Research from 2006 to 2015
During the period 2006-2015 internet shopping exploded and research surrounding
apparel shopping matured. Kim and Forsythe (2007) described technology adoption. They
reported, virtual try on when shopping for clothing online was becoming popular. The results
showed that virtual try on was not necessary for on-line shoppers to enjoy entertainment and
fun. They further indicated, virtual try on was not gender oriented. In virtual technology,
anxiety and innovation play an important role (Kim & Forsythe, 2007).
Won Jeong, Fiore, Niehm and Lorenz (2009) reviewed the concept of the virtual buying
environment for experienced customers. They examined a 4 E scale prescribed in the study (i.e.
entertainment, experience, education, excitement), and further tested if website features affected
these scale factors. These four have affected the emotional fun and excitement of consumers as
well as the pleasure and excitement of the outcome. Pleasure and excitement results led to an
increased inclination to the shopping website. The study added further meaningful aspects of
the popularly known SOR (Stimulus, Organism, Response) phenomenon to the extent that
indicated organisms affected emotional variables and subsequent response variables. In the
study, educational experience was found to be merely an influencing factor thus did not affect
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emotion by the product presentation. Among 4Es experience, the pleasure and excitement had
the strongest influence and the researchers suggested to take these aspects into special
consideration for website design (Won Jeong et al., 2009).
With reference to online clothing shopping, Kwon and Noh (2010) examined the
relationship between perceived risks and benefits and purchase intention for mature consumers.
Also, the impact on the online shopping phenomenon of demographic factors such as age and
different experiential factors were investigated. They reported the diversity of product offering
and price value plays an important role for online apparel retailers. In addition, they reported
online shoppers' experiences and maturity influence them to utilize different online shopping
benefits (Kwon & Noh, 2010).
Jones and Kim (2010) examined the impact of the online shopping intention of young
U.S. customers on retail brand confidence and off-line patronage, clothing involvement, and on
website quality. Data from 200 young women consumers who completed an online survey were
collected for the study. Participants were asked to choose one of three pre-determined retail
clothing brands with which they had experience. Using multiple regression analysis they tested
the formulated hypotheses. In their study, online shopping intention was greatly influenced by
retail brand confidence, offline experiences, clothing qualities and two website quality factors
(usability and information quality, visual appeal and image). The most powerful predictor of
online shopping was the off-line patronage process (Jones & Kim, 2010).
Jacobs and De Klerk, (2010) explore the role played in adopting the Internet in the
purchase of clothing from a social-cognitive perspective for female consumers ' clothing needs.
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.
Rogers’ Diffusion-Innovation Model (Rogers, 1995) was employed along with Social Cognitive
Theory (Bandura, 2001). There were 24 semi-structured interviews with professionals living in
a major city in South Africa. The participants expressed their view that certain steps, actions
and procedures such as textile touch, feel and try the items before deciding to buy them were
valued. Participants showed that they had an important role to play in whether to adopt an
online platform or not.
Cho and Workman (2011) looked at the influences of gender identity, fashion
innovativeness and leadership orientation, and individuals’ need for touch aspects in decision
making in shopping online for clothes. A convenient sample was used to conduct this survey.
The results revealed that multi-channel choice of participants was influenced only by fashion
innovativeness and opinion leadership orientation. The preference of the need for touch was
influenced by the need of multi-channel choice that local and non-local stores provided.
Participants who needed to touch clothing preferred local stores. Regardless of gender, those
who use multiple channel preferred TV home shopping, catalogs order shopping and online
platforms (Cho & Workman, 2011).
Ha and Stoel (2012) looked at the dimensions of e-shopping quality, how the quality of
eshopping influenced consumers' shopping (e-shopping satisfaction and e-shopping intent) and
tested the moderating impacts on e-shopping quality of consumer experience within the context
of online shopping. Of these 4 quality factors identified for e-shopping (Privacy/Security,
Website Content and Functionality, Customer Service and Shopping Experience), the content
and functionality of the website as well as shopping experience quality had a considerable
impact on e-shopping satisfaction, which contributed to e-shopping intentions. In addition, this
57
study supported the moderating role played by experiential e-shopping motives in the quality of
eshopping experience (Ha & Stoel, 2012).
Park and Cho (2012) analyzed whether social network communities affected information
and clothing shopping behavior. They mentioned there were important views for e-shoppers
who use the community of social networks as a platform to shop online. They reviewed online
communities like Facebook, fashion blogs, and consumers’ comments on a retailers’ website.
They reported, for companies to achieve their objectives they should concentrate on different
ways of communication with the members of the community. The following were also
suggested: Text, Chat, E-mails, and Bulletin Boards. They concluded effective communication
means that the community member not only delivers good results, but also brand identity and a
sense of membership. The positive attitude to the brand increased site visits (Park & Cho,
2012).
Aghekyan-Simonian, Forsythe, Kwon and Chattaraman (2012) analyzed and compared
the impact of specific types of perceived risk and online clothing purchasing intentions of two
key risk reducers for online clothing shopping: product images and online store images. The
results showed that the product brand image affects the intentions of online purchasing by
reducing different perceptions of risk both directly and indirectly. They found online store
image had an indirect impact in decreasing risk perceptions on purchasing intentions
(AghekyanSimonian et al., 2012).
Orzan, Iconaru, Popescu, Orzan and Macovei (2013) proposed and tested an online
clothing model to explain and forecast the behavior of online consumers. It was reported that
perceived benefits and perceived risks were the main indicators of consumer attitudes towards
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.
buying clothing online. The perceived risks of online purchasing had a significant direct
negative impact on the attitude of consumers to purchase clothing online. However, perceived
benefits showed an even greater influence on the attitude of the consumer to buy clothing online.
Consumers ' attitude towards online clothing provides the best indication of their intention to use
the Internet in the online purchase process. Further, it was found E-word-of-mouth had an
important direct impact on the intention of the consumer to buy clothing online. It was
concluded that consumers’ behavior towards the purchase of clothing online affected consumers
more strongly when it came to their behavioral intention (Orzan et al., 2013).
The results of Blázquez’s (2014) quantitative survey carried out with 439 consumers
suggested that the shopping experience must be redefined and technology used as a means of
generating interchangeable and integrated experiences across channels. It was suggested that
retailers should think holistically on every channel and increase interactive and new Internet
technologies and use all points of contact with consumers, including mobile and social networks
to build a strong relationship. It was mentioned in the article that due to the difficulty of
transposing the experience of the shop to the online environment, it was one of the principal
reasons why the fashion industry was slower to adopt electronic commerce than other sectors.
However, the article discussed how new technologies recently enabled consumers to assess
fashion online, creating an interactive and exciting purchasing experience (Blázquez, 2014).
Kang and Johnson (2015) examined, the impact of users ' personalities, psychographic
characteristics, and gratification on online social shopping in Facebook commerce and whether
the strength of links and experiences related to gratification and intent. The consumer panel
consisted of Facebook users in the U.S. with online shopping experience (n=601). It was found
that market mavens and social browsers were likely to use online social shopping for
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socialization. Users who are social browsers were likely to use social shopping online to search
for information as well as to make a purchase (Kang & Johnson, 2015).
2.6.3. Research from 2016 to 2018
A study conducted in 2016 by Tsai, Chang and Tsai included 524 people. They
examined the history of customer satisfaction and the intent to repurchase online clothing brands
with a view to expectation-confirmation theory. An enlarged model was developed to explain
repurchase intentions and the concept of corporate social responsibility was taken into account.
The results showed that offline features (quality of services and quality of products) had a
significant impact on satisfaction, while online characteristics (quality of information and
quality of system) had a limited impact. Satisfaction and social corporate responsibility both
had a major impact on the intention to repurchase (Tsai, Chang, & Tsai 2016).
Generation Y’s website satisfaction was the focus of a 2016 research study of San Lim,
Heng, Ng and Cheah. This study identified four key factors that influence web-based consumer
satisfaction: usability, credibility, service quality, and transaction costs. The results showed that
when consumers buy using the website, they are satisfied with usability, credibility and service
quality. The researchers developed a model to explain omni-channel shopping behavior based
on the variables used in the UTAUT2 model and two additional factors, namely: personal
innovation and security (San Lim et al., 2016).
Juaneda-Ayensa, Mosquera and Sierra Murillo (2016) used a sample of 628 Spanish Zara
customers. They tested the model of shopping intention for consumers who had used at least
two channels during their latest shopping experience. Results showed that, in an omni-channel
context, the most significant determinants of the purchasing intention were: personal innovation,
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.
expected effort, and expected results. However, they failed to analyze the moderating role of
age, gender, prior knowledge and also did not consider the perceived product risk aspect. They
did not examine the hedonic motivation, facilitating condition, price value and habit constructs
of the UTAUT2 theory (Juaneda-Ayensa et al., 2016).
Based on the Technology Acceptance Model, Li, Chen and Liu, (2017) examined the
consumer's perception of satisfaction, the usefulness of online shopping and their intention of
participating in co-design. The study participants were 203 undergraduate students. The
consumer satisfaction was influenced by individual preferences, the appreciated price range on
the website, prior experience and usefulness of the online shopping platform. Further it was
reported that online co-design and re-buying intentions were positively affected by consumer
satisfaction. The usefulness of online shopping affected the intention of buying back
significantly but did not relate to the intention of co-designing online (Li et al., 2017).
Molinillo, Gómez-Ortiz, Pérez-Aranda and Navarro-García (2017) had developed a
model with regard to the history of the loyalty to online shopping based upon a multitude of
histories, including: affective and cognitive experiential conditions, hedonistic values of
purchasing, satisfaction, trust and the perceived value of service. The results showed that the
first positive effect on the degree of satisfaction was affective and cognitive experience, and the
positive effects on confidence also apply in affective experiences. It was also shown that both
hedonic and utilitarian shopping values can increase consumers ' satisfaction with online clothes
retailers. An indirect relationship between satisfaction and loyalty was established by trust and
perceived value. However, no moderating effects of age, gender, previous experience and
perceived risk were included, which are considered important aspects of the clothing purchase
process (Molinillo et al., 2017).
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Escobar-Rodríguez and Bonsón-Fernández, (2017) reviewed the determinants of online
fashion retailing and offered a model of online buying intention. A total of 433 people were
surveyed for this purpose. The results showed that the main influences of online fashion buying
were found to be value, confidence and innovation, while time saving and security aspects were
found to be the main predictive factors of perceived value and confidence (Escobar-Rodríguez &
Bonsón-Fernández, 2017).
Loureiro, Cavallero and Miranda (2018) estimated two alternative styles for the word-
ofmouth drivers, the fashion leadership and performance expectancy for fashion retailers’
websites platform. The researchers provided tablets to consumers to reply to the online
questionnaire. More than 300 participants were included in the final sample. Both the fashion
leadership orientation and performance expectancy of a website was identified as significant
predictor in this regard (Loureiro et al., 2018).
2.6.4. Prior Apparel Subscription Service Related Research
Although apparel subscription services are relatively new business models they have
received some attention in the academic literature. For instance, Tao and Xu (2018) researched
the perceptions and attitudes of young consumers towards a subscription retail service in the
fashion industry. A total of 10 students (9 females and 1 male) were recruited for the study from
a U.S. Southeastern University. None of the participants had experience with their clothing
needs using subscription service. The participants reported different levels of knowledge
regarding the retailing of fashion subscription services (Tao & Xu, 2018).
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.
In another study, Woo and Ramkumar (2018) investigated 385 U.S. fashion consumers
regarding subscription-based online services. They explored the predictors of online
subscription service use for fashion and beauty products using five consumer variables such as:
‘age’, ‘gender’, ‘e-tailer’s trust, ‘fashion consciousness’, and ‘exploratory product acquisition’.
Results indicated that individual gender identity, trust on the electronic platform, and fashion
consciousness are significant predictor for the use of fashion subscription services (Woo &
Ramkumar, 2018).
However, both of these studies were not theoretically underpinned, rather fragmented in
nature and the participants of both studies were mostly non-users of such services leading to
nonrepresentative sampling. Further the fashion and beauty subscription services cover a range
of products such as, apparel, make-up box, accessories etc. None of the studies defined
appropriately specific segments of consumers they were investigating, thus, the findings are less
generalizable. For instance, in terms of ‘e-tailer trust’, ‘fashion consciousness, and ‘exploratory
product acquisition’ customer variables, individuals may have different levels of perception for
each of these variables for a make-up box subscription service versus for clothing subscription
service (Tao & Xu, 2018; Woo & Ramkumar, 2018).
As has been discussed, it is evident that online apparel consumer behavior studies have
received plenty of attention, whereas being relatively new in nature, consumers’ online apparel
subscription service-related behavior has not. Very few published studies to date have
investigated online apparel subscription services, and those studies faced serious limitations.
Therefore, a study of online subscription services with appropriate theoretical underpinning is
warranted.
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2.7. The Present Study
As discussed, retailing of apparel and fashion products have gone through enormous
changes. New retailing formats are evolving and online apparel subscription services are one of
the latest developments. Additionally, almost all the professional researchers and marketers
agree that human behavior is intricate, challenging, and controversial (Eagly & Wood, 1999;
Plassmann, Venkatraman, Huettel, & Yoon, 2015; Zinkhan & Braunsberger, 2004). Regardless,
consumer behavior is considered vital for the affluence of modern marketing approaches
(Lemon & Verhoef, 2016). Moreover, the perception of consumer behavior has been widely
accepted as an effective and efficient tool to generate new sales tactics and provide a framework
for strategic planning. All the studies, as well as the conceptual framework of economic man,
affirm that the consumer's foremost concern is their self-interest prior to making up their minds
for any purchase (Kennedy & Laczniak, 2016). Therefore, the element of consumer behavior
has the capacity to be comprehended through intensive and proper research and evaluation; such
conducts make consumer behavior knowable to a great extent (Ajzen & Fishbein, 1980).
Further, as discussed above, there exists abundant literature on online apparel consumer
behavior, however there is little published research exploring apparel subscription services and
consumer behavior. A study investigating consumer needs for online apparel subscription
services may provide valuable insight for both industry and academia.
It is important for both researchers and online vendors to understand online customers
have double identities as both shoppers and technology users (Wen, Prybutok, & Xu, 2011).
Thus, in order to better understand online apparel consumers’ needs and wants it is necessary to
investigate the phenomenon from both the apparel need perspective as well as the technology
acceptance perspective. A review of literature from 2001 to 2014 identified 72 research articles
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investigating online shopping behavior. Many of these studies used a self-developed model,
modified from different theoretical frameworks. The Technology Acceptance Model (TAM)
developed by Davis (1989) was the most used theory used as a basis for modification. Despite
the fact that Technology Acceptance Model (TAM) has been used in many prior study to
understand online consumer behavior, it failed to capture important aspects of consumer
behavior that are specific to online shopping context and it has become outdated (Ahmed &
Sathish, 2015; Ingham, Cadieux, & Berrada, 2015). A variety of new theories have evolved to
better explain online shopping, but none of them fully address the online shopper’s behavior
(Ingham, et al., 2015; Zhou, Dai & Zhang, 2007).
Moreover, brands are paying more attention to improve customer relationship by
incorporating innovative approach like high quality website design, engaging with their
customers through social media and blogs (Farquhar & Rowley, 2009). In order to improve
consumer experiences, brands are developing interactive electronic platforms that can offer
seamless experience (e.g. Algharabat, Alalwan, Rana, & Dwivedi, 2017; Jung & Seock, 2017;
Toufaily, Ricard, & Perrien, 2013) and credibility (e.g., Park, Lee, & Ahn, 2004; Sparks &
Browning, 2011). The use of websites, social media and other digital technologies is becoming
increasingly active for fashion brands (Dhaoui, 2014).
To date no published studies in the apparel and fashion discipline could be found that
used the extended Unified Theory of Acceptance and Use of technology (UTAUT) to investigate
online apparel consumer behavior. The UTAUT was developed by Venkatesh, Morris, Davis
and Davis (2003) to fully explain an individual’s acceptance of technology at the individual and
organizational level. It has been cited more than 19,500 times to date (Google Scholar, 2018).
65
The UTAUT is a combination of eight social and behavioral science theories. In 2012,
Venkatesh et al. extended their theory and named it the UTAUT2 (Venkatesh et al., 2003, 2012).
UTAUT2 was intended to describe the consumer-level of technology acceptance phenomenon,
whereas the UTAUT covered organizational level acceptance of technology by employees. As
of 2018 it has been cited more than 2,600 times (Google Scholar, 2018). Ahmed and Sathish,
(2015) suggested that as the UTAUT2 was explicitly instrumented for the investigation of
consumer driven behavior it should incorporate other important factors for future research
purposes. As UTAUT2 addresses the phenomena in the consumer context it is the most
appropriate theory to use as an underpinning for a study of determinants of online apparel
consumers’ subscription based service.
Thus, the objectives of this study were to investigate antecedents of online apparel
subscription services, and to better understand consumers’ attitudes towards online subscription
service items in the context of extended Unified Theory of Technology Acceptance and Usage
(UTAUT2). This study was designed to uncover: Factors that predict whether a consumer will
use online apparel subscription services, the relationship between age, gender, and shopping
orientation and a consumer’s interest in subscribing to a fashion service. Understanding
consumers’ needs and wants during the entire buying process as well as analyzing antecedents
that consumers consider prior to purchasing apparel using an online subscription service will
provide valuable insights as well as help determine quality services that will help develop loyal
consumers.
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2.8. The Theoretical Framework and Hypotheses Development of the Present Study
To better understand any phenomenon or process, a systematic investigation with
appropriate integration of theories is needed rather than scattered and fragmented approaches
(Estabrooks, Thompson, Lovely, & Hofmeyer, 2006). According to Pedersen (2007), “Theory
is a set of statements that advances knowledge by describing, explaining, or predicting the
relationship between two or more concepts. A theory provides order and guides research” (p.
123). Theories provide maps for different kinds of terrain and develop testable and useful
interventions (Estabrooks et al., 2006). Through incorporation of theories, knowledge becomes
cumulative and capable enough to provide useful solutions for continuously changing issues
(Pedersen, 2007).
2.8.1. The Extended Unified Theory of Acceptance and Use of Technology (UTAUT2)
As mentioned above, online consumers possess dual identities, such as: shoppers and
technology users (Wen et al., 2011) and this is more accurate in online apparel subscription
service receivers’ cases, as they need to communicate and maintain the consumers relationship
through a technology platform. Thus, in order to better understand online apparel subscription
service consumers’ needs and wants it was decided to use UTAUT2 (Venkatesh et al., 2012) as
a theoretical underpinning for the present study. The phenomenon can be justified through
implicating the UTAUT2 (Venkatesh et al., 2012). UTAUT2 is an extension of the previously
proposed UTAUT, which was developed to explicate the causations of wide level acceptance of
Information and Communication Technology (ICT), particularly by consumers. UTAUT was
devised to address the characteristics of utilizing ICT by a workforce. In the field of ICT, the
conceptual framework of UTAUT has attained popularity and is known as a rational theoretical
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option (Williams, Rana, & Dwivedi, 2015). However, regardless of its origins, the model has
been applied to investigate consumers in numerous other fields. UTAUT has been used in the
context of studying human behaviors in areas such as communicational and information
technology, mobile banking, direction, and map-based services, online family legal consultation,
FAQs services, and internet banking (Zhou, 2011). Others include virtual learning programs,
egovernment portals, HRM e-recruiting, outsourcing services, rural tourism and travel ticketing,
and dispute resolution services (Laumer, Eckhardt, & Trunk, 2010). On the other hand, the
business applications of the UTAUT model have comprised basic computer and internet usage,
e-commerce applications, virtual training meetings, and learning facilities, HRM database,
organizational-based social networking (Curtis et al., 2010), management, accounting, and
health information systems as well as stock management, and inventory control (Chang, Hwang,
Hung, & Li, 2007).
UTAUT was developed through synchronization of eight theoretical models, which
elaborate the causation of the future prospect of technology use, along with an insight of past
and present usage, through an organizational chronological perspective. The eight theories are:
•Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1977)
•Technological Acceptance Model (TAM) (Davis, 1989)
•Motivational Model (MM) (Igbaria, Parasuraman, & Baroudi, 1996)
•Theory of Planned Behavior (TPB) (Ajzen, 1991)
•A combination of planned behavior and technology acceptance model (Taylor
& Todd, 1995b)
•Model of PC Utilization (MPCU) (Thompson, Higgins, & Howell, 1991)
•Innovation-Diffusion Theory (IDT) (Rogers, 1995)
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•Social Cognitive Theory (SCT) (Bandura, 2001)
In addition to the prior four constructs extended by the UTAUT model, UTAUT2
integrates three additional constructs that further explain modern relationships regarding the
acceptance level of ICT exploitation by the consumer. The subject approach rearranged and
redefined these seven constructs, which address the consumers' perspective, instead of
employees or an organization. UTAUT2 consists of these seven constructs, embedded within its
framework:
•Performance expectancy
•Effort expectancy
•Social influence
•Facilitating conditions
•Price value
•Hedonic motivation
•Habit
The main reason behind developing new constructs of intrinsic value and hedonic
motivation is to integrate UTAUT2 with the previous version of UTAUT, because the later one
addressed the matters relevant to utilitarian approach or motivational factors only, through the
implication of construct of performance expectancy. Several research studies of information and
communication technologies (ICT) elaborated on the impact of the hedonic motivation of the
actual and intending utilization of technology (Davis, Bagozzi, & Warshaw, 1992; Khare &
Rakesh, 2011; Opaloglu, 2012). The difference between actual and intended technological
usage is categorized as perceived enjoyment (Brown & Venkatesh, 2005). The findings
explored that hedonic motivation evokes the practice of technological acceptance specifically
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from the consumers' standpoint (Childers, Carr, Peck, & Carson, 2001). On the other hand, the
price value considers the monetary issue and its implication to technology through the
consumers' perspective. Technological usage within the premise of an organization is not a
money-based factor; however, an individual consumer must pay a fee or charges for utilizing
technology. For this purpose, the perceived expenditures of a commodity are estimated through
a comparative analysis between prices and product's perceived worth to the subject consumer
(Zeithaml, 1988).
The third newly developed construct is consumer habit, which asserts the habitual
implementation of technology usage in the consumers' context (Limayem, Hirt, & Cheung,
2007). Habits are a set of automated actions, practiced by consumers subconsciously (James,
1890). Prior experiences that facilitates habit are substantially relevant to the utilization of
technology by consumers (Venkatesh et al., 2012).
In summation, it becomes evident, that the model of UTAUT2 boosts the overall
proportion of explained variance regarding ICT usage intention, which is 18% as compared to
12% ICT actual usage. The subject study incorporates the measures of UTAUT2, in order to
examine the acceptance and use of apparel subscription services by consumers through
ecommerce web portals. The rationalization behind choosing UTAUT2 for the present study, is
that it considered online apparel subscription service adapters as technology users, as well as the
seven constructs of the model cover many facets of consumer behavior.
2.8.2. Theoretical Framework and Hypothesis Development
This study attempted to measure perceived usefulness, perceived ease of shopping, social
influence, curated service, hedonic motivation, price value and habit related factors of
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consumers’ online apparel subscription services. In the context of online apparel subscription
services, it was discussed earlier that convenience, curated service, and personalized service
influence a consumer to choose the service. When online apparel subscription services help
consumers save shopping time, get their taste of fashion without effort, get the service at a
reasonable price, consumers are expected to have a positive attitude toward apparel subscription
services. The conceptual framework of the present study is depicted as shown in the Figure
2.10.
Perceived Usefulness and Behavioral Intention to Use Online Apparel Subscription Service
In this study the construct ‘Perceived Usefulness’ refers to the degree to which using
online fashion subscription services will provide benefits to apparel consumers (Venkatesh et
al., 2012). In the original UTAUT, performance expectance was considered to be driven from
perceived usefulness (TAM/TAM2), relative advantage (IDT), extrinsic motivates (MM), job-fit
(MPCU), and outcome expectations (SCT). In a prior online consumer behavior related study,
Kim, and Forsythe, (2007) demonstrated that, perceived usefulness was directly related to
attitude formation to use virtual try on. Brown, Pope and Voges (2003) empirically
demonstrated that the greater the perceived relative advantage, the more likely services would be
adopted. Kim, Han and Lee (2014) found that perceived usefulness positively related to
perceived value creation. Usefulness was found to be an important determinant of attitude
towards online shopping behavior in many prior research studies (Childers, Carr, Peck, &
Carson, 2001; Chiu, Chang, Cheng, & Fang, 2009; Gillenson & Sherrell, 2002; Ha & Stoel,
2009; Lim, Osman, Salahuddin, Romle, & Abdullah, 2016; Lin, 2007; Sohn, 2017; Yen & Wu,
2016). Additionally, convenience, which is considered as important dimension of usefulness
was found to be a significant predictor of electronic shopping platforms (Childers et al., 2001).
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Gefen, Karahanna, and Straub (2003) have identified that consumers behavioral intentions to
transact with e-vendor depends on the perceived usefulness of the platform.
The quality of e-services leads consumers to shop online rather than to the quality of the
information given from the e-retailer (Hsu et al., 2012). The same claim was made in earlier
studies of Bart, Shankar, Sultan and Urban (2005) and Reichheld and Schefter, (2000). Further,
Hongfeng, Chunjing and Jie (2008), Kim and Lennon (2013) reiterated that the major factors
driving consumers to shop online are electronic customer services. It was mentioned that good
design and after-sales service are important elements that drive the intent of online shopping
(Hongfeng et al., 2008).
Pappas, Kourouthanassis, Giannakos and Chrissikopoulos (2014) found customization
was a major online shopping driver among Greek online shoppers. The functionality of the
website includes elements of usability and interactivity (Constantinides, 2004). It refers to the
ease of navigation, response, interaction and ease of access to the website (Al-Debei, 2013). As
part of shopping online, functionality of websites can be assessed examining browsing quality,
ordering procedure as well as the pace of performance of such activities and processes. All
these elements are related to the usefulness construct.
Further, the perceived usefulness was identified as substantial influential factor of online
purchasing intentions (Mohd Suki, Ramayah, & Mohd Suki, 2008). Kim, Jin Ma and Park
(2009), analyzed the intention to use mobile technology for shopping in terms of ease of use,
usefulness, enjoyment and the influence of surrounding peoples. It was found that both ease of
use and usefulness played important role in determining the intention to use the platform for
shopping. Additionally, in a study of 213 Hong Kong online shoppers, data indicated that
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perceived usefulness played more influences than perceived ease of use in forming a usage
intention (Kucukusta, Law, Besbes, & Legohérel, 2015).
Figure 2.10. Conceptual framework for online apparel consumers’ subscription service need
In their study Morosan and DeFranco (2016), revised the United Theory of Technology
Acceptance and Use (UTAUT) to build a comprehensive model that explained the intent of
using an online platform for the hotel industry. Based on data from a sample of 794 hotel
consumers selected from the general American population it was shown that ‘Performance
Expectancy’ was the highest predictor of intentions, with relatively low effects on hedonistic
motivations, habits and social influences. Likewise, Gupta, Dasgupta and Gupta (2008), Luarn
and Lin (2005), and Riquelme and Rios (2010), found the same in their studies and they
73
identified the perceived usefulness as a key factor. Lu, Yao and Yu (2005) used UTAUT to
investigate mobile information rather than mobile banking. It was concluded that ‘Performance
Expectancy’ influenced people significantly in mobile services adoption.
Kim et al. (2017) concluded that the perceived usefulness of the components in virtual
mirrors and socially interactive clothing was clearly important for the formation of behavioral
intentions. Based on a data set of 410 smartphone shoppers, empirical results showed that ease
of use and usefulness had a significant effect on mobile shopping 's intentional and behavioral
formation. Further, SivaKumar and Gunasekaran, (2017) mentioned that the perceived
usefulness had an important effect on the intention of consumers to buy online clothing.
Additionally, Li et al. (2017) found that online shopping usefulness had a significant impact on
the intention to repurchase clothing online. The same was found with Taiwanese consumers’
eshopping behavior (Moslehpour, Pham, Wong, & Bilgiçli, 2018).
In view of past research the following hypothesis was developed:
H1: Perceived usefulness will significantly affect an individual’s intention to use an
online apparel subscription service.
Perceived Ease of Shopping and Behavioral Intention to Use Online Apparel Subscription
Service
Perceived ease of shopping was conceptualized as the degree of ease associated with a
consumers’ perception of use of online fashion subscription services for apparel shopping
(Venkatesh, et al., 2012). In the UTAUT2 the ‘Effort Expectancy’ refers to the level of
technological ease of utilization (Venkatesh et al., 2003). Taking advantage of other
competitive models, Venkatesh et al. (2003) captured the perceived ease-of-use concept (TAM /
TAM2), complexity (MPCU) and ease of use (IDT) as the level of easiness associated with use
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of technology. Effort expectancy was conceptually tantamount to ease of use (Al-Qeisi, Dennis,
Alamans, & Jayawardhena, 2014).
In online apparel subscription services, ease of use can be associated with ease of
creating an online account, ease of communication with the fashion curator, access to the
member site and ease of navigating the subscription service providers’ site and related features.
Early adoption is expected to have significant expectations and later will be insignificant (Davis,
1989; Szajna, 1996; Venkatesh, 1999). ‘Effort Expectancy’ is expected to remain significant.
Therefore, it is anticipated that ease of shopping will have a significant and positive impact on
the attitude towards accepting online subscription services. In prior literature, ‘Perceived Ease of
Use’ affects both directly and indirectly the intention of consumers to shop online (Venkatesh,
Davis & Morris, 2007). Moreover, the ‘Ease of use’ affects the consumer's attitude and
intention in terms of online shopping (Panda & Swar, 2013; Teo, 2001). It was suggested in
prior literature that the design of the sites should be simple and information-oriented rather than
complex in design (Ranganathan & Ganapathy, 2002). Ease of use was found to be a significant
predictor of online shopping behavior in prior research studies (Childers et al., 2001; Lin, 2007)
as well as repurchase intention (Chiu et al., 2009; Yen & Wu, 2016). Additionally, prior
research findings revealed that perceived ease-of-use acted as an antecedent of online purchase
attitude (Gillenson & Sherrell, 2002; Van der Heijden, Verhagen, & Creemers, 2003).
Additionally, prior empirical studies of mobile banking service adoption (Gupta et al.,
2008; Luarn & Li, 2005; Riquelme & Rios 2010) reinforced perceived ease-of-use as a decisive
factor influencing individuals to use a mobile banking service. Based on UTAUT, Park et al.
(2007) and Luarn and Lin (2005) delved into three behavioral predictors namely performance
expectancy, effort expectancy and social influence, to explore the effects of an individual’s
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intent to accept mobile technology and data services. Both studies showed that the intention to
make use of mobile technologies or services was significantly influenced by these three factors.
In addition to that, perceived effort expectancy was found to be as an influencing factor in
determining the perceived usefulness of mobile payment services (Khalilzadeh, Ozturk, &
Bilgihan, 2017) as well as perceived ease of use was found to be positively related to the
intention to use mobile shopping (Natarajan, Balasubramanian, & Kasilingam, 2017).
In recent literature, Ma, Gam and Banning, (2017) showed perceived ease of use was
associated with consumers’ use of sustainability labels. Agrebi and Jallais’, (2015), study of
French purchasers and non-purchasers, showed perceived ease of use influenced satisfaction and
facilitated intention to use. Further, Hsu, Chang and Chen, (2012), investigated 310 consumers
who had experienced various types of mobile shopping services, both perceived usefulness and
perceived ease of use were significantly related to the flow of consumers buying process. Flow
functioned as a full mediator between perceived usefulness and attitude. Flow was significantly
related to attitude and purchase intention in the Zhou (2011) research where 287 responses were
collected through an online questionnaire survey in China. The perception of usefulness,
perception of usability, subjective norm, compatibility and past non-store shopping experiences
positively affect participants’ intention to use clothing in m-commerce.
As a result, guided by UTAUT2, this study hypothesizes:
H2: Perceived ease of shopping will significantly affect an individual’s intention to use
an online apparel subscription service.
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Social Influence and Behavioral Intention to Use Online Apparel Subscription Service
Social Influence refers to the extent to which consumers’ important others (e.g., family
and friends) influence their decision making (Venkatesh et al., 2012). Venkatesh et al. (2003)
used social influence to replicate the ‘subjective norm’ construct of TRA, TAM2, TPB / DTPB,
and C-TAM-TPB, social factors of MPCU and image of IDT. The degree to which a person
sees important others as having to use technologies was defined in the social influence.
Normative beliefs or social influence refer to “the perceived behavioral expectations of such
important referent individuals or groups as the person's spouse, family, friends, and—depending
on the population and behavior studied—teacher, doctor, supervisor, and coworkers” (Ajzen,
1991, p.
412). Normative belief may influence behavioral intent based on the theory of planned behavior
(Ajzen 1991) through "subjective norms" (e.g. perceived social pressure to engage or not to
engage in a behavior). Venkatesh et al. (2012) viewed social influence as a generic concept
encompassing the concepts of subjective norm, social factors thus defining it as ‘‘the degree to
which an individual perceives that important others believe he or she should use the new
system’’ (p. 451).
A major precedent of the online shopping intention is the corresponding influence
(Foucault & Scheufele, 2002; Limayem et al., 2007). Prior literature suggests recommendations
influence women more than men (Garbarino & Strabilevitz, 2004). The impact of friends has
been mixed. For example, the effect of family was much less than the influence of the media on
online shopping, and the influence of friends in Limayem et al. (2007). In recent studies
(Chevalier & Mayzlin, 2006; Cheung, Lee & Rabjohn, 2008; Ho & Chen, 2014; Kim et al.,
2014; Lian & Yen, 2014), social influence was the major driver for online shopping. Research
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also showed consumers’ behavioral patterns on this aspect of social influence changed based on
geographical context. Australians are high compared to China when it comes to external
influences as a determinant of consumers’ online shopping intention (Lee & Tan, 2003). Lee,
Khong and Hong (2014) indicated that a consumer can shop on the Internet with a positive
social information influence. Lian and Yen (2014) found that the expectations for performance
and social influence among Taiwanese old and young online shoppers were the major drivers for
online shopping.
Additionally, consumerism can be defined as a thorough consideration of underlying
causations, which lead to consumption activity. It is an amalgamation of a psycho-social notion
of juncture among the individual and structural area of utilization (Bray, 2008). In summation,
it is believed that, it is the act of consumption through which an individual connects with the
community.
Moreover, retailing industries’ fashion fiesta is the fundamental avenue to express the
height of consumerism (Bailey & Seock, 2010). Cho and Workman (2011) explained that
fashion, apparel, and other appearance-based accessories are the objects, which are associated
with the idea of belonging. Through sporting a particular type of dress or brand, people attempt
to experience a profound sense of differentiation from one another. It is explicitly
overshadowed by the social influence, which in turn motivates a consumer to demonstrate
individuality in their societal backdrop.
Logically, this information lead to the third hypothesis
H3: Social influence will significantly affect an individual’s intention to use an online
apparel subscription service.
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Curated Service and Behavioral Intention to Use Online Apparel Subscription Service
Curated Service was defined as a consumer’s perception of the stylists’ help available to
perform shopping using an online fashion subscription service (Venkatesh et al., 2012). This
construct has been incorporated within the conceptual framework of the present study to replace
the ‘Facilitating Condition’ construct. In the original UTAUT2 the facilitating condition was
incorporated by capturing the concepts of perceived behavioral control (TPB), facilitating
conditions (MPCU), and compatibility (IDT). Venkatesh et al. (2003) defined facilitating
conditions as the extent to which an individual believes that the technology use is supported by
an organizational and technical infrastructure. Venkatesh et al. (2003) in UTAUT incorporated
32 factors in eight models and further identified facilitating condition as a direct driver of
behavioral intention and actual use of technology.
Previous studies showed that the facilitating condition has a direct influence on
behavioral intention (Ajzen, 1991, Taylor & Todd, 1995a, 1995b). Other studies showed that
the facilitating condition is a major factor in technology adoption (Lian and Yen, 2014; Sareen
& Jain, 2014). The findings show that all UTAUT variables have a positive and significant
influence on individuals’ behavioral intention formation (Yu, 2012).
In the case of apparel subscription services, the construct ‘facilitating condition’ is more
likely ‘Curated Service’ related. In the subscription-based service, a personal stylist works
closely with subscribers, analyzing their preference and helping consumers pick ideal products
per their taste and preferences. Stylists evaluate consumer’s filled survey data, build the
subscription box to be sent to the customers door at certain intervals, review their feedback and
based on consumers’ review, they build the next box to be sent to customer. In this cyclic
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process, the stylist offers customized services to an individual and works towards providing
them products.
Subscribers receive periodic customized boxed items that are solely developed by the
personal stylist (Bischof et al., 2019). Gupta et al. (2018) mentioned that a subscription service
is more likely a self-gifting process where people can surprise themselves by curated products
that can exceed their expectations. Additionally, Tao and Xu (2018) reported that a tailored
service could offer clients the chance to try fresh styles that they were not conscious of.
Prior researcher identified that a quality curated service can influence individuals’
behavioral intentions to use such services (Sebald and Jacob, 2018). Cha, You, Gang and Park
(2018) revealed that the degree to which a curated service satisfies consumers will determine the
intention to use curated shopping. Gupta et al. (2018) reported that, if consumers are not
satisfied with a curated service they will be likely to abandon the use of such a service in the
future. In another study, Blaise, Halloran and Muchnick, (2018) found that the facilitating
condition helped form attitudes toward mobile shopping. Thus, it has been hypothesized that the
curated service of the stylist will affect consumers’ intention to use an apparel subscription
service. Therefore, hypothesis four was developed as:
H4: Curated service will significantly affect an individual’s intention of using an online
apparel subscription service.
Hedonic Motivation and Behavioral Intention to Use Online Apparel Subscription Service
In this study, the hedonic motivation referred as the fun or pleasure derived from using
an online fashion subscription services (Venkatesh et al., 2012). The motivation for shopping is
another well-established predictor that has been incorporated in many consumer studies. It was
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identified to have an impact on consumers’ online shopping intention (Babin et al., 1994;
Childers et al., 2001; Venkatesh et al., 2012).
Consumers shop differently in traditional retail stores depending on whether their
motivation is hedonic or utilitarian (Childers et al., 2001). Utilitarian (also known as
targetoriented shoppers) are concerned with the timely, effective purchase of goods in order to
reach their objectives with minimal irritation (Childers et al. 2001). Hedonic consumers (also
referred to as experimental shoppers) are those who are shopping for fun and entertainment
purposes (Childers et al. 2001). The utilitarian motivation was studied extensively in the online
shopping environment (Bhatnagar, Misra, & Rao, 2000; Bhatnagar & Ghose, 2004; Brengman,
Geuens, Weijters, Smith, & Swinyard, 2005; Kolsaker, Lee-Kelley, & Choy, 2004) and deemed
more important than hedonic motivation. This was also echoed by marketing research in the late
nineties, in which majority of online shoppers were represented by utilitarian shoppers
(Wolfinbarger & Gilly, 2001). There are several factors which explain such high numbers of
utilitarian shoppers (Johnson, Moe, Fader, Bellman, & Lohse, 2004). The focus on freedom of
shopping at any time and control over the shopping experience as well as the online shopping
attributes— convenience, accessibility, selection and information availability were reported in
earlier study (Wolfinbarger & Gilly, 2001).
Prior research identified perceived enjoyment as a positive influencing factor on online
shopping intention (Ramayah & Ignatius, 2005). Attitudes toward interactive online shopping
was influenced by a perceived enjoyment construct (Childers et al., 2001). Enjoyment acted as a
determining factor of repurchase intentions too (Chiu et al., 2009). Ha and Stoel (2009)
revealed shopping enjoyment as a significant predictor of individuals’ e-shopping adoption.
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Moreover, Kim and Forsythe (2007) revealed that the hedonic motivation influenced
positively toward using product virtualization technologies in electronic shopping platforms. In
addition to that, it was identified as a significant predictor in determining the intention to use
mobile shopping (Agrebi & Jallais, 2015; Natarajan et al., 2017). Prior literature has also
identified hedonic motivation as an influencing predictor of online shopping motivation
(Childers et al., 2001; Delafrooz et al., 2011; Escobar-Rodríguez & Carvajal-Trujillo, 2014; Lin
& Tang, 2005; Opaloglu, 2012).
Additionally, prior researchers reported hedonic motivation to be an influential factor in
technology acceptance as well as shopping channel choice. Davis et al. (1992) investigated the
degree to which an individual believes that using technical services is enjoyable. They found
this to be a decisive factor in determining technology performance. Khare and Rakesh (2011)
found the motivation for utilitarian purposes to be a major factor for Indian shoppers online.
Opaloglu, (2012) stated that hedonic motivation among Turkish online shoppers is the most
significant factor of their purchase behavior.
Hedonic shoppers (or experiential shoppers) shop online for information gathering
purposes such as hobby-type searches, participation, positive sociality and surprise, as well as
business hunting (Wolfinbarger & Gilly, 2001). Several theoretical and empirical investigations
were conducted in order to identify the intrinsic motivating role (Cheema, Rizwan, Jalal,
Durrani, & Sohail, 2013; Dennis, Morgan, Wright, & Jayawardhena, 2010; Dholakia &
Uusitalo,
2002; Huang & Oppewal, 2006; Khare & Rakesh, 2011; Kim et al., 2014; Lee, 2009; Lee et al.,
2014; Teo, 2001; Tong, 2010). The most powerful predictor of online shopping attitude was
found to be pleasure (Childers et al., 2001; Chiu, Lin, & Tang, 2005; Delafrooz, Paim, &
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Khatibi, 2011). If consumers enjoy their shopping experience, they are more likely to adopt the
Internet as a medium of shopping and to have a positive attitude towards online shopping. The
same motives can work for consumers’ online apparel subscription service needs.
Taking these aspects into consideration, it was hypothesized:
H5: Hedonic motivation is positively related to the use of an online apparel subscription
service.
Price Value and Behavioral Intention to Use Online Apparel Subscription Service
The construct price value was considered as the consumers’ cognitive tradeoff between
the perceived benefits of the fashion subscription services and the monetary cost for using them
(Venkatesh et al., 2012). The UTAUT2 model includes the influence of the “price value”
construct on the consumers’ intention to use technology (Venkatesh et al., 2012). This construct
makes a reference to its value or quality as regards money or the price of products or services
(Venkatesh et al., 2012; Zeithaml, 1988). It should be noted that Venkatesh et al. (2012)
included the price value construct in the UTAUT2 as the price value structure of a technology
that uses the cost of a currency to the consumer for providing its users with multiple economic
benefits.
Price was identified as a major online shopping driver in previous studies (Chiang &
Dholakia, 2003; Delafrooz et al., 2011; Liao & Cheung, 2001; Reibstein, 2002). Additionally,
price among Indian online shoppers had been identified as a high priority (Panda & Swar, 2013).
Price-conscious customers shop online more frequently than those who are not concerned with
price (Bigné, Hernández, Ruiz, & Andreu, 2010; Reibstein, 2002). However, Chiang and
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Dholakia (2003) found that price had no impact on the intention of online shopping.
Additionally, a previous study considered the variable of "saving prices" for an online shopping
platform, and it was reported consumers shop online to achieve lower prices (Khare, Khare, &
Singh, 2012).
Researchers revealed that individuals’ monetary concern, and value perception can
influence online shopping motivation and purchase intention (Chiang & Dholakia, 2003;
Childers et al., 2001; Delafrooz et al., 2011; Gupta & Kim, 2010; Irani & Hanzaee, 2011; Lee,
2005; Liao & Cheung, 2001; Reibstein, 2002). Additionally, it was identified that consumers
who seek good money value are more inclined to shop online than those who are not concerned
with seeking value (Bigné et al., 2010; Khare & Singh, 2012; Reibstein, 2002).
In addition to that, consumers perceived value seeking motivation acted as an important
predictor for repurchase intention (Fang, Wen, George, & Prybutok, 2016). A prior study found
that price saving motivation and convenience seeking were two significant determinants of
consumers’ intention to shop online (Overby & Lee, 2006). Further, Irani and Hanzaee (2011)
reported that in an apparel shopping context consumer’ price concern played as an important
determinant factor. Also, researchers suggested that retailers should develop pricing strategies
carefully so that they satisfy customers (Park, Kim, Funches, & Foxx, 2012).
Additionally, price was found to be an important indicator in tourism as well. Liu et al.
(2012) stated that the price saving motivation was the reason behind using online website
platform for booking hotel rooms. Furthermore, Babin et al. (1994) identified that booking
tourism related services using electronic platform allows consumers to get the service at a
certain price. Another study has shown that websites can facilitate consumer price comparisons
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(Alba et al., 1997). It was also reported that online tourism shopping reduces cost and time
(Wen, 2012). Similarly, price savings motivation and the possibility of capturing different
benefits in turn leads to greater intention to shop online (Wen, 2012). A study of 401 customer
experiences by Pappas et al. (2014) showed the greatest motivation for shopping online was
price and sensitivity to the promotional campaign.
Therefore it was hypothesized:
H6: The desire to get value for the money spent is positively related to the use of an
online apparel subscription service.
Habit and Behavioral Intention to Use Online Apparel Subscription Service
Here ‘habit’ is conceptualized as the extent to which individual tend to perform
behaviors automatically because of lifestyle (Venkatesh et al., 2012). James (1890) was the first
to point out the relevance of habit in the management of our daily lives. That was probably a
concept of habit.
It should be underscored that habit and behavior are not similar and these two terms
should not be interchanged with each other. Rather it should be understood as a special type of
subconscious thinking which improves perceptibility for habits and prevents the distraction of
individuals and the adoption of other, less effective ways of action (Verplanken & Aarts, 1999).
When a habit is found, conduct is automatically performed (Orbell, Blair, Sherlock, & Conner
2001; Triandis, 1980, p. 204). In other words, conscious performance needs little attention
(Wood, Quinn, & Kashy, 2002). Because of its negligible cognitive effort, habitual behavior
can be easily and efficiently performed (Lindbladh & Lyttkens, 2002). This means that a "habit
simplifies the movements required to achieve a given result, makes them more accurate and
diminishes fatigue" (James, 1890, p. 112).
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Habit also refers to “situational sequences of activity that happens automatically or have
automatically developed into behavior” (Triandis, 1980, p. 204). It is a tendency to behave in a
manner based on past experience (Gefen, 2003). It represents a behavioral willingness to repeat
an action, developed in a stable context through frequent performance (Ouellete & Wood, 1998).
The response to a situation stimulus can be viewed as an automatic behavioral response without
having to be preceded by an analysis process of cognition (Verplanken & Aarts, 1999). The
person is not necessarily aware nor needs to conduct thinking or rational assessments before
taking part (Ouellete & Wood, 1998). In other words, habitual behaviors, in which individuals
can preserve their limited mental resources, require a minimally conscious mind (Aarts &
Dijksterhuis, 2000). This automaticity allows for easy and rapid performance of usual behaviors
(Quinn & Wood, 2005). Within the online shopping process, the individual with online
shopping habits would automatically turn to an online channel rather than to a physical outlet
(i.e., an online behavioral response). In this study, it was expected that consumers’ who have a
habit of shopping online for apparel will more likely use an online apparel subscription service.
Thus, it was hypothesized:
H7: Individuals who have a habit of purchasing apparel online will have a positive
intention to use an online apparel subscription service.
Behavioral Intention
Behavioral intention was conceptualized as the degree to which a consumer has
formulated conscious plans to use or not use online fashion subscription services (Venkatesh et
al., 2012). Consistent with most psychological theories and models UTAUT has proposed and
demonstrated that the behavioral intention influences the usage behavior (Ajzen, 1991; Davis,
1989; Venkatesh et al., 2003; Venkatesh & Zhang 2010). Given that the aim of an apparel
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subscription service is to attract consumers with their service, and extensive research has
investigated the relationship between behavioral intention and actual use, it was hypothesized
that:
H8: Behavioral intention will significantly affect individual’s behavior of using an online
apparel subscription service.
Moderating Effect of Age
Consumers need to be studied from demographic and personality traits or features
perspectives to better understand what drives them to shop online. According to Chang, Cheung
and Lai (2005), consumers’ demographics play an important role in accepting online shopping.
Zhou et al. (2007) stated that the intention to shop online will have an influence on consumer
demographics. There have been many studies which have found that age, gender, education and
revenue have a substantial effect on consumer attitudes towards online shopping (Brown et al.,
2003; Delafrooz et al., 2011; Gong, Stump, & Maddox, 2013; Hernández, Jiménez, & Martin,
2011).
The barrier of consumer-age in online shopping is because older customers do not feel
comfortable with the new technologies of e-commerce (Steenkamp, Hofsede, & Wedel, 1999).
Young consumers under 25 have been more interested in using new technology (Wood, 2002).
Older shoppers have more fun shopping in physical stores than younger consumers (Dholakia &
Uusitalo, 2002; Lian & Yen, 2014).
Moreover, many studies have examined the effects of demographic variables on the
adoption of new technology. However, as compared to traditional studies on the dissemination
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of technological innovations, prior adopters were typically younger, more income-intensive,
better educated, and more socially professional (Rogers, 1995).
However, findings from past studies were not consistent. Some studies have shown that
the consumer age has a positive relationship with likelihood of on-line purchases (Stafford et al.,
2004), whereas others reported a negative relationship (Joines et al., 2003) or not (Li, Kuo, &
Rusell, 1999; Rohm & Swaminathan 2004). Such a discrepancy in research results may be
caused by different criteria in different studies for defining age groups. For example, the studies
mentioned above used 10 years in length (Li et al., 1999; Stafford et al., 2004), 15 years in
length
(Donthu & Garcia, 1999), twenty years in length (Rohm & Swaminathan, 2004).
Based upon the above conflicting results, this is a need to ascertain the moderating effect
of age in the case of consumers online apparel service acceptance and usage behavior. As a
result, this study posits:
H9: Age will have moderating effect on relationships among predictors of intention to
use an online apparel subscription service
Moderating Effect of Gender
Li et al., (1999) stated that women who like to shop online, buy more frequently than
men. Women usually take responsibility for household shopping and have a more positive
attitude to traditional shopping and catalog shopping than their male counterparts (Alreck &
Settle, 2002). However, the Internet's new shopping channel seems to lead to a different gender
pattern, if not an opposite. While online shoppers and non-shoppers had no substantial
difference in terms of gender orientation (Donthu & Garcia, 1999) increased purchases were
found in men (Stafford et al., 2004) and men spend more money online than women (Susskind,
2004). The attitude towards online shopping in relation to women was found to be more
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positive for men. Males are deemed to be more inclined than females to be utilitarian shoppers
(Shopping for purpose) while females are deemed more to be inclined than males as hedonic
(shopping for fun) (Al-Maghrabi, Dennis, Vaux Halliday, & Bin Ali, 2011).
A similar change in gender pattern was explained in several factors related to online
shopping by prior research (Bae & Lee, 2011; Citrin, Stem, Spangenberg, & Clark, 2003; Davis,
Lang, & San Diego, 2014; Garbarino & Strabilevitz, 2004; Rodgers & Harris, 2003; Susskind,
2004; Swaminathan, Lepkowska-White, & Rao, 1999; Garbarino & Strabilevitz, 2004). In the
case of women, online shopping wasn’t found to be as convenient and practical as for men
(Rodgers & Harris, 2003). The technology for online shopping is another reason. Studies of
information systems have shown differences in gender with respect to individual adoption and
continuous use of technology (Venkatesh et al., 2003, 2012). New technologies are being
differently assessed by men and women (Venkatesh, 1999). Women have been reported to be
more concerned about the Internet (Susskind, 2004). Women were emotionally less satisfied
with online shopping than men, skeptical about e-business and even less satisfied online
shopping than men (Rodgers & Harris, 2003). According to prior studies, women prefer more
information than men (Garnik & Basinska, 2013), females perceive greater risk than men
(Dillon et al., 2014), while men conduct more online transactions than women (Brown et al.,
2003; Dholakia & Uusitalo, 2002; Garbarino & Strahilevitz 2004; Kim et al., 2014;Teo, 2001).
Additionally, there are different products which male and female consumers want to buy.
Male consumers are more interested in hardware, software and electronics, for example, while
female consumers are more concerned about food, drinks and clothing. At the early stage of
ecommerce there used to be only male-oriented types of products available online (Van Slyke,
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Comunale, & Belanger, 2002). Women did not shop online because the products they were
interested in could not be found.
Moreover, women were found to need to touch a product in their assessment more than
men (Citrin et al., 2003). The impossibility of touching or testing products, could lead to fewer
women online buyers. This feature has a negative impact on online purchasing, especially for
products requiring more tactile testing (i.e. for clothing and accessories).
In prior information and technology acceptance research, regarding gender, earlier
studies have shown that mobile services are of more use to men than women. The reason why
men appear to be more task-oriented than women is because electronic banking services are
generally driven by goal. Furthermore, several empirical studies show the statistical differences
in the mobile banking environment between female and male respondents. For example, women
are more at risk in online shopping than men (Garbarino & Strahilevitz, 2004), peer reviews in
mobile services have a higher impact upon women, and men are more likely than women to use
mobile banking (Koenig-Lewis, Palmer, & Moll, 2010; Laukkanen & Pasanen, 2008).
Additionally, men are more concerned about the cost of accessing the Internet and service
charges than women. As there exists influences of gender in consumers behavioral patterns this
study hypothesizes:
H10: Gender will have moderating effect on relationships among predictors of intention
to use an online apparel subscription service
In comparison with the original UTAUT2 which comprised the three moderator variables
gender, age and prior experience, this study does not include prior experience as a moderator
variable. The first reason is that, this study is not a longitudinal in nature, thus it cannot capture
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increased user experience at different time frames. Furthermore, the current research includes
only two moderators in view of the research resources. Additionally, instead of the construct
‘Facilitating Condition’ of the original UTAUT2, the construct for this study have been named
as ‘Curated Service’. The rationalization behind this is in case of online apparel subscription
service the professional stylist or curator works as a facilitator to better serve the customer.
Chapter 3.
Methods and Procedures
This chapter provides a description, with rationale for all the methods used in this study.
A quantitative research design was used to understand the antecedents of adult consumers use of
online apparel subscription services. According to Creswell and Creswell (2017), quantitative
research is appropriate when the researcher tests a theory by specifying narrow hypotheses and
collects data to support or refute the hypotheses. Quantitative analysis is useful in understanding
and establishing the relationships among variables (Creswell & Creswell, 2017). As this study
was designed as theory-based hypothesis testing, a quantitative research design was chosen.
3.1. Approval of the Use of Human Subjects
Prior to conducting human related research, application for permission was submitted to
the Institutional Review Board (IRB) Human Subject Review Committee of the Louisiana State
University (LSU). The relevant personal evaluated the application and provided consent for this
research prior to recruitment of participants. Original approval was received via email (see
Appendix B). Following documents were submitted and approved by the LSU Human Subject
Review Committee: Invitation to participate in research (see Appendix C) and Survey (see
Appendix D). There was no foreseeable risk identified that may threaten human subjects in thus
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the welfare and rights of the human subjects were appropriately protected. Potential participants
were assured confidentiality of data from their voluntary participation.
3.2. Recruitment of Study Participants
Participants for this research were recruited through personal emailing to potential
participants (see Appendix C), posting the survey link in social media platforms and using the
Amazon Mechanical Turk (MTurk) platform. Amazon Mechanical Turk (referred to as Mturk;
located at mturk.com) is an online marketplace designed to allow individuals or groups known
as requesters to pay other individuals (workers) for completing small online tasks. A
selfadministered Qualtrics survey link was generated and distributed to MTurk participants, and
the social media platforms (Facebook, Instagram, Twitter, LinkedIn, different subscription
service related blogs, and the YouTube video comment section).
The rationality to use both the Mturk and social media platforms at the same time was to
eliminate the disadvantages of each data collection procedure. Previous research stressed that,
although efficient in nature, Mturk participants represent a particular age range of the population
(mostly young) with specific income level (low income level), so the data is not diverse in
nature, thus it may not be a representative sample of the actual population (Antoun, Zhang,
Conrad, & Schober, 2016; Huff & Tingley, 2015). Also, the reliability of Mturk data was found
to be lower than other sources of data (Rouse, 2015) and there exists some concern over the
quality of Mturk data as respondents are getting minimal payments for their task (Casler, Bickel,
& Hackett, 2013; Sheehan, 2018). However, using social media platforms and a personal email
platform have some advantages and disadvantages as well. It offers the opportunity to reach
participants that are diverse in demographic nature and the data represent more real-world
phenomena (Antoun et al., 2016; Casler et al., 2013). But the response return rate is lower in
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email and social media platform approaches (Antoun et al., 2016; Casler et al., 2013). Taking
these issues into consideration it was decided that, using both the platforms together will provide
the opportunity to triangulate and thus eliminate possible limitations of the respective platforms.
3.3. Survey Design
Consent to participate in the research was obtained prior to participants accessing the
survey questions. This was obtained through a description of the research and informing
participants that proceeding to take the survey constituted consent (see Appendix D).
A survey was constructed to collect the data necessary to answer the hypotheses. Survey
construction for data collection in behavioral research is well established as it can be designed to
measure and quantify variables of interest, and decrease the cost and time required for data
collection (Fricker & Schonlau, 2002; Wright, 2005). It was suggested that “A strategy to
develop scale measurement is to find a suitable existing instrument whose results generalize to a
broader class of measures that legitimately employ the same name” (Nunnally & Bernstein,
1994, p. 53). Statements used in this survey were adapted from published research articles and
validated measures for the eight key constructs of the proposed conceptual framework:
Perceived Usefulness, Perceived Ease of Shopping, Social Influence, Curated Service, Hedonic
Motivation, Price Value, Habit and Behavioral Intention.
An online survey was created using Qualtrics software to gather data (see appendix D).
Online surveys have been found to be an appropriate method to gather large quantities of data
that can be quantitatively examined (Creswell & Creswell, 2017). For this study, one qualifying
question “Have you ever used apparel subscription services?” was used to prequalify
participants (see Appendix D).
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The survey was purposively constructed to provide the data necessary to test each
hypothesis. Modifications were made to UTAUT2 to adapt it to online apparel subscription
services. In the original UTAUT2, the construct ‘Performance Expectancy’ and ‘Effort
Expectancy’ was written for technological performance measures; here the term ‘Perceived
Usefulness’ and ‘Perceived ease of shopping’ was used as they were more appropriate for the
context. Further, instead of the construct ‘Facilitating Condition’ of the original UTAUT2, the
term ‘Curated Service’ is used to consider ‘Consumers’ perceptions of the stylists’ help to
perform shopping using online fashion subscription services’ (Venkatesh et al., 2012).
Additionally, the construct ‘Habit’ has been described as the extent to which people tend
to perform behaviors automatically because of lifestyle (Venkatesh et al., 2012). Here it was
hypothesized that people who have a habit of buying apparel online, will likely subscribe to
online apparel subscription services. So, the items are written for measuring online buying
habit, not online apparel subscription habit.
To test the moderating effects of age and gender, relevant demographic questions were
added to the survey. These demographic questions were adapted from prior literature (Yu,
2012). As the participants age were set at 18 or above, the age-related questions’ options were
rewritten to fit within the research study.
Current members of an apparel subscription service were targeted as the potential
participants for the study. In order to meet the objective of the present study, specific criteria
were evaluated for each of the selected apparel subscription service providers to be included in
the study. Specifically, all the selected subscription service providers operate in the U.S.,
clothing is the sole product offered, stylist or curator service is available for each of the service
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providers, they cater to either women, men or both, they have a Box service, and they all have
free return and shipping policies (See Appendix A).
3.4. Survey Questions and Hypothesis Testing
Each question on the survey (see Appendix D) was designed to provide the data needed to
answer the following hypothesis:
H1: Perceived usefulness will significantly affect an individual’s intention to use an online
apparel subscription service, was answered by evaluating data collected in Questions 5 and
Question 11 of the survey.
H2: Perceived ease of shopping will significantly affect an individual’s intention to use an
online apparel subscription service, was answered by evaluating data collected in Question 6 and
Question 11 of the survey.
H3: Social influence will significantly affect an individual’s intention to use an online apparel
subscription service, was answered by evaluating data collected in Question 7 and Question 11
of the survey.
H4: Curated service will significantly affect an individual’s intention of using an online apparel
subscription service, was answered by evaluating data collected in Questions 8 and Question 11
of the survey.
H5: Hedonic motivation is positively related to the use of an online apparel subscription service,
was answered by evaluating data collected in Questions 9 and Question 11 of the survey.
H6: The desire to get value for the money spent is positively related to the use of an online
apparel subscription service, was answered by evaluating data collected in Questions 10 and
Question 11 of the survey.
H7: Individuals who have a habit of purchasing apparel online will have a positive intention to
use an online apparel subscription service, was answered by evaluating data collected in
Questions 4 and Question 11 of the survey.
H8: Behavioral intention will significantly affect an individual’s behavior of using an online
apparel subscription service, was answered by evaluating data collected in Questions 11 and
Question number 14.
H9: Age will have moderating effect on relationships among predictors of intention to use an
online apparel subscription service, was answered by evaluating data collected in Question
number 5, 6, 7, 8, 9, 10, 11 and 12 of the survey.
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H10: Gender will have moderating effect on relationships among predictors of intention to use
an online apparel subscription service, was answered by evaluating data collected in Question
number 5, 6, 7, 8, 9, 10, 11 and 13 of the survey.
3.5. Explanation of the Survey
Section A explains the nature of the study and gains consent to participate. Section B
provides a brief explanation of apparel subscription services (see Appendix D). This ensures
that potential participants understand what services are offered. A participant may be a current
user of a subscription service, but be unaware of the term apparel subscription service. After
participants read the brief description of apparel subscription, they are immediately given a
qualifying question “Have you ever used an apparel subscription service?”. If the participant
answers no, to the question, skip logic in the survey takes them to the end of the survey, thanks
them for their time, and exits. Only potential participants with prior or current experience with
an apparel subscription service were considered as users and thus eligible as participants in this
research. Further in order to gain participants’ level of familiarity with apparel subscription
services they were given a list of selected current apparel subscription providers in the U.S. and
requested to select those that they were familiar with (see question number 3 in the Appendix
D).
Section C, contained the survey questions (see Appendix D). Measurements in the
research model (items used in the survey) were based on items used in Venkatesh et al. (2012).
Items were reworded carefully and counter-checked with two social science researchers to fit
within the present research context. The objective of developing a survey is to achieve its goals
for the research and that the basic criteria of relevance and precision are always taken into
consideration (Creswell & Creswell, 2017). The theories and models, in the theoretical
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framework and investigative hypotheses presented in Chapter 2, have significantly guided the
process of survey design. Items were only integrated with associated construct measure based on
their relevance to the research goals.
Each construct was measured using 7-point likert scale. This is because attitudes and
opinions are effectively evaluated and quantified by using Likert scales (Croasmun & Ostrom,
2011). A Likert scale is often used to collect data on a range of attitudes and often used in
consumer behavior studies (Croasmun & Ostrom, 2011; Kang, Liu, & Kim, 2013; Venkatesh et
al., 2003, 2012; Zhou, Dai, & Zhang, 2007; Zhou, Lu, & Wang, 2010).
Section D focused on respondents’ demographic data relating their gender and age. The
age and gender related information were gathered at the very end of the survey as participants
might feel sensitive to provide this information (Roberson & Sundstrom, 1990). Finally, to
gather data about respondents’ use of subscription services a question was set related to their
usage frequency of online apparel subscription services (see Appendix D).
Strategies were considered in designing the survey to improve the response rate. The
question wording was simplified with the aim of facilitating understanding and answering by
respondents.
3.6. Validity and Reliability of Survey Instrument
3.6.1. Content Validity
Content validity or facial validity is assessed by expert judges to evaluate the
correspondence of the individual items with the concept through pre-testing with multiple
subpopulations (Creswell & Creswell, 2017; Pedhazur & Schmelkin, 2013). Often, the
researcher needs to determine the content validity through: 1) conducting a comprehensive and
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systematic literature review; 2) reviewing items with experts (pre-test); 3) conducting a pilot
test; and 4) purifying the items using coefficient alphas and factor analysis (Churchill &
Iacobucci, 1999;
Pedhazur & Schmelkin, 2013). This research utilized all these strategies to test content validity
(face validity). Following an extensive literature review the initial items were identified and
afterwards two social and business research experts were requested to make their opinions on
the survey questions, on each item in each set (concept), in order to determine whether
individual items were consistent with the concept. According to their feedback, some minor
changes were made to the survey instrument. Further based on the factor analysis and
coefficients alpha values of survey pretesting data, changes were made to the survey instrument.
3.6.2. Construct Validity
Construct validity, together with convergent validity and discriminant validity, assess the
degree to which a measurement is represented and logical. Construct validity refers to the
degree in which a test measures an intended hypothetical construct (Creswell & Creswell, 2017).
Construct validity could be determined by (1) correlation analysis, (2) factor analytics, and (3)
the multitrait, multispecific correlation method matrix. Others suggest that the three forms of
validity most widely accepted are convergent, discriminatory and nomological (Campbell &
Fiske, 1959). The convergent validity is synonymous with the validity and correlation analysis
criterion (Zikmund, Babin, Carr, & Griffin, 2003) and is one way to establish the validity of the
construct for this research. It states that items which indicate a specific structure should
converge or share a high proportion of common variance (Pedhazur & Schmelkin, 2013). In
other words, the level of correlation between 2 measures of the same concept, with high
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correlations indicates that the scale measures the concept it intends to use. Therefore, reliability
is also a validity indicator (Pedhazur & Schmelkin, 2013).
3.6.3. Pre-testing the Survey
The survey was pretested to ensure validity and reliability. Pretesting helps researchers
identify areas of concern such as: are the questions understandable, non-ambiguous, or
prejudicial (Creswell & Creswell, 2017; Pedhazur & Schmelkin, 2013). Pedhazur and
Schmelkin (2013) suggested 25-50 participants in the pre-testing group is enough to uncover
survey issues. Fashion and Business program students enrolled in a Southern University of the
U.S. were given the initial survey for the pretesting purposes. In the pre-test, a basic statistical
analysis of the first pre-test was performed from March 10, 2019 to March 20, 2019. Based on
feedback received the survey was revised. A total 45 students participated in the survey
pretesting phase (42 undergraduate and 3 graduate students). To test for reliability and internal
consistency, a Cronbach alpha was generated at 0.7 which is considered an acceptable level for
survey construction (Pedhazur & Schmelkin, 2013). See Appendix E for the detailed survey
pretesting results. The reliability of the survey scale was found to be ‘0.925’ based on the
standardized Cronbach’s Alpha value (See Appendix E). Thus, the instruments' measures have
ensured that the concepts are adequately covered. The instruments to be used were reliable, and
valid, and thus were ready to use in the main survey.
3.7. The Population and Sample
The sample unit refers to the aggregate level of collected data for use in data analysis. It is
also termed as unit of analysis. The scientist treated each answer as a single source of data. The
objective of the research determines the analysis unit' (Cavana, Delahaye, & Sekaran, 2001, p.
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119). The analytical unit can make up either the individual or group investigated. In line with
the research objectives indicated in Chapter one and Chapter two, the analysis unit was selected.
A target of 1,000 potential participants over 18 years of age with previous experience with
clothing subscription services within the United States were sought to participate in this
research. The target population to reach was reasonable as data was collected using an online
Qualtrics survey administering tool. Prior research suggests that the online survey response
rates are 40 to 50 percent (Guo, Kopec, Cibere, Li, & Goldsmith, 2016) and 300 to 500
participants is good for factor analysis regardless of the communalities issue of data analysis
(MacCallum, Widaman,
Zhang, & Hong, 1999).
Customers of selected apparel subscription service providers were targeted as the
potential participants for the study. As mentioned above, using personal emailing, MTurk
participants recruitment system, and the social media platforms (Facebook, Instagram, Twitter,
LinkedIn, different subscription service related blogs, and the YouTube video comment section)
the targeted sample were contacted to participate in the study. In order to meet the objective of
the present study, for each of the potential participants and their respective apparel subscription
service providers’ specific criteria were evaluated carefully to be included in the study.
Specifically, respondents were allowed to participate in the study if their apparel subscription
service providers met the following criteria: operate in the U.S., clothing is the sole product
offered, stylist or curator service is available, they cater to either women, men or both, they have
a Box service, and they have free return and shipping policies. They must also list among the
top
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20 apparel subscription service providers name in the ‘My Subscription Addiction’ review of
2018 for their respective category (Gender wise category).
3.8. Data Collection
Once the survey was ready to collect field data, permission for data collection was
sought from IRB, LSU. After careful review of the survey, IRB determined there was no
potential harm for human subjects and gave the permission to proceed. Initially the Qualtrics
survey link was forwarded to potential participants that may use the service. Faculty members’
friends and colleagues were requested to participate if they use such service. Also, the survey
link was distributed to friends who were studying at different universities of U.S. and was
requested to distribute among the potential participants of their university. Further the link was
posted in different social media as discussed above. Finally, it was posted in MTurk platform.
The survey data was collected from the period of April 1, 2019 to April 30, 2019.
3.9. Data Analysis Step 1: Data Editing and Refining
All data collected by Qualtrics was downloaded in both Excel and the compatible form
for the respective data analysis software used. The very first step after collecting data was to
purify the data so that incomplete surveys, biased responses etc. were eliminated. In survey
research, missing data or missing values are pervasive issues that can cause problem with data
analysis and inferences. Data and values may be missing when a respondent fails to respond to
one or more questions intentionally or unintentionally. Response bias (also called survey bias)
is the tendency of a person to answer questions on a survey untruthfully or misleadingly. For
example, they may feel pressure to give answers that are socially acceptable or may choose the
same responses for all the items of the survey (Statistics How To, n.d.). In this study response
bias was identified by examining whether respondents clicked the same likert scale score for all
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the items of the survey and how much time was spent to take the survey. If response bias is
identified it indicates that the responses do not necessarily reflect the behavioral facts that were
under investigation. In this study the missing data were simply omitted from further processing,
as the collected number of responses were adequate to proceed without missing data
After initial screening and editing of the raw data using Excel tools, files were imported
into the SmartPLS version 3.2.8.data analysis software package for analysis to summarize data
and information. Descriptive statistics such as minimum, maximum, frequency, percent,
average, standard differences in skewedness, kurtosis were reported. Tables of the results are
presented in Chapter 4.
Step 2: Test of Normality
The scale data was evaluated to determine the normality of its distribution. A normality
test is used to determine whether sample data has been drawn from a normally distributed
population (within some tolerance). In order to perform multivariate statistical analysis to
conduct factor analysis and structural equation modeling, data needs to be normally distributed
(Samani, 2016). The normality of data was tested by estimating the value of skewness and
kurtosis from the distribution of scores of the latent variables. Skewness and kurtosis are
statistical approaches for measuring the symmetry (skewness) and peakedness (kurtosis) of a
distribution (Samani, 2016). The absolute values of skewness should not exceed 2, whereas the
kurtosis value should not exceed 7. If they do so, it indicates the data are not normally
distributed (West, Finch & Curran, 1995). The skewness and kurtosis were evaluated using the
generated table by SmartPLS software version 3.2.8 as shown in Chapter 4.
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.
Step 3: Identifying and Dealing with Outliers
As discussed by Samani (2016), an outlier refers to an examination that is statistically far
in comparison to the rest of the research data set. Statistical inferential tests can be very
sensitive to outliers, usually because the estimations rely on squared deviations from the mean.
One or two values that are far from the mean can alter the results considerably. To identify
outliers in a large sample size of 80 and above, a standardized score value of +/- three was
suggested to be used. Based on this standard, to identify outliers in this study, the data were
examined for all variables using SmartPLS version 3.2.8. Data were recognized by adopting the
standard score, which have a mean of 0 and a standard deviation of 1 (Samani, 2016).
Step 4: Partial Least Square (PLS) Based Structural Equation Modeling (SEM)
The Partial Least Squares (PLS) based structural equation modeling method, is a two-
step technique, where in the first step all the items to measure latent variables estimate the outer
relationship among variables and in the second step the interrelationship among latent constructs
are evaluated (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005). The PLS approach was used as it is
mainly considered for prediction purposes (Cepeda-Carrion, Cegarra-Navarro, & Cillo, 2019).
The empirical measures of PLS based SEM enable the researcher to determine how well the
theory fits the data. Therefore, using PLS-SEM enabled the researcher to measure the model's
predictive potential and competences to judge the quality of the model. Additionally, it is a
useful exploratory analysis tool particularly for evaluating predictive models if there exist any
possibility of collinearity among factors (Yu, 2012). A similar approach was taken in the
original UTAUT studies as well (Venkatesh et al. 2003; Venkatesh & Zhang 2010).
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As recommended by Samani (2016), Lee, Fairhurst and Wesley (2009), Yu (2012) and
Venkatesh et al. (2012), factor loadings of the items used to measure individual constructs,
composite reliability constructs measure, and the average variance extracted by each constructs
(AVE) were analyzed to evaluate the convergent validities. Additionally, the discriminant
validity was examined by testing the squared roots of AVE and the correlations among
constructs. Reliability was evaluated by evaluating internal consistency reliability (ICR) as
suggested by Venkatesh et al. (2003) and Venkatesh and Zhang (2010).
In structural equation modeling analysis, the Goodness-of-Fit (GoF) is a measure to
assess the overall model fit (Tenenhaus et al., 2005). For the model fit evaluation in the PLS
approach, the standardized root mean square residual (SRMR) is evaluated, which is measured
by assessing the difference between observed correlation and model implied correlation matrix.
A value less than 0.10 or of 0.08 (in a more conservative version) are considered a good fit.
Henseler et al. (2014) introduced the SRMR as a goodness of fit measure for PLS-SEM.
Additionally, in the Structural Equation Modeling (SEM) literature the normed fit index (NFI) is
evaluated to assess the model fit. The closer the value of NFI to 1, the better the fit (Hair Jr.,
Hult, Ringle, & Sarstedt, 2016).
Chapter 4.
Results and Discussion
The purpose of this research was to investigate consumers’ behavioral intention and use
of online apparel subscription services and to uncover predictors that influence such behavior.
Using the extended Unified Theory of Technology Acceptance and Usage (UTAUT2) as a basis,
ten hypotheses were developed and tested using a Partial Least Square (PLS) regression analysis
technique. The ten hypotheses that were formulated and tested were as follows:
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.
H1: Perceived usefulness will significantly affect an individual’s intention to use an
online apparel subscription service
H2: Perceived ease of shopping will significantly affect an individual’s intention to use
an online apparel subscription service
H3: Social influence will significantly affect an individual’s intention to use an online
apparel subscription service
H4: Curated service will significantly affect an individual’s intention of using an online
apparel subscription service
H5: Hedonic motivation is positively related to the use of an online apparel subscription
service
H6: The desire to get value for the money spent is positively related to the use of an
online apparel subscription service
H7: Individuals who have a habit of purchasing apparel online will have a positive
intention to use an online apparel subscription service
H8: Behavioral intention will significantly affect individual’s behavior of using an online
apparel subscription service
H9: Age will have moderating effect on relationships among predictors of intention to
use an online apparel subscription service
H10: Gender will have moderating effect on relationships among predictors of intention
to use an online apparel subscription service
This chapter consists of an overview of data screening, sample characteristics, and results
of measurement model estimation. The hypotheses testing results of the direct and moderating
effect related relationships among variables of the proposed conceptual framework were
evaluated. Finally, Partial Least Square (PLS) model fit results are reported and the discussion
of these results are provided.
4.1. Data Screening Results
Prior to data analysis, data quality was tested using the standard data screening and
cleaning procedure outlined in Chapter 3 to evaluate missing values, outliers, and normality of
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the data distributions. A total of 935 survey responses were recorded out of which, 201
respondents did not satisfy the survey participation criteria, so these responses were omitted
from further proceedings leaving 734 responses. Of these remaining responses, a further 56
responses were not usable because of missing data and response bias. Response bias occurs
when respondents select the same responses for all the likert scale type response choices on a
questionnaire (Statistics How To, n.d.). This resulted in a total of 678 being usable for data
analysis, an overall response rate of 67.8%. Using SmartPLS version 3.2.8 software, the data
quality report for the usable data was generated and is summarized in Table 4.1.
As displayed in Table 4.1 there are no missing values in the final refined data set. The
mean, median, minimum, and maximum values indicate that there were no outliers as well. To
assess the degree of normality, values of skewness and kurtosis can be examined. West, Finch
and Curran (1995) suggested that absolute values of skewness and kurtosis exceeding 2 and 7
respectively are indicative of non-normal distributions. As can be seen in Table 4.1 there is no
such indication and, thus, it can be concluded the final data were normally distributed.
Table 4.1 Data Quality Report (n=678)
Survey
Items
Missing
Values
Mean Median Min. Max Standard
Deviation
Excess
Kurtosis
Skewness
Q4_1 0 5.547 6 1 7 1.141 1.907 -1.118
Q4_2 0 5.44 6 1 7 1.251 0.168 -0.707
Q4_3 0 5.206 5 1 7 1.396 0.013 -0.688
Q4_4 0 5.432 6 1 7 1.265 0.617 -0.888
Q5_1 0 5.363 6 1 7 1.132 1.116 -0.923
Q5_2 0 5.242 5 1 7 1.339 0.105 -0.71
Q5_3 0 5.37 6 1 7 1.207 0.514 -0.775
Q5_4 0 5.513 6 1 7 1.272 0.817 -0.982
Q6_1 0 5.223 5 1 7 1.288 0.703 -0.91
Q6_2 0 5.618 6 1 7 1.148 0.823 -0.891
Q6_3 0 5.426 6 1 7 1.234 0.555 -0.886
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.
Q7_1 0 4.689 5 1 7 1.523 -0.375 -0.65
Q7_2 0 4.723 5 1 7 1.6 -0.375 -0.605
Q7_3 0 4.841 5 1 7 1.546 -0.226 -0.683
Q8_1 0 5.22 5 1 7 1.21 0.613 -0.778
Q8_2 0 5.243 5 1 7 1.244 0.527 -0.773
Q8_3 0 5.314 5 1 7 1.195 0.677 -0.811
Q8_4 0 5.348 6 1 7 1.298 0.736 -0.877
Q9_1 0 5.487 6 1 7 1.198 0.831 -0.876
Q9_2 0 5.532 6 1 7 1.179 1.242 -0.93
Q9_3 0 5.559 6 1 7 1.182 0.933 -0.888
Q10_1 0 4.695 5 1 7 1.65 -0.576 -0.599
Q10_2 0 4.788 5 1 7 1.623 -0.441 -0.594
Q10_3 0 4.706 5 1 7 1.687 -0.519 -0.631
Q11_1 0 5.193 5 1 7 1.377 0.906 -1.04
Q11_2 0 5.236 5 1 7 1.364 0.855 -0.931
Q11_3 0 5.353 6 1 7 1.359 1.04 -1.059
Q12 0 2.058 2 1 6 0.901 1.498 1.074
Q13 0 1.406 1 1 5 0.542 6.339 1.548
Q14 0 3.416 3 1 7 1.55 -0.023 0.524
4.2. Sample Characteristics of the Study
Respondents’ demographic characteristics are summarized in Table 4.2. The results
indicated that 60.77 percent of respondents were female, 38.79 percent were male, and 0.44
percent were ‘prefer not to answer’ category. These percentages are consistent with prior
subscription related research (Chen, et al., 2018; Columbus 2018; Kestenbaum, 2018) as well as
fashion related research participants (Kang & Kim, 2012). Demographic data also showed that
the majority of the apparel subscription service users were young with 52.51 percent in the age
group 26 to 35 years and another 25.81 percent in the 18 to 25 years age group. These findings
align with the demographic characteristics outlined in the subscription review website (MSA,
2018) as well as with prior research reports (Chen, et al., 2018; Columbus 2018; Kestenbaum,
2018).
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Table 4.2. Demographic Characteristic of the Sample (n=678)
Demographic Variable Percent
Gender Female 60.77%
Male 38.79%
Other
0.44%
Age 18 to 25 Years 25.81%
26 to 35 Years 52.51%
36 to 45 Years 13.86%
46 to 55 Years 5.90%
56 to 65 Years 1.77%
66 and above
Total
0.15%
100%
Respondents’ apparel subscription service usage was profiled in Table 4.3. In response
to apparel subscription service usage, 13 popular service providers were listed, and participants
were asked to check all that apply; there was an option ‘other’ for respondents to list sites not
listed. Results showed that Stitch Fix topped the usage chart followed by Trunk Club, which
was also reported in prior research studies (Chen, et al., 2018; Columbus 2018; Kestenbaum,
2018).
Table 4.3 Apparel Subscription Service Usage
Service Provider Users Percent Frequency of use Percent
Stitch Fix 355 16.96% Every week 5.90%
Trunk Club 212 10.13% Once every two weeks 5.75%
Shirt cycle 180 8.60% Once every three weeks 5.75%
Frank and Oak 179 8.55% Once a month 27.73%
Dia & Co. 146 6.98% Once every three months 27.44%
The Mr. & Ms. Collection 145 6.93% Once every six months 16.37%
Trendy Butler 141 6.74% Once a year 11.06%
ModaBox 139 6.64%
Rent the Runway 124 5.92%
ThreadBeast 121 5.78%
LeTote 110 5.26%
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.
Bombfell 104 4.97%
MM.LaFleur 92 4.40%
Other (Please specify) 45 2.15%
To assess the magnitude of apparel subscription service usage, questions on how
frequently they used ‘every week’ to ‘once a year’ were analyzed. When it came to the
frequency of the use, the majority of the participants of the study used the service once a month
(n= 188, 27.73 percent) or once every three months (n= 186, 27.44 percent).
4.3. Results of Conceptual Framework Analysis
The sole purpose of the present study was to identify the predictors of online apparel
subscription box choice usage. Based on the purpose of the present research study and the
nature of the variables, the Partial Least Square (PLS) based Structural Equation Modeling
approach was selected for data analysis (Cepeda-Carrion et al., 2019). A similar approach was
taken in the original UTAUT studies (Venkatesh et al. 2003; Venkatesh & Zhang 2010), as well
as in prior literature (Yu, 2012).
In the PLS approach, assessing the measurement model involves testing the validity and
reliability of the items in terms of convergent and discriminant validity (Lee et al., 2009; Yu,
2012). As a part of convergent validity test, the items loading or indicator's reliability must be
above 0.7 to demonstrate a reliable indicator for a specific construct. Based on previous
findings of studies using the UTAUT model, factor loadings, composite reliability, and the
average variance extracted (AVE) were reported to test the convergent validities (Lee et al.,
2009; Yu, 2012), whereas the discriminant validity was evaluated by comparing the squared
roots of AVE and the correlations among constructs. The reliability of scale measure means the
consistency of measurement, meaning the degree to which the items used in this survey will
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produce the same results if they were replicated in a future event under the same conditions
(Creswell & Creswell, 2017). Composite reliability is a measure of internal consistency in scale
items, for example Cronbach’s alpha is one of these types of reliability measures (Netemeyer,
Bearden, & Sharden, 2003).
The threshold for composite reliability is values above 0.6 (Statistics how to, n.d.),
however it has been suggested that composite reliability should be in the range between 0.7 to
0.8 or higher based on the number of items measuring the construct (Netemeyer et al., 2003).
As discussed in Chapter 3, the convergent validity is a measure that indicates how coherently
scale items are measuring the same construct, and the suggested threshold value is above 0.7
(Creswell & Creswell, 2017; Pedhazur & Schmelkin, 2013). Discriminant validity measures
whether two different constructs or concepts that are not supposed to be related are actually
unrelated (Creswell & Creswell, 2017). The method for measuring discriminant validity
compares the correlation coefficients among constructs and evaluates whether their value within
the same construct are highest or not in comparison to values with other construct correlation
measures (Yu, 2012). The reliability of the measures were assessed by examining internal
consistency reliability (ICR) as suggested by Venkatesh et al. (2003) and Venkataesh and Zhang
(2010).
Table 4.4 Factor Loadings, Composite Reliability and AVE
Constructs Survey Items Loadings Composite
Reliability
AVE
Perceived Usefulness Q5_1
Q5_2
Q5_3
Q5_4
0.778
0.838
0.829
0.830
0.837
0.563
Perceived Ease of
Shopping
Q6_1
Q6_3
0.900
0.824
0.701 0.513
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.
Social Influence Q7_1
Q7_2
Q7_3
0.897
0.911
0.912
0.892 0.733
Curated Service Q8_1
Q8_2
Q8_3
Q8_4
0.816
0.841
0.854
0.829
0.855 0.597
Hedonic Motivation Q9_1
Q9_2
Q9_3
0.851
0.883
0.854
0.829 0.619
Habit Q4_1
Q4_2
Q4_3
Q4_4
0.731
0.843
0.815
0.870
0.835 0.560
Price Value Q10_1
Q10_2
Q10_3
0.910
0.922
0.914
0.903 0.757
Behavioral Intention Q11_1
Q11_2
Q11_3
0.899
0.901
0.911
0.888 0.725
After running factor analyses, it was found that the majority of the factor loadings of the
items were acceptable. However, one question used to assess perceived ease of shopping (as
shown in Appendix F) was below 0.7 and was thus removed. The perceived ease of shopping
factor was still part of the model as the remaining two questions had acceptable factor loadings.
Thereafter, the analysis was performed again, and the obtained results are tabulated in
Tables 4.4, Table 4.5 and in Figure 4.1. As shown in Table 4.4, all factor loadings had
demonstrated acceptable reliability and convergent validity values in the final measurement
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model as all the values were found to be greater than the threshold value 0.7 (Lee et al., 2009;
Yu, 2012), the composite reliabilities met the criteria of meeting or exceeding the threshold value
of 0.6 (Yu, 2012), and the average variance extracted were greater than 0.5, which was also
surpassed the expected threshold value (Yu, 2012).
Table 4.5. Measurement Model Estimation
ICR Mean SD PU PEoS SI CS HM HT PV BI Use
PU 0.837 5.37 1.24 0.750
PEoS 0.701 5.32 1.26 0.707 0.716
SI 0.892 4.75 1.55 0.469 0.428 0.856
CS 0.856 5.28 1.23 0.714 0.644 0.514 0.773
HM 0.828 5.53 1.18 0.685 0.676 0.343 0.654 0.787
HT 0.832 5.41 1.26 0.638 0.631 0.284 0.47 0.492 0.748
PV 0.904 4.73 1.65 0.505 0.429 0.63 0.553 0.347 0.298 0.870
BI 0.888 5.25 1.36 0.734 0.664 0.522 0.722 0.635 0.445 0.626 0.851
Use N/A 3.42 1.55 0.177 0.167 0.173 0.116 0.171 0.115 0.224 0.198 N/A
Notes:
1. ICR: Internal consistency reliability; SD: Standard Deviation
2. PU: Perceived Usefulness; PEoS: Perceived Ease of Shopping; SI: Social Influence; CS:
Curated Service; HM: Hedonic Motivation; HT: Habit; PV: Price Value; BI: behavioral
intention
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.
In Table 4.5 the discriminant and convergent validities testing results are summarized.
As can be seen in Table 4.5 the diagonal values are the square roots of average variance
extracted (AVE), and off-diagonal values across rows and columns are representing
correlationvalues among constructs. Since Table 4.5 shows all diagonal values were higher than
the off-diagonal values in the corresponding rows and columns and all internal consistency
reliability (ICRs) value were above 0.7, the evaluations of discriminant validity and the
reliability of constructs measures were found to be appropriate and reliable (Yu, 2012).
Figure 4.1. PLS model evaluation results
Notes: Asterisks (**) paths represents significant relationship at 0.05 confidence interval
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The hypothesis testing results are depicted in Table 4.7 and Figure 4.1. As shown, the
generated R2 adjusted were 0.685 and 0.723 that represented the percentage of variances
explained by the variables in the conceptual framework. The results indicate that the perceived
usefulness was the most powerful factor in affecting intention to use an apparel subscription
service. Additional supplementary PLS results have been tabulated in Appendix F.
Additionally, data analysis from this study demonstrated that respondents were significantly
influenced by price value, curated service, ease of shopping and hedonic motivation. The actual
usage behavior was also found to be significant influenced by individual’s behavioral intention.
However, the social influence and individuals’ habit of online shopping did not play a
significant role in determining behavioral intention and actual behavior. The moderating effects
of age and gender on the constructs toward behavioral intention, are also shown in Table 4.6.
Results of the moderating effects related hypothesis testing revealed that the age and gender did
not have a moderating effect.
As discussed in Chapter 3 for the model fit evaluation of the present study, the
standardized root mean square residual (SRMR) value was reported. A value less than 0.10 or
of 0.08 of SRMR are considered a good fit (Henseler et al., 2014). In addition to that, the
normed fit index (NFI) was evaluated to assess the model fit. It was also discussed earlier, the
closer the value of NFI to 1, the better the model fit (Hair Jr., Hult, Ringle, & Sarstedt, 2016).
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.
Table 4.6 The PLS Results on Hypothesis Testing
Direct Effects
Dependent Variable T-statistics Remarks
Behavioral
Intention
Use
Perceived Usefulness 4.34 S
Perceived Ease of Shopping 2.27 S
Social Influence 1.414 NS
Curated Service 4.028 S
Hedonic Motivation 3.457 S
Habit 1.776 NS
Price Value 6.281 S
Behavioral Intention 5.584 S
Age 0.319 NS
Gender 1.539 NS
Moderating Effects
Perceived Usefulness x Age 0.177 NS
Perceived Ease of Shopping
x Age
1.035 NS
Social Influence x Age 1.533 NS
Curated Service x Age 0.447 NS
Hedonic Motivation x Age 0.326 NS
Habit x Age 1.098 NS
Price Value x Age 1.52 NS
Perceived Usefulness x
Gender
0.552 NS
Perceived Ease of Shopping
x Gender
0.173 NS
Social Influence x Gender 0.781 NS
Curated Service x Gender 0.855 NS
Hedonic Motivation x
Gender
0.786 NS
Habit x Gender 0.554 NS
Price Value x Gender 0.682 NS
Behavioral Intention x Age 0.177 NS
Behavioral Intention x
Gender
0.961 NS
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Notes: S: Significant relationship at 0.05 confidence interval
NS: Non-significant relationship at 0.05 confidence interval
Table 4.7 PLS Model Evaluation Results
Dependent Variable
Behavioral Intention Use
R2 Adjusted 0.685 0.723
Path Co-efficients
Perceived Usefulness 0.406
Perceived Ease of Shopping 0.166
Social Influence -0.027
Curated Service 0.201
Hedonic Motivation 0.126
Habit -0.185
Price Value 0.269
Behavioral Intention 0.21
Table 4.8. Model Fit Results
Measures Saturated Model Estimated Model
SRMR 0.046 0.047
NFI 0.880 0.878
As can be seen from Table 4.8, both the SRMR and NFI values for the conceptual
framework of the present study are satisfactory. Finally, Table 4.9 summarizes the overall
hypothesis testing results for the present study.
4.4. Discussion
To predict the factors that will determine the behavioral intention and use of an online
apparel subscription service, this study tested a proposed conceptual framework by evaluating
116
.
formulated hypotheses based on the extended UTAUT. Data analysis revealed six of the ten
hypotheses were supported. In the following section, a brief discussion is provided on each of
the outcomes.
Table 4.9 A Summary of Hypotheses Testing Results
Hypothesis Results
H1 Perceived usefulness will significantly affect an
individual’s intention to use an online apparel
subscription service
Supported
H2 Perceived ease of shopping will significantly
affect an individual’s intention to use an online
apparel subscription service
Supported
H3 Social influence will significantly affect an
individual’s intention to use an online apparel
subscription service
Not Supported
H4 Curated service will significantly affect an
individual’s intention of using an online apparel
subscription service
Supported
H5 Hedonic motivation is positively related to the use
of an online apparel subscription service
Supported
H6 The desire to get value for the money spent is
positively related to the use of an online apparel
subscription service
Supported
H7 Individuals who have a habit of purchasing
apparel online will have a positive intention to use
an online apparel subscription service
Not Supported
H8 Behavioral intention will significantly affect
individual’s behavior of using an online apparel
subscription service
Supported
H9 Age will have moderating effect on relationships
among predictors of intention to use an online
apparel subscription service
Not Supported
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H10 Gender will have moderating effect on
relationships among predictors of intention to use
an online apparel subscription service
Not Supported
H1: Perceived usefulness will significantly affect an individual’s intention to use an online
apparel subscription service was supported.
In this study the perceived usefulness construct was measured using four items that
covered the time convenience, efficiency, usefulness and product assortment aspects of apparel
shopping. Data analysis indicated that consumer intention to use an online apparel subscription
service was significantly impacted by perceived usefulness
As shown in Figure 4.1 the path coefficient for perceived usefulness and behavioral
intention is 0.406 which is highest among all the paths. This means the perceived usefulness
variable had the greatest influencing factor towards the behavioral intention to use an online
apparel subscription service (Guritno & Siringoringo, 2013). It implies that consumers have a
positive attitude toward the use of an online apparel subscription service when consumers feel
greater benefits such as convenience, efficiency, finding desired apparel items while using
shopping subscription services.
This finding of the present study is in accordance with many prior studies as usefulness
was a significant predictor of attitude towards online retail shopping behavior (Childers et al.,
2001; Chiu et al., 2009; Gillenson & Sherrell, 2002; Ha & Stoel, 2009; Lim et al., 2016; Lin,
2007; Sohn, 2017; Yen & Wu, 2016). Convenience was found to be an important predictor of
online shopping attitudes (Childers et al., 2001). Researchers have shown that consumers
intentions to transact with e-vendors depends on the perceived usefulness of the platform (Gefen,
118
.
Karahanna, & Straub, 2003). Additionally perceived usefulness was found to be a significant
predictor of behavioral intention and use of various platform like online shopping (Brown et al.,
2003; Constantinides, 2004; Gupta, Dasgupta & Gupta, 2008; Kim et al., 2014; Kim et al, 2009;
Li et al., 2017; Mohd Suki et al., 2008; Moslehpour et al., 2018; SivaKumar & Gunasekaran,
2017), mobile banking service (Luarn & Lin, 2005; Riquelme & Rios, 2010), smart instore
technology acceptance (Kim et al., 2017), internet service acceptance via mobile technology (Lu
et al., 2005; Morosan & DeFranco, 2016), technology acceptance research (Davis, 1989;
Venkatesh et al., 2007; Venkatesh et al., 2012), and online booking services (Kucukusta et al.,
2015). Ha and Stoel (2009) identified that e-shopping quality for apparel products consisted of
customer service and an experiential aspect and suggested that consumer perceptions of
usefulness and attitude toward e-shopping influence intention to shop online. Another research
study suggested that consumers may accept online customization for their apparel shopping
when they feel the shopping platforms are competent and useful for purchasing customized
apparel (Cho & Fiorito, 2009). Kim et al. (2017) suggested that perceived usefulness
determined the clothing purchase intention using virtual mirror technology. Further, SivaKumar
and Gunasekaran, (2017) also reported that perceived usefulness was an important predictor to
the intention to buy clothing online. Additionally, Li, Chen and Liu, (2017) identified
usefulness as a significant predictor to repurchase clothing online. The same was found with
Taiwanese consumers’ e-shopping behavior (Moslehpour et al., 2018).
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Apparel subscription service providers should offer and maintain these usefulness and
ease of shopping aspects of the customer shopping experience in their platform in order to meet
consumer shopping needs as well as to retain their customers. Yen and Wu (2016) reported that
perceived usefulness, and perceived ease of use were the significant determinant that influenced
consumers continued usage intention for online shopping. The same was found for other online
service providers as well, for instance Guritno and Siringoringo (2013) identified when
consumers got benefits such as faster ticket search, low efforts, and cost saving, consumers were
more willing purchase airline tickets online.
H2: Perceived ease of shopping will significantly affect an individual’s intention to use an
online apparel subscription service was supported
The perceived ease of shopping construct was evaluated using the effortlessness of
shopping and the ease to get the desired clothing items. Data analysis showed that consumer
intention to use an online apparel subscription service was significantly impacted by the
perceived ease of the shopping mode. This suggests that the ease of shopping with an online
apparel subscription service platform and the degree in which the shopper finds it as an
effortless experience will determine the intention of using such a service.
This outcome was expected as ease of use was also a significant predictor of positive
consumer attitude towards online retail shopping behavior in prior research studies (Childers et
al., 2001; Lin, 2007) as well as repurchase intention (Chiu et al., 2009; Yen & Wu, 2016).
Additionally, results of prior research suggested that perceived ease-of-use would act as
antecedents of attitude towards online purchasing (Gillenson & Sherrell, 2002; Van der Heijden
et al., 2003). Moreover, perceived effort expectancy influences perceived usefulness in mobile
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payment service in restaurants (Khalilzadeh et al., 2017) and perceived ease of use positively
related to the intention to use mobile shopping (Natarajan et al., 2017).
This finding in the present study is aligned with many other prior research outcomes as
well (Brown et al., 2003; Constantinides, 2004; Gupta, Dasgupta & Gupta, 2008; Davis, 1989;
Kim et al., 2014; Kim et al., 2017; Kim et al., 2009; Kucukusta et al., 2015; Li et al., 2017; Luarn
& Lin, 2005; Lu et al., 2005; Mohd Suki et al., 2008; Moslehpour et al., 2018; Morosan &
DeFranco, 2016; Riquelme & Rios, 2010; SivaKumar & Gunasekaran, 2017; Venkatesh et al.,
2007; Venkatesh et al). Perceived ease of use (PEU) was found to have a positive influence on
the online shopping intention (Ramayah & Ignatius, 2005). It was also found that perceived
ease of use influenced an individual’s attitudes towards online airline ticketing (Guritno &
Siringoringo, 2013). In addition, perceived ease of use was identified as a determinant factor for
mobile banking service adoption (Gupta et al., 2008; Luarn & Li, 2005; Riquelme & Rios 2010).
The data from the present research indicated that perceived ease of use played a major
role in influencing whether an individual would use an online apparel subscription service or
not. Retailers of such services should pay attention to by improving the navigation, and
reducing the effort required to shop using the platform. The results implied that to attract and
keep consumers, retailers should not only improve the overall usefulness of their service, but
also should design a simple and efficient ordering and fulfillment procedure to match consumer
shopping needs.
H3: Social influence will significantly affect an individual’s intention to use an online
apparel subscription service was not supported
In this study, the social influence construct was measured using terms like ‘people who
are important to consumer’, ‘who influence’ or ‘whose opinions consumer value’. Interestingly,
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when it came to social influence in determining the behavioral intention and actual use of an
online apparel subscription service, the data from this study provided a somewhat different
outcome from prior literature (Chevalier & Mayzlin, 2006; Cheung et al., 2008; Foucault &
Scheufele, 2002; Ho & Chen, 2014; Lian & Yen, 2014; Limayem et al., 2007; Kim et al., 2014;
Venkatesh et al., 2012). The initial hypothesis regarding this aspect was not supported. Data
from this study indicated social influence neither considerably influenced behavioral intention
nor significantly affected actual behavior in the online an apparel subscription service context.
This means, consumers who use apparel subscription services do not use the services based on
their family member or peer influence.
One possible reason behind this outcome could be users of such services are more
fashion-conscious individuals as reported by Woo and Ramkumar (2018). Fashion-conscious
consumers are more individualistic in nature (Park, 1998). Further individuality, a desire for
differentiation and a behavior of non conformity, appears to be a motivation for the adoption of
fashion consumption for such individuals (Cho & Workman, 2011; Goldsmith, Moore &
Beaudoin, 1999; Summers, 1970). Moreover, the individuality concept evokes bi dimensional
desire, that is, differentiation from others and a tendency towards independence. Also it was
mentioned that an independent fashion-oriented individual is aware of social norms but not
affected by them (Park, 1998). Additionally, as the data was collected in the U.S., which is
basically an individualistic society in nature, social influence might not affect consumers as
highly as it does in a collectivistic culture (Andrews & Bianchi, 2013).
Another possible reason could be the users of innovative apparel subscription services
are opinion leaders in nature. Researchers outlined the characteristics of opinion leaders as
younger, had more education, had higher incomes, and had higher occupational status (Cho &
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Workman, 2011; Goldsmith et al., 1999; Summers, 1970; Workman & Johnson, 1993). While
this research did not collect information regarding respondents’ education, income and
occupational status, it did collect respondents age with the majority being a younger population
(78.32% of respondents ages being 35 or less). However, fashion leaders would typically not
have their fashion items selected by others (in this case by the personal stylists). From this
perspective, there is another possibility that individuals who simply do not care much about
fashion and want to pay someone else to do the work for them are the users for such services. In
this regard future research should be formulated that can measure consumer lifestyle and fashion
opinion leadership to offer insights.
However, there are some prior studies that have reported subjective norms or social
influence not being significant in determining an individual’s intention to shop online (Çelik,
2011; Limayem, Khalifa, & Frini, 2000; Ming-Shen, Chih-Chung, Su-Chao, & Yung-Her, 2007)
and family has a relatively stronger influence than peers in influencing such behavior (Sahay &
Sharma, 2010). In another study, subjective norm did not influence behavioral intentions to
shop online rather it influenced the perceived ease of use of shopping online (Çelik, 2011).
Retailers of online apparel subscription services should consider this interesting finding
of the present study and can formulate a marketing strategy that can attract consumers who are
fashion-followers in nature rather than fashion-leaders. Also, in order to better cater to their
present consumers, which were found to be fashion-opinion-leader in nature, they can formulate
their service strategy to satisfy this customer group. Further, the nature of the conversation
between personal stylists and consumers of such services should be well planned, as this user
group might be individualistic in nature and they do not want to be influenced by others.
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H4: Curated service will significantly affect an individual’s intention of using an online
apparel subscription service was supported
Unlike the variable ‘facilitating condition’ of the original UTAUT, this study
incorporated a new construct ‘curated service’ as a determinant of behavioral intention to use an
online apparel subscription service. This construct was measured using subscription service
providers’ personal stylists’ knowledge, the level of help provided, and the fashion taste
matching between the consumer and stylists. It was found that curated service significantly
influenced participants’ behavioral intention.
This result was expected as the stylist’s curated box brought happiness along with
surprise to the consumer’s door (Bischof, et al., 2019; Gupta, Eilert, & Gentry, 2018).
Subscribers of such services receive customized boxes filled with merchandise based on their
fashion taste (Bischof et al., 2019). Gupta et al. (2018) suggested that a subscription service is a
self-gifting process where individuals’ can surprise themselves with curated products that can
exceed their expectations. Additionally, Tao and Xu (2018) reported that a customized delivery
might give customers the opportunity to try new styles that they were not aware existed.
A prior study showed that a curated service influenced consumers intentions to use a
customized fashion retailing service which supports the finding of the present study (Sebald and
Jacob, 2018). Cha, You, Gang and Park (2018) reported that the efficiency of curated shopping
is a positive factor that increased the intention to use curated shopping. Retailers should provide
a high-quality curated service that can meet customer needs. Gupta et al. (2018) reported that, if
consumers are not satisfied with a curated service, they become frustrated with the subscription
service provider as well as become reluctant to use it in the future.
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Based on this finding it is expected that retailers will offer regular training to their
personal stylists to make them knowledgeable regarding the ongoing fashion trends. Also, they
should take care of the fashion taste of their customer professionally. Finally personal stylists
should have the motivation to offer high quality customer service to each of the customers they
serve.
H5: Hedonic motivation is positively related to the use of an online apparel subscription
service was supported
In this study the hedonic motivation construct was measured using the fun and enjoyment
aspect of apparel subscription service usage. The data analysis showed that consumer intention
to use an online apparel subscription service was significantly impacted by an individual’s
hedonic motivation. This outcome suggests that consumers use an online apparel subscription
service as a source of entertainment, a finding supported by Gupta and Kim (2010) who found
that the intention to purchase from an Internet vendor was significantly influenced by the
pleasure of the shopping experience.
This finding was expected as opening the curated box with customized products for the
individual consumer may generate a positive emotional feeling of excitement associated with
surprise (Tao & Xu, 2018). Prior research reported that perceived enjoyment was found to have
a positive influence on the online shopping intention (Ramayah & Ignatius, 2005). Additionally,
enjoyment was found to be a consistent and strong predictor of attitude toward interactive virtual
shopping (Childers et al., 2001). Also, enjoyment influenced consumers repurchase intentions
(Chiu et al., 2009). Ha and Stoel (2009) reported that shopping enjoyment played a significant
role in determining consumers' e-shopping adoption.
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Moreover, Kim and Forsythe (2007) investigated online apparel shoppers' adoption of
technologies and the result revealed that the hedonic motivation had a stronger positive
relationship than functional motivations with the attitude toward using product virtualization
technologies. Additionally, perceived enjoyment was identified as positive influencing factor to
the intention to use mobile shopping (Agrebi & Jallais, 2015; Natarajan et al., 2017). Further, in
prior literature hedonic motivation appeared to be powerful predictor of online shopping
(Childers et al., 2001; Escobar-Rodríguez & Carvajal-Trujillo, 2014; Chiu et al., 2005;
Delafrooz et al., 2011). Opaloglu (2012) found that Turkish shoppers buy online for pleasure.
This literature supports the findings of this present study as well.
In one recent study, Woo and Ramkumar (2018) examined consumers utilitarian and
hedonic shopping motivation towards fashion and beauty products subscription service and it
was found that hedonic motivation played a significant role in the usage of such services.
Therefore, it is imperative that retailers incorporate a fun component in their subscription service
platform. Also, retailers need to train their personal stylists who work closely with consumers,
so that they can pay attention to consumers expectations in this regard and can fulfill these
needs.
As per Kim and Eastin (2011) hedonic shoppers usually look for fun, amusement,
enjoyment, fantasy, arousal, sensory stimulation, novelty, variety, and surprise. That is to say,
along with the fun part of shopping retailers should offer variety of brands, offer the most
updated styles, and/or incorporate surprise gift giveaway schemes to attract more hedonic
consumers. Additionally as mentioned above, Tao and Xu (2018) reported that this type of
service offers customer the opportunity to try new styles which is fun as well as low risk,
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because customer can typically return items for free. Retailers can continue to offer this type of
service to serve consumers’ hedonic needs and attain customer satisfaction.
H6: The desire to get value for the money spent is positively related to the use of an online
apparel subscription service was supported
The price value construct was measured using terms like better price, and good money
value for the product. The data analysis indicated that consumer intention to use an online
apparel subscription service was significantly impacted by price value. This means, consumers
who use an apparel subscription have the desire to get good money value for the products they
get from using the service.
This finding aligns with many prior studies as price was a major determinant in online
shopping (Chiang & Dholakia, 2003; Childers et al., 2001; Delafrooz et al., 2011; Irani &
Hanzaee, 2011; Lee, 2005; Liao & Cheung, 2001; Reibstein, 2002). Prior research found that
customers’ monetary factors, and value perception of Internet shopping influenced their decision
to purchase from an Internet vendor (Gupta & Kim, 2010). Additionally, researchers identified
price-conscious consumers are more inclined to shop online than those who are not concerned
with price (Bigné et al., 2010; Khare & Singh, 2012; Reibstein, 2002). Pappas et al. (2014)
found that the price value acted as a motivational factor for shopping online.
In addition to that, perceived value was found to be as an important predictor for
repurchase intention (Fang, Wen, George, & Prybutok, 2016). A prior study found that
consumers indeed perceived price as a stronger predictor and that online shoppers turned to the
Internet primarily for utilitarian reasons, such as price savings and convenience (Overby & Lee,
2006). Irani and Hanzaee (2011) reported that in apparel shopping, consumer buying tendencies
were found to be affected by customers price concerns. Researchers recommended retailers
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develop pricing strategies that can satisfy customers (Park, Kim, Funches, & Foxx, 2012).
Given the findings of the current study, apparel subscription service providers should ensure that
they are providing good money value to their subscribers for the products they are offering.
H7: Individuals who have a habit of purchasing apparel online will have a positive
intention to use an online apparel subscription service was not supported
Individuals’ online shopping habit construct was measured using terms like ‘I purchase
apparel online frequently’, and ‘buying apparel online has become natural to me’. Data from the
study revealed that an individuals’ habit of shopping online in determining the behavioral
intention and actual use of an online apparel subscription service was not supported. This result
of the study provided a somewhat different outcome from prior literature (Limayem et al., 2007;
Venkatesh et al., 2012). It was found that respondents from this study were not influenced by
habit in determining their behavioral intention as well as actual behavior in the online apparel
subscription service context. Therefore results of this study implied that individuals who
ordinarily did not purchase clothing online were now more likely to purchase online because of
the apparel subscription service. The innovative nature of the service is what may be attracting
these participants to do so.
Prior research has indicated that habit as well as affective involvement acted as a
significant determinant of intention to purchase online as well as usage of online financial
services (Bosnjak, Galesic, & Tuten, 2007; Hubert, Blut, Brock, Backhaus, & Eberhardt, 2017;
Soopramanien & Robertson, 2007; Yen & Wu, 2016). In the case of online apparel subscription
services, this was not the case for respondents for this study. Retailers should note that users
might be conscious in nature and made the decision to use the service based on the usefulness as
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well as perceived value of the service. In order to retain these consumers, retailers should
maintain high quality high service along with good value.
H8: Behavioral intention will significantly affect individual’s behavior of using an online
apparel subscription service was supported
As expected, the present study found that there was a significant relationship between
individuals’ behavioral intention and actual use of an online apparel subscription service. This
finding is in accordance with many prior studies (Gillenson & Sherrell, 2002; Lim et al., 2016;
Lin, 2007; Tao & Xu, 2018; Venkatesh et al.u, 2012; Yen, 2012). In Lin’s (2007) study,
behavioral intention was the primary direct determinant of actual usage for three different
models. Further, consumer acceptance and use of online shopping platform was reasonably
predicted from their intention (Gillenson & Sherrell, 2002). Lim et al. (2016) reported
individuals purchase intention significantly influenced their online shopping behavior. The
same type of finding was revealed in the case of online banking service acceptance (Yen, 2012)
as well as individuals’ acceptance of technology (Venkatesh et al., 2012).
As it has been observed in this study that individuals’ behavioral intention influenced
actual behavior, retailers should pay attention to develop consumers’ behavioral intention
towards their service. Data from this study indicated that, perceived usefulness, perceived ease
of use, good money value, effective curated service, and individual hedonic motivation all
significantly influenced behavioral intention. This suggests that, in order to influence
consumers behavioral intention, retailers should work on these micro level service aspects; they
should develop a useful service that is easy to use, provide a service with value for money spent,
and a service with a high level of customization.
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The data from this study also indicated that some of these variables directly influenced
actual behavior as well. For instance, it was observed that curated service, hedonic motivation
and price value had a significant relationship with the actual use of an online apparel service.
The results also suggest that a modified model to the one proposed may better explain online
apparel subscription users behaviors. This modified model, shown in Figure 4.2 had an adjusted
R2 value for behavioral intention and actual use that increased to 0.72 and 0.81 respectively,
which suggested the percentage of variance explained can get a boost if this modified model was
used to explain online apparel subscription service users behavior. These observations indicate
that, a modified model should be tested in future research. As can be seen from the Figure 4.2,
retailers should pay special attention to the curated service, hedonic aspect of subscription
service shopping and offer good money value in their service as these variables can directly
influence the actual behavior along with influencing the behavioral intention for such a service.
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Figure 4.2. Alternative model indicated by the data from present study
H9: Age will have moderating effect on relationships among predictors of intention to use
an online apparel subscription service was not supported
Considering the fact that young consumers perceive shopping online differently than
older ones, this study hypothesized that age would have moderating effects on the outcome
variables. However, in this study. the data revealed that age did not have any moderating effects
on the relationship between predictor and outcome variables as it did in many prior studies
(Brown et al., 2003; Citrin et al., 2002; Delafrooz et al., 2011; Fang et al., 2016); Garbarino &
Strabilevitz, 2004; Gong et al., 2013; Hernández et al., 2011; Rodgers & Harris, 2003; Susskind,
2004; Swaminathan et al. 1999; Venkatesh et al., 2012). This means, that for apparel
subscription services, both young and older populations likely will have the same shopping
motivation.
One possible reason could be most of the respondents (more than 75%) of present study
were young (35 years old or younger). This may hold the notion that it is mostly a younger
population that are the users of such services, which was previously reported by some
researchers (Chen, et al., 2018; Columbus 2018; Kestenbaum, 2018). Another aspect could be
that, users of such services could possess a similar behavioral pattern outlined in the research
study of Gupta et al. (2018). They suggested that individuals’ self-gifting and surprising
motivation influenced customers to use such services and they followed specific psychological
patterns.
However, age did not show any significant influences in some of the prior literature as
well (Chung, Park, Wang, Fulk, & McLaughlin, 2010; Hernández et al., 2011; Li et al., 1999;
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Rohm & Swaminathan 2004) which is consistent with the findings of the present study. Thus,
retailers of such services should take note that there appears to be no differences in behavioral
patterns of younger and older populations for such services. This information should help
retailers design the service uniformly for all customers rather than customizing it age-wise.
H10: Gender will have moderating effect on relationships among predictors of intention to
use an online apparel subscription service was not supported
As for the age demographic variable, another interesting finding in this study was that
gender too did not have any moderating effect on the relationship between predictor and
outcome variables as it did in many prior studies (Brown et al., 2003; Citrin et al., 2002;
Delafrooz et al.,
2011; Garbarino & Strabilevitz, 2004; Gong et al., 2013; Hernández et al., 2011; Rodgers &
Harris, 2003; Susskind, 2004; Swaminathan et al. 1999). This means for online apparel
subscription service behavior, both men and women possess similar cues of behavioral intention
in terms of usefulness, easiness of shopping mode, money value, hedonic motivation, curated
service and these variables relationship with behavioral intention did not vary based on gender
orientation. Retailers of such services might get benefit from such outcomes as they might not
need to formulate customized service for different genders. They can uniformly develop a
service platform that can work equally both for men and women.
One possible reason behind such a finding could be as was discussed earlier, users of
subscription services are more fashion conscious and fashion opinion leaders in nature. Prior
literature mentioned the underlying cause behind gender difference as the psychological
difference aspect of women, who feel more price conscious, seek more usefulness of the
platform and are motivated to shop for a specific product category (Al-Maghrabi et al., 2011;
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Davis et al., 2014; Van Slyke et al., 2002). In the case of online apparel subscription services,
there might not exist any gender difference. Also lifestyle, education level and income level for
such users could be same for both men and women as reported by Chen et al. (2018), and
Columbus (2018), which could lead to such an outcome. However, this also implies that users
for such services are from specific demographic groups that have similar behavioral patterns of
using the service and retailers may need to think about reaching more diverse populations which
in turn would increase the number of customers as well as to boost their revenues.
However, gender did not show any significant influences in some of the prior literature
(Li et al., 1999; Rohm & Swaminathan 2004) which is consistent with the findings of the present
study. Additionally, Sebastianelli, Tamimi and Rajan (2008) found no gender-based differences
in the frequency of online purchasing. In another research study using the extended UTAUT
model, researchers examined gender differences in the online purchasing behavior of consumers
who purchase digital and non-digital goods (Pascual-Miguel, Agudo-Peregrina, &
ChaparroPeláez, 2015). It was found that no significant gender differences appeared for
purchase intention in non-digital goods (Pascual-Miguel et al., 2015). This outcome also
aligned with the finding of present study regarding gender influence.
This chapter represents the proposed conceptual framework analysis reports related to
survey scale measures as well as PLS hypotheses testing results discussed in chapter 2 and 3.
The findings of the study were discussed as well. In the following chapter, implications of these
results are addressed. The chapter concludes with a discussion regarding the limitations of the
present study that will lead to some future research possibilities.
Chapter 5.
Conclusion
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In recent years, online apparel subscription services have gained in popularity. More
consumers are using this type of service and retailing industries are witnessing a new way of
catering to the fashion consumer using an innovative business model. Consumer attitudes
towards online fashion retailing and decision-making in purchasing clothing have been well
documented in many prior studies (Childers et al., 2001; Chiu et al., 2009; Gillenson & Sherrell,
2002; Ha & Stoel, 2009; Lim et al., 2016; Lin, 2007; Sohn, 2017; Yen & Wu, 2016). However,
consumers’ apparel subscription needs have not received much attention. In this study a
conceptual framework based on the UTAUT was proposed and evaluated to investigate
consumer use of such services. The results of this study indicated that U.S. consumers’
behavioral intentions and use towards online apparel subscription service are influenced by the
usefulness, ease of shopping, price value, curated service and consumer hedonic shopping
orientation. Further, it was found that age and gender do not play a role in the decision process
for such services. The theoretical and practical business implications significantly contributed to
understanding the consumers’ need for such services.
5.1. Contribution of this Study
This study systematically investigated the apparel consumers’ online subscription service
needs and reported the predictors for behavioral intention and use for such a service. As
discussed earlier in Chapter 2, very few researchers have addressed consumers’ online apparel
subscription service behavioral issues, thus little was known about consumers’ attitudes towards
such a service as well as their purchase behavior. This study collected responses from actual
users of apparel subscription services in the U.S. and has made significant contributions to both
academics and the retail industry.
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5.2. Theoretical Contributions
This study evaluated the determinants that influence a consumers’ online apparel
subscription service behavior and has proposed and tested a conceptual framework of such
service intentions in the U.S. context. The conceptual framework tested in this study is based on
the extended UTAUT that has been a well-established theory to explain an individual’s
technology acceptance and usage. This study demonstrated that the extended UTAUT can
empirically predict individual intention and behaviors for an online apparel subscription service.
It identified the variables that are important in influencing the behavioral intentions and use of
an online apparel subscription service. The hypothesis testing and model evaluation results
obtained in this study suggest that:
(i) Consumers’ behavioral intentions and use of online apparel subscription service
are significantly influenced by the perceived usefulness, perceived ease of use, price value,
curated service and hedonic motivation.
(ii) Social influence and individual online shopping habit do not influence behavioral
intentions and use of such services.
(iii) Demographic variables such as age and gender do not influence usage of such
services.
This study adds new knowledge to the growing research in the field of online apparel
consumer behavior and contributes to the current literature. It also has provided valuable
insights and understanding of online apparel consumers subscription service needs.
Additionally, it reports the consumers’ usage profile of such services.
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5.3. Business Implications
The outcomes of this research have valuable implications for the retail industry. The
study has identified important factors and their elements which influence consumers’ usage
behavior of online apparel subscription services. The results of the study imply that consumers’
behavioral intention and use for online apparel subscription service are largely determined by
the usefulness, user experiences, quality of curated service and the price value of the service.
These findings suggest that retailers who cater this type of service should pay attention to these
aspects to retain and attract more customers. The results of this study suggest that retailers
should design user friendly platforms to interact with existing and potential consumers, develop
a quality curated service that can satisfy consumers, and offer good money value to their
consumers to stay competitive. Also, consumers’ hedonic motivations were found to be another
influencing factor that implies retailers should incorporate the fun aspect of shopping into their
service to attract more customers. Moreover, this study suggests retailers should formulate new
business strategies to attract an older population, as this group seems to be using the service less.
5.4. Limitations and Future Research Directions
Although the present study contributed to the online consumer behavior body of
knowledge in the apparel subscription service context, there were some limitations of the study.
Firstly, due to time and financial constraints, this study was only able to collect a limited number
of responses (678 valid responses). The data was also primarily collected in the U.S. Secondly,
only quantitative data was gathered to understand consumers apparel subscription service needs
and wants. Qualitative or mixed method research designs with direct interviews as well as
openended responses could offer more insights. Further, as for consumers demographic
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characteristics, only age and gender were investigated, other variables such as occupation,
income, lifestyle, fashion orientation whether fashion leader or follower could offer more broad
knowledge. Also, the study used constructs as variables that are incorporated in the UTAUT,
other important variables such as website attributes, consumers’ brand attachment, loyalty
aspects could not be investigated in this study. Moreover, the study did not address the type of
fashion items that individuals would prefer to get using this type of service.
Additionally, the present study used the UTAUT scale to investigate online apparel
consumers behavioral intention and actual use. While the original UTAUT was devised to
understand consumers acceptance of technology, this study explored technology driven fashion
services. In this regard further research should be devoted to identify scales that can
appropriately measure perceived usefulness, perceived ease of shopping, hedonic motivation,
habit constructs in the fashion shopping context. Also this study did not test whether there exists
any significant relationship between the perceived usefulness and perceived ease of shopping
construct. Future study should investigate this phenomenon as well.
However, these limitations pave the way for future research studies that can investigate
more diverse population and generalize the findings of the present study. Other important
variables such as consumers lifestyle, income, occupation, fashion orientation could be
incorporated in future studies. As for research design, qualitative research with an open-ended
response capacity as well as a mixed method research approach with other theoretical
underpinnings could be formulated to gain more insights. Also, a case study approach could
help identify consumers’ cues and preferences for certain service providers. Additionally, this
research does not take into consideration the consumers’ loyalty aspect, which further research
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can address. More specifically how this type of service help builds consumer loyalty as well as
how loyalty influences consumers to use this type of service can be examined in future events.
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