FINAL REPORT - WHAT DRIVES COLLEGE STUDENTS TO SHOP ONLINE compressed

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What Drives College Students to Shop Online?
A Quantitative Pre-test
Luoning Xu
Boston University

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Table of Contents
INTRODUCTION 3
BACKGROUND SEARCH 4
THE CLIENT 4
THE COMPETITION 5
THE INDUSTRY 8
LITERATURE REVIEW 10
THEORETICAL FRAMEWORK 20
PROPOSED PREDICTORS 22
PROPOSED PREDICTORS FROM THE LITERATURE 22
PROPOSED PREDICTORS WITHIN THE THEORY OF PLANNED BEHAVIOR 27
DEVELOPMENT OF MEASURES 31
MULTI-ITEM MEASURES 31
SINGLE-ITEM MEASURES 40
DEVELOPMENT OF SURVERY INSTRUMENT 41
ANALYSIS OF MEASURES 49
QUALITATIVE ASSESSMENT 49
QUANTITATIVE ASSESSMENT 50
RIVISION OF SURVEY INSTRUMENT 79
CONCLUSION 80
REFERNCE 81
APPENDIX 87
APPENDIX 1 87
APPENDIX 2 88
APPENDIX 3 90

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The Introduction
Retail e-commerce, namely online shopping, is an important part of people’s life due to
its convenience and speed. People can browse from anywhere on any devices and click a
“place order” button, then in the next 1 to 5 days, they can expect the items to arrive on their
doorstep.
A 16% compound annual growth rate (CAGR) from 2010-14 shows that online sales are
shoving the retail industry towards a bigger market. They propelled 11 retailers to grow by
more than $500 million in 2014. However, many e-commerce giants are struggling to make
profits. Leading companies in the industry were reported profit losses for 2014 or in the first
quarter of 2015 (Mintel, 2015). E-commerce accounts for merely 7% of total retail sales
although it has demonstrated a significant increase from 2010 – 15. Yet in order to obtain
more in the $4.5+ trillion industry, top e-commerce companies need to figure out solutions to
multiple problems confronting them, which includes scattered channel approaches to clothing
retailing, high premiums for same day delivery of groceries, and an imperfect means of
duplicating the discovery process available in-store to brick-and-mortar shoppers (Mintel,
2015).
Therefore, maintaining and attracting college students, who are used to modern lifestyle,
is a significant strategy for e-commerce companies to make profits. This study analyzes what
elements of e-commerce drive college students to choose the online shopping mode instead
of a traditional one to help e-retailers optimize their strategies targeting on the huge college
student market.

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Background
The Client
Amazon.com is the leading internet retailer in the U.S, with millions of products across
a broad range of categories. Amazon.com launched its website selling books in 1994 in
Seattle, WA.; Then the company expand to sell all kinds of commodities: applicants,
electronics, personal care products, clothing, jewelry, accessories, and so on. Convenience in
placing order, diversity in inventory, minimum in shipping charge and ease in return,
Amazon.com marks itself No.1 popular e-retailer among Americans (Mintel, 2013), it attracts
approximately 65 million customers to its U.S. website per month (Miva, 2011). In 2003
Amazon first achieved its annual profit, 6 years after it went public (Leschly et al., 2003).
Amazon’s total North American sales grew from $26.7 billion in 2011 to $34.8 billion in
2012 and $44.5 billion in 2013, an increase of 27.8% relative to 2012 (Mintel, 2015).
Amazon.com offers subscription services such as Amazon Mom, Amazon Student, and
Amazon Prime (Mintel, 2013). Paying an annual fee of $99/year for Amazon Prime enables
consumer to receive free two-day shipping on all orders with no minimum order amount,
special incentives and promotions and special student discounts (for Amazon Prime Student
subscribers) and unlimited instant streaming of videos and television shows. Amazon offers
free 30-day trial of Amazon Prime for potential subscribers to test out such services (Mintel,
2013). For college students, Amazon provide a 6-month free trail of Amazon Prime service to
encourage them to depend on the service and become loyal subscribers. Students also get a
discount annual service fee of $49/year. Amazon is also the first to launch “review” section

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for a product that allows customers to express their opinions and refer to others’ comments
about the item (Leschly et al., 2003).
Although the Amazon’s headquarters is in the USA, it has a series of sites around the
world, including United Kingdom (amazon.co.uk), Canada (amazon.ca), France (amazon.fr),
Germany (amazon.de), Japan (amazon.jp), and China (z.cn). Amazon provided website
technology supports and operation for many other companies, such as Marks & Spencer,
Lacoste, the NBA, Bebe Stores, etc (Ecommerce land, 2008). Amazon.com also owns
affiliated online shopping sites targeting on customers that are more specific. For example,
myhabit.com is for those who love to buy discounted high-end brands’ apparels, shoes,
accessories, home essentials and others.
According to a recent back-to-school shopping report, Amazon.com was ranked No.3,
after Walmart and Target. Even though the latter two added in-store and online purchase
together to exceed Amazon, Amazon still need to take it serious since Walmart and Target’s
online orders fell only 8% and 18% behind it respectively (Mintel, 2013). Amazon’s strong
competitor eBay is also taking actions: it launched a “My feed” function, aiming at Amazon’s
personalization edge (Evans, 2013).
The competition
eBay.com
eBay.com is an online auction-based marketplace founded in 1995, and has been
performing well in online retailing (Mintel, 2013). eBay.com brings buyers and sellers
together in a consumer-to-consumer format (with some small business users as well).

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Merchants or sellers provide either new or used items and buyers can either bid on them or
purchase for “buy it now” prices. The site’s access to hard-to-find, discontinued items and
collectibles is popular among customers (Mintel, 2015). eBay was reported to have more than
100 million users internationally and its total sales in 2011 was $68.8 billion (Mintel, 2013).
eBay’s revenue grew 21% to $14.1 billion in 2012 and 14% to 16 billion in the next year
(Mintel, 2013).
eBay began to build technology partnerships with big retailers in 2008. eBay started to
sell products for companies like Home Depot, Macy’s, Toys “R” Us, and Target, expanding
eBay’s business to the level of reliable, returnable items at fixed prices. Auctions now
accounts for only 30% of total purchases made on eBay.com; as for fixed price items, the site
sells 13,000 cars a week through its mobile app alone. eBay also offers a same-day delivery
service with a fee of $5 per order, eBay Now, which allows customers to get what they want
on the same day of their purchase.
Just like Amazon, eBay provides e-commerce technology support and services to other
retailers by an affiliated company called GSI Commerce (Evans, 2013). eBay also owns other
businesses, including subsidiary websites such as StubHub, an online marketplace for tickets
(sporting events, concerts, etc.), Shopping.com (a site for comparison shopping), and
Half.com (used books, music, movies, and games), as well as secure online payment service,
PayPal (Mintel, 2013). PayPal was introduced to the market in 1998 and currently has
business in 190 markets. The service helps customers to process their payments online
securely, including online vendors, auction sites, other commercial users as well as
individuals. It offers 24 currencies choices for customers to send, receive and hold funds

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globally. PayPal currently has more than 100 million active users (Miva, 2011). This secure
online payment service enables eBay to keep their customer’s loyalty.
Walmart.com
Walmart is the largest company by revenue and the largest retailer globally. Based in
Bentonville, Ark., its US net sales reached $279 billion in fiscal year 2014. It had 10,900
distribution centers and more than 6,100 stores in 27 countries in 2014. It had 4,203 stores in
the US alone. Walmart group owns different kinds of stores such as discount stores,
supercenters, Walmart Neighborhood Markets, Sam’s Club, and operates online at
walmart.com (Mintel, 2015).
Walmart.com is an online extension of Walmart stores. It was founded in January 2000,
near Silicon Valley, where it was able to connect with the world’s best internet talents.
Walmart.com features a great selection of high-quality merchandise, friendly service and,
“Every Day Low Prices” (also a principle of the Walmart group). In some areas, it also offers
in-store pickup and just like eBay, it has same-day delivery services.
Walmart.com ranks in the top 5 among North American online retailers in 2011 (Mintel,
2013). According to Internet Retailer, Walmart.com’s online sales in 2013 increased more
than 30% year-on-year to more than $10 billion when its physical stores suffered a slight
decline.
Walmart invested a lot to develop its online performance. The company opened
@WalmartLabs to fully focus on e-commerce. Walmart also has been aggressively
developing and expanding its online platforms and expanding online selections. By March

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2013, Walmart.com’s selection grew substantially to about 2 million kinds of goods, 35-40%
more than in 2012, about 10 times of a typical Walmart store (Mintel, 2015).
Target.com
Founded in 1902 in Minneapolis, Target Corporation is the second largest mass
merchandiser in the US with 1,792 stores and 38 distribution centers. Target’s target is to
“make Target the preferred shopping destination for our guests by delivering outstanding
value, continuous innovation, and an exceptional guest experience by consistently fulfilling
our ‘Expect More. Pay Less.’ brand promise” (Target.com, 2016). It provides products across
a range of categories such as household, health care, beauty and personal care products,
clothing, footwear and accessories (Mintel, 2013).
Target launched target.com in 1999. In 2012, it released an award-winning mobile app
and in 2013, it offered a “order online and free pickup in store” service. In 2014, target.com’s
contribution to year on year sales change (also known as comparable sales change) was 0.7%,
when the company’s total comparable sales change was 1.3% (source from Target 2014
annual report).
The Industry
E-commerce, by definition, is buying and selling goods and products via the internet
(Rayport and Bernard, 2004).
The term “electronic commerce” was created to describe electronic data exchange for
sending business documents in the 1970s (Nanehkaran, 2013). However, what really made

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ecommerce possible is internet opening to commercial use in 1991. A great number of
companies began to build their business on the internet (Ecommerce land, 2008). The sheer
growth of the industry transformed the meaning of “electronic commerce” to refer to the
business of goods and services via the internet. Although founded by inexperienced
practitioners and unprofessional websites, the industry has been spreading rapidly in most
cities in America, Europe and East Asia in 2000s. The number of people around the world
who have access to the internet has been increasing evidently in the recent decade, leading to
the reform of the electronic commerce structure. A kind of business from specific business
case for a particular group was developed and became the industrial standard (Nanehkaran,
2013). In the next few years, ecommerce sales increased stably, and in 2007, it took up 3.4
percent of total sales (Ecommerce land, 2008).
While there are several types of e-commerce, here we mainly discuss two types. First is
the business-to-consumers (B2C) model, where organizations are the sellers and individuals
are the buyers. B2C is also the most discussed e-commerce type. Second, the consumer-to-
consumer (C2C) model, in which both the sellers and the buyers are consumers (Chan, 2002).
They can be called electronic retailing (e-tailing), which is selling products directly through
electronic storefronts or electronic malls. The sales are usually displayed in an electronic
catalog format and/or auctions.
Current statistics in e-commerce industry indicate that 40 percent of global internet users
have the experience of buying products or services online via desktop, mobile, tablet or other
online devices. It adds to more than 1 billion internet buyers, and the number will be
continuously increasing (Statista, 2015).

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In the late 20th
century, B2C and C2C e-commerce in the US grew in a rapid speed.
During this period, future giants such as Amazon.com and eBay.com laid their foundation
and physical stores such as Target and Walmart launched their online platforms to extend
their business from offline to online.
Amazon.com, as the dominant in online retailer sales in the U.S., accounted for a 23%
market share in 2014, exceeding its 12 other followers, including eBay, Target and Walmart
(The Motley Fool, 2015).
Literature Review
The preceding parts of this study has developed an overview of the client, Amazon.com,
its major competitors, eBay.com, Walmart.com and Target.com, the overall history and
development of e-commerce industry and the current market dividends in the industry. Given
this basic knowledge of the case, we will now take a look into existing literatures regarding
related topics from scholarly journals, trade journals, newspaper, magazines, and commercial
reports to depict a deeper understanding of the electronic commerce industry. This section is
particularly aimed at finding predictors that proved to be influential in motivating consumers
conducting online shopping from available literatures to help develop a diagram in guiding
the subsequent research process.
Predictors found in scholarly journals are the most valuable ones given the fact that they
are mostly gained from empirical experiments that were conducted under the principle of
scientific guidelines.

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As many studies claimed, demographics of a person is an influential variable that affects
consumers online purchase behavior (Burke, 2002; Wood, 2002; Sorce et al., 2003; Naseri
and Elliott, 2011). Among the broad range of demographic information, age, gender,
education level and income (Bellman et al., 1999; Burke, 2002; Naseri and Elliott, 2011), are
the most obvious elements that proved to have substantial impact on online shopping
intentions and behaviors.
Age is supported as an active element that influences consumer’s online buying
behavior (Bellman et al., 1999; Wood, 2002; Chang et al., 2005; Joines et al., 2003) Even
though age is disproved by Sorce et al. (2003) by concluding that concerning actual online
purchasing behavior, the younger ones are not buying more than the older, younger people
are searching products more on the internet (Wood, 2002; Sorce et al., 2003).Wood (2002)
confirms the idea by stating that, comparing to older citizens, young adults (epscially those
under 25) are more attracted to use new technologies, such as the internet, to search product
information, and to compare and assess similar items (Wood, 2002). In addition, categories of
product consumers would like to buy online are more age-related, in that older consumers
tend to buy more gardening tools and younger consumers more digital music products (Sorce
et al., 2003). Yet when the older consumers have searched products online, they are more
likely to purchase the commodity online than the younger consumers (Sorce et al., 2003).
Income, or more specifically, disposable income (Holstein et al., 1998; Case et al.,
2001), and gender (Greer and O’Kenner, 1999; Then and DeLong, 1999; Tweney, 1999) are
two other strong factors that are always positively associated with online purchase intentions
and behaviors.

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Education level, on the other hand, is important in predicting online purchase behavior
(Case et al., 2001; Mafé and Blas, 2006; Naseri and Elliott, 2011): the higher one person’s
education level is, the more positive his or her attitude towards innovations such as internet
and the more exposure of him or her to the internet, thus, it positively influences his or her
subsequent online buying behavior (Mafé and Blas, 2006).
Besides, employment status, as Xu and Paulins (2005) claimed in their study, is also a
powerful indicator particularly for students. Employed students are under greater time
pressure and have more money to spend than those who are not, which makes the
employment status a significant predictor on students’ online shopping intentions and
behavior.
Moreover, personality matters in making predictions about online purchase behavior.
People who enjoys hedonism is identified to be driven for conducting a certain kind of online
shopping – hedonistic online purchase (Cowart and Godsmith, 2007 ) Cowart and Godsmith
(2007) found hedonistic consumers spent significantly more time online purchasing.
Impulsiveness is another personality that is consider to be accountable for one of online
shopping drives (Phau and Lo, 2004). According to the research results of Phau and Lo
(2004), characteristics of impulsiveness is probably a part of online consumers’ psychology,
for instance, intimacy. Cowart and Godsmith (2007) confirms the statement by finding that
impulsive shoppers spend more money and time online. Goal-orientation is another
highlighted characteristic that affects consumer’s online purchase behavior. With this
personality, consumers are favoring online retailing for four specific attributes: convenience

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and accessibility; selection; availability of information; and lack of sociality (Wolfinbarg and
Gilly, 2001).
Services provided by online retailers are proved influential indicators that will
substantially affect consumer’s online shopping attitude, intentions, and behavior.
Return policy is a major concern that may significantly influences consumer’s online
shopping intentions and behavior (Siddiqui et al., 2003; Xu and Paulins, 2005; Lester et al.,
2005). Whether free or not, accept mail return only or items can be returned to a physical
store, will refund be transact to the original payment or only store credit will be issued after
return, all influence consumers online buying intentions and behavior.
Delivery efficiency and cost could be crucial in online shopping behavior prediction. As
Lester et al. (2005) stated in their study, long delivery time of merchandise and high shipping
costs could be the most important disadvantage of online retailers.
Website experience is believed to be a prevalent effective variable, of which includes
online functionality, information, and related services (Constantinides, 2004).
The amount, quality and expression of information on the website, would largely affect
consumer’s perception of the website and the product (Sorce et al., 2005). The effectiveness
that consumers can extract useful information from the information sea drives them to use
more online shopping approaches (Constantinides, (2004)). Aside from that, the inexpensive
information acquisition tactic through the internet is also an encouraging element motivating
consumers to choose purchasing online rather than in physical stores (Constantinides, 2004).
Moreover, some websites satisfy information’s interaction control of consumers by offering
detailed description and pictures of products and providing online customer service chat box

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that allows consumers to get real time responses and support from a representative, both
before and after purchase, which is also a stimulating variable (Bulter and Peppard, 1998;
Siddiqui et al., 2003).
As the first and most direct impression to consumers, decent visual designs positively
affect consumers’ online purchase behavior (Siddiqui et al., 2003); It can be broken down
into two aspects: 1) the design of the webpage, which is the reflection of artistic factors in
online visualization and marketing mix, and 2) the display of the product (virtual product
affordance). Especially for the latter one, interactive 3-D visualization enables consumers to
learn better about the product been demonstrated, hence generates positive brand attitude and
purchase intentions towards the item (Li et al., 2003).
In addition to above predictors, trust is a critical issue associated with the success of a
online merchant. A good deal of studies identified that building trust or confidence is the key
to success (Lee, 2002; Liebermann and Stashevsky, 2002; McKnight et al., 2002; Suh and
Han, 2002; Liang and Lai, 2002; Jadhav and Khanna, 2016) Consumers’ familiarity of the
online retailer will build consumer’s trust on the online retailer, but is not the primary
influence on people’s online purchase behavior.
Self-efficacy is another factor that is recognized as a powerful indicator for internet
shopping intentions by many researchers (Wang et al., 2006; Lin, 2008; Mandilasa et al.
2013).
Previous experience is another factor that is reported to have massive influence on
predicting online purchase behavior (Foucault and Scheufele 2002; Crespo-Almendros, and
Del Barrio-García, 2015). One person’s previous online buying experience or even online

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experience will influence his or her decision on following online purchase behavior. In their
study Crespo-Almendros, and Del Barrio-García (2015) suggests, comparing with new online
shopping users, experienced users have a greater purchase intention.
Price sensitivity is found to be a major motive behind consumers making internet
purchases (Joines et al., 2003; Jadhav and Khanna, 2016). Consumers shop online in that
online retailers offer better prices than other channels, they shop online to save money (Joines
et al., 2003) Similarly, promotional incentives including online discounts and gifts is
identified to be a important factor, affecting consumers online purchase intentions in a
positive way (Crespo-Almendros and Del Barrio-García, 2015; Jadhav and Khanna, 2016).
Time saving, or speed, and convenience are always suggested primary reason of
consumers’ online purchase behavior (Jadhav and Khanna, 2016; Cowart and Godsmith,
2007; Constantinides, 2004; Makhitha, 2014). To customers, convenience means easy and
fast in gathering information, purchasing and settling of online transaction (Constantinides,
2004). The more convenient the online shopping context is, the more and the stronger online
buying intentions and behaviors it will embrace. However, time could influence consumer’s
online shopping intentions or behavior in another way. When concerning urgent purchase
necessity, consumers may choose go to physical stores instead of online shopping if the
online retailer does not provide pick up in store services (Then and DeLong, 1999).
In addition, cognition saving is also a influential factor in predicting online purchase
behavior. Consumers tend to control the brainwork they spend in acquiring information,
comparing prices, and making decisions so that to ease their online purchase actions.

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Consumers want to avoid the situation where they have to process a great amount of complex
information and make a decision (Then and DeLong, 1999).
A series of perceptions of online shopping, also known as online shopping’s advantages
over purchasing at brick-and-mortar stores, perceived usefulness, perceived ease of use,
perceived enjoyment, are agreed by Godsmith and Bridges(2001), Monsuwé et al. (2004),
Faqih (2008), Mandilasa et al. (2013), and Jadhav and Khanna’s (2016) studies as influential
predictors of attitudes towards online shopping, and therefore influence online shopping
intentions. Monsuwé et al. (2008) developed a framework tailored from the construct of
Technology Acceptance Model (TAM) by Davis (1989) to display the relationship among a
series of perceptions and online shopping attitude that is listed below. Among these merits,
perceived usefulness of the product was found to be the most important factor that affects on
attitudes toward online purchase and online shopping intention (Monsuwé et al., 2008;
Mandilasa et al., 2013). The study of Monsuwé et al. (2008) assigned two factors to describe
usefulness, Consumer return on investment (CROI) and service excellence. Perceived easy of
use is also an influential element in predicting attitude towards online shopping, it can be
broken down to experience, control, computer playfulness and computer anxiety (Monsuwé
et al., 2008; Mandilasa et al., 2013). But according to Monsuwé et al.’s (2008) finding, it is
not as strong as perceived usefulness. As for perceived enjoyment, it is positively associated
with online buying intentions. Based on Monsuwé et al.’s (2008) structure, it can be
described by escapism, pleasure and arousal.

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The framework is particularly adapted to grasp the process of consumer shaping attitude
towards online shopping and their intentions to shopping online later. Technology Adoption
Theory identifies two determinants that influences consumer shaping attitude towards a new
technology, which are usefulness and ease of use. A more recent research done by Davis et
al. (1992) included enjoyment to better explain the situation. In later research practices, Faqih
(2008) added the word “perceived” before every determinant to make it more sensible.
Source: Monsuwé et al. (2008)
Aside from above advantages, better selection provided by online retailers in general is
another influential determinant (Wolfinbarger and Gilly, 2011; Jadhav and Khanna, 2016).
First, online retailing’s characteristic of being able to overcome geography limit is proved to
positively associated with online buying intentions and behavior (Wolfinbarger and Gilly,
2011). Online shopping’s ability of being a potential source of inventory that is out of stock

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in or being taken off from physical stores also affect consumer’s online purchase intentions
and behavior (Wolfinbarger and Gilly, 2011). Regarding selection, certain product, such as
digital music, would only be available online, thus drives consumers to generate online
purchase intentions and behavior (Burtler and Peppard, 1998).
Social environment, according to Joines et al. (2003) and Foucault and Scheufele’s
(2002) studies, would have a great impact on consumer’s online purchase behavior. Foucault
and Scheufele’s (2002) college-based research confirmed the influence of friends, peers and
superiors would affect consumer’s online purchase behavior significantly: friends’
recommendations to buy textbooks online and professors’ positive attitude towards buying
textbooks online will lead to a large percentage of the respondents conducting online
textbook purchasing actions.
Brand loyalty to a certain online retailer or to a brand significantly affect consumer’s
online purchase intention and behavior (Bandyopadhyay and Martell, 2007). Especially when
the consumers have a strong positive attitude towards the brand, they might turn it into
purchase intention or actually buying behavior (Bandyopadhyay and Martell, 2007).
Fashion consciousness could also be a strong stimuli of people’s online purchase
intention and behavior (Xu and Paulins, 2005; Cowart and Godsmith, 2007). According to
the study of Cowart and Godsmith (2007), consumers, particularly college students, when
involve in online retailing, primarily purchase fashion products.
Besides, internet proficiency is also a significant dominant in predicting consumer’s
intention and behavior to shop online (Case et al., 2001). In Case et al.’s 2001 studies among
425 undergraduate and MBA students in the U.S. colleges from the perspective of customer

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relationship management, the researchers stated the more familiar the students are with
internet, the more positive their online purchase intentions and behavior are. Similarly,
computer knowledge is another highly effective determinant in predicting consumer’s online
shopping intentions and behavior (Case et al., 2001).
Many studies support the idea that internet and devices accessibility strongly affects
people’s online purchase behavior (Xu and Paulins, 2005; Makhitha, 2014; Jadhav and
Khanna, 2016). Internet usage is proved to significantly influence consumer’s online
shopping behavior in a positive direction (Xu and Paulins, 2005; Mandilasa et al., 2013),
especially for students (Xu and Paulins, 2005). The longer time the student spends in and the
more frequent he or she is browsing online, the more positive effect there is on his or her
online purchase intentions and behavior (Xu and Paulins, 2005).
Car accessibility of consumers, especially of college students, would largely affect them
on their online shopping intentions and (Xu and Paulins, 2005). In the 2005 study, Xu and
Paulins confirms that as an alternative of in-store shopping, online shopping is easily
influenced by store accessibility. Therefore, students who do not have access to cars tend to
hold more favorable attitude towards online shopping.
While some research indicates privacy concerns and security of payment have little
influence on people’s online purchase intentions, they are found by Joines et al., (2003) and
Mandilasa et al. (2013) to be major elements inhibiting online buying intentions and
behaviors.

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Theoretical framework
The previous sections have conducted a complete literature review in existing researches
of consumer’s online shopping attitude, intentions, and behavior; A number of predictors are
identified in the literature review as influencing factors in motivating consumers to shop
online. Given the fact that all of these valuable predictors come from researches conducted in
different contexts with different guidelines, a theoretical framework is needed as a principle
to organize the variables in a taxonomy that logically makes sense and provide other more
indicators in case they are missed in the literature review. The theoretical framework in
Theory of Planned Behavior (TPB) developed by Icek Ajzen (1991, 2006) will be utilized to
guide this research project.
It is clear that some of the above variables identified in the literature review, such as
information quality in the web experience part and delivery efficiency in customer services
part are easy to understand. However, it is also obvious that other, or a large number of
indicators mentioned in the literature review is not so simple for everyone to grasp. For
example, factors fall in consumer’s decision-making process are those too complicated to
fully comprehend. Therefore, it is important to introduce the framework from the Theory of
Planned Behavior (Ajzen, 1991, 2006) to help explain the complex process. The renowned
TPB framework believes attitude will influence intention, and therefore has an impact on
behavior (Ajzen, 1991, 2006). Although the TPB diagram is not specifically developed for
predicting consumer’s online shopping behavior, and have been widely used in different
industries, a great number of indicators found in the above literature review can be fitted into
the TPB framework.

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The TPB claims that to achieve a behavior, people should be motivated by their
intentions (Ajzen, 1991, 2006). The intention to take a certain action, however, is influenced
by three factors: attitude towards the behavior, subjective norm, and perceived control
(Ajzen, 1991, 2006). Note that the attitude here should be attitude towards the specific
behavior being measured to make a precise prediction. The attitude, in turn, is influenced by
consumer’s behavior beliefs. In addition, the subjective norms are another category of
indicators that are important in prediction intentions. Researchers and scholars usually use the
word subjective norms to indicate perceived social pressure on the decision of taking or not
taking certain action (Ajzen, 1991). Normative beliefs have an impact on subjective norms.
The last predictor is perceived behavior control, which refers to people’s prediction of their
ability to conduct the behavior. According to Ajzen (1991), people’s perceived behavior
control will be affected by their control beliefs. Combination of the three factors will exert
the intention to perform the behavior in question (Ajzen, 1991, 2006). In general, the more
positive people’s attitude toward the behavior, the more subjective norms and the less
perceived behavioral control, the stronger people’s intention is, and therefore the higher the
possibility of the final achievement of the behavior is (Ajzen, 1991, 2006). A graphic
demonstration of the theoretical model is shown as in Figure 2.

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Source: Ajzen (2002)
Notice that aside from intention, actual behavioral control could also be a variable that
influences people’s behavior as indicated in the model. That being acknowledged, actual
behavioral control may be different from perceived behavioral control. That is, regardless of
the intent to execute the behavior, people might not be able to complete the action as they
previously perceived due to some external interference. The TPB also acknowledge that
Proposed predictors from the literature
From the literature review above, a good deal of indictors has been found to predict
consumer’s online shopping drives. These potential predictors are listed below. For readers’
convenience, these factors are divided to five groups: pricing and promotional incentives,
services attributes, customer service, e-retailer’s image and recommendations, and customer
traits. These variables together influence consumers online shopping attitude, intention, and
behavior.
Pricing and Promotional Incentives

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Price sensitivity
Source: Jadhav and Khanna, 2016; Joines et al., 2003
Promotional incentives
Source: Crespo-Almendros and Del Barrio-García, 2015; Jadhav and Khanna, 2016
Service Attributes
Amount of information on website
Source: Constantinides, 2004
Quality of information on website
Expression of information on website
Visual design of the website
Source: Siddiqui et al., 2003
Visualization of product
Source: Li et al., 2003
Privacy concerns
Source: Joines et al., 2003; Mandilasa et al., 2013
Payment security
Source: Joines et al., 2003; Mandilasa et al., 2013
Perceived time saving

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Source: Constantinides, 2004; Cowart and Godsmith, 2007; Makhitha, 2014; Jadhav and
Khanna, 2016
Perceived convenience
Source: Constantinides, 2004; Cowart and Godsmith, 2007; Makhitha, 2014; Jadhav and
Khanna, 2016
Perceived inexpensive of information acquisition
Source: Joines et al., 2003
Perceived wide selection range
Source: Wolfinbarger and Gilly, 2011; Jadhav and Khanna, 2016
Perceived complete stock
Source: Wolfinbarger and Gilly, 2011
Perceived cognition saving
Source: Then and DeLong, 1999
Perceived usefulness
Source: Godsmith and Bridges, 2001; Monsuwé et al., 2004; Faqih, 2008; Mandilasa et al.,
2013; and Jadhav and Khanna, 2016
Perceived ease of use
Perceived enjoyment

25
Certain type of product only available online
Source: Burtler and Peppard, 1998
Customer Service
Return policy
Source: Siddiqui et al., 2003; Xu and Paulins, 2005; Lester et al., 2005
Delivery efficiency
Source: Lester et al. 2005
Perceived in time interaction customer services
Source: Joines et al., 2003
E-retailers Image and Recommendations
Recommendation by professors
Source: Foucault and Scheufele’s (2002)
Recommendation by friends
Source: Foucault and Scheufele’s (2002); Joines et al. (2003)
Trust in online retailer
Source: Lee, 2002; Liebermann and Stashevsky, 2002; McKnight et al., 2002; Suh and Han,
2002; Liang and Lai, 2002; Jadhav and Khanna, 2016)
Brand loyalty
Source: Bandyopadhyay and Martell, 2007

26
Customer Traits
Consumer’s age
Source: Bellman et al., 1999; Wood, 2002; Joines et al., 2003; Chang et al., 2005
Consumer’s gender
Source: Greer and O’Kenner, 1999; Then and DeLong, 1999; Tweney, 1999
Consumer’s income
Source: Holstein et al., 1998; Case et al., 2001
Consumer’s education level
Source: Case et al., 2001; Mafé and Blas, 2006; Naseri and Elliott, 2011
Consumer’s employment status
Source: Xu and Paulins, 2005
Consumer’s hedonism personality
Source: Cowart and Godsmith, 2007
Consumer’s impulsiveness personality
Source: Phau and Lo, 2004
Consumer’s goal-orientation personality
Source: Wolfinbarg and Gilly, 2001
Previous online experience
Source: Foucault and Scheufele 2002; Crespo-Almendros, and Del Barrio-García, 2015
Previous online shopping experience
Source: Foucault and Scheufele 2002; Crespo-Almendros, and Del Barrio-García, 2015
Fashion consciousness

27
Source: Xu and Paulins, 2005; Cowart and Godsmith, 2007
Car accessibility
Source: Xu and Paulins, 2005
Internet proficiency
Source: Case et al., 2001
Computer proficiency
Source: Case et al., 2001
Internet access
Source: Xu and Paulins, 2005; Makhitha, 2014; Jadhav and Khanna, 2016
Device access
Source: Xu and Paulins, 2005; Makhitha, 2014; Jadhav and Khanna, 2016
Internet usage
Source: Xu and Paulins, 2005; Mandilasa et al., 2013
Proposed predictors within the theory of planned behavior
1) Attitude toward behavior
Trust in online retailer
Brand loyalty
Fashion consciousness
2) Behavioral Belief Antecedents
Previous online experience

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Previous online shopping experience
3) Behavioral Beliefs
Perceived time saving
Perceived urgency of product
Perceived convenience
Perceived inexpensive information sources
Perceived interaction customer services
Perceived in time customer services
Perceived wide selection range
Perceived complete stock
Perceived cognition saving
Perceived usefulness
Perceived ease of use
Perceived enjoyment
Certain type of product only available online
4) Subjective Norms
5) Normative Beliefs
Recommendation by professors
Recommendation by friends

29
6) Perceived Behavioral Control
Internet proficiency
Computer proficiency
Internet access
Device access
Internet usage
Amount of information on website
Quality of information on website
Expression of information on website
Visual design of the website
Visualization of product
Privacy concerns
Payment security
Return policy
Delivery efficiency
Price sensitivity
Promotional incentives
Income
Employment status
Car accessibility

30
7) Control Beliefs
hedonistic personality
impulsiveness personality
goal-oriented personality

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Development of Measures
We aim at developing a set of valid and reliable measures that can help identify
factors affect students’ intention to shop with a particular online retailer. To make this survey
as precise as possible, the researcher enclosed multiple item measures for the 15 constructs
identified from the Theory of Planned Behavior (Ajzen, 1991) and Marketing Scales
Handbook (Bruner, 2009) as independent variables, as well as several single item measures
for measuring the dependent variable, the likelihood to shop online and the likelihood to
choose the client (Amazon.com) if they are going to buy a product. Questions about
demographic information are also using single item measure as the ending part of the survey.
Multi-item Measures
Multi-item measures are used for all 15 factors in this pre-test. Items are chosen from
the practical Marketing Scales Handbook (Bruner, 2009) within the theoretical frame of
Theory of Planned Behavior (Ajzen, 1991).
Selected 15 constructs and their predictors are listed by category in the tables below.
1) Attitude toward behavior

38
The researcher created a table for readers to refer to in a clearer view.

40
Single-item Measures
Aside from multiple item measures mentioned above, the researcher used single-item
measures to capture the dependent variables and demographic information of respondents as
shown below.
How likely are you to spread positive word of mouth about the specific online retailer?
__Extremely likely __Likely __Neither likely nor unlikely __Unlikely __Extremely unlikely
How old are you? _________________
Are you male or female?
__Male __Female
Which of the following describes your current academic level?
__Freshman __Sophomore __Junior __Senior __Graduate student

41
Development of Survey Instrument
To design a successful self-report survey questionnaire, we need to combine above
measures in an organized way. The questionnaire follows general-to-specific path to prevent
respondents being prompted and biased, and is thus divided to six sections: general ideas
about cognition, questions about computer and internet, general ideas about shopping,
general ideas about shopping online, questions about specific online retailers and questions
about demographics.
The complete survey questionnaire is shown below:
Student Opinion Survey
We are conducting this study to learn about students’ opinions concerning a variety of
current topics. Thank you for taking the time to complete our survey. Your responses are
anonymous.
Our first questions are general ideas. For each of the following statements, please
tell us how well it describes you by checking the box corresponding with your choice.
Strongly
disagree
Disagree Neutral Agree
Strongly
agree
I would rather do something that requires
little thought than something that is sure to
challenge my thinking abilities.
I like tasks that require little thought once I
have learned them.
I only think as hard as I have to.
I try to anticipate and avoid situations where
there is a likely chance I will have to think in
depth about something.

42
The next questions are about computers and the internet (including using multiple
devices to get online). For each of the following statements, please tell us how much you
agree or disagree with each of the following statements by checking the box corresponding
with your choice.
Strongly
disagree
Strongly
agree
The computer is as essential in my
home as is any other household
appliance.
It would be difficult to imagine life
without a computer
The computer has saved me time.
The computer has become part of my
daily routine.
Compared with most people, I think I
spend a lot of time on the internet.
Outside of the time I spend with e-
mail, I consider myself to be a “heavy
user” of the internet.
In a typical week, I visit dozens of
sites.
The next questions are about shopping. For each of the following statements, please
tell us how much you agree or disagree with each of the following statements by checking the
box corresponding with your choice.
Strongly
disagree
Strongly
agree
I often buy things spontaneously.
“Just do it” describes the way I buy things.
“Buy now, think about it later” describes
me.
Sometimes I feel like buying things on the
spur of the moment.
I buy things according to how I feel at the
moment.
I carefully plan most of my purchases.

43
Sometimes I am a bit reckless about what I
buy.
Shopping is generally a lot of fun for me.
I consider shopping a big hassle.
I often visit stores just for something to do,
rather than to buy something specific.
I take my time when shopping for even
small items such as toothpaste.
The next questions are about shopping online. For each of the following statements,
please tell us how much you agree or disagree with each of the following statements by
checking the box corresponding with your choice.
Strongly
disagree
Strongly
agree
Using the internet to shop challenges me.
Shop online is more convenient than shop
in a traditional retail store.
Using the internet to shop provides a good
test of my skills.
Shop online saves more time than shop in a
traditional retail store.
I found that using the internet to shop
stretched my capabilities to my limits.
When shopping online, it is easier to
browse items than shopping in a traditional
retail store.
Which online retailer you go most to shop?
__Walmart.com __Amazon.com __Target.com __eBay.com __Other, please
specify_______
Please keep the above answer in mind to finish the rest of the questionnaire.

44
The next questions are about this particular online retailer you choose. For each of the
following statements, please tell us how much you agree or disagree with each of the
following statements by checking the box corresponding with your choice.
Strongly
disagree
Strongly
agree
This specific online retailer
delivers what it promises.
This online retailer’s product
claims are believable.
Over time, my experiences with
this online retailer have led me to
expect it to keep its promises, no
more and no less.
This online retailer has a name
you can trust.
This online retailer doesn’t
pretend to be something it isn’t.
This online retailer is presenting
uncluttered screens.
This online retailer is presenting
information fast.
For each of the following statements, please tell us how much you agree or disagree
with each of the following statements by checking the box corresponding with your choice.
Strongly
disagree
Strongly
agree
The online retailer’s website
provides in-depth information.
The online retailer’s site doesn’t
waste my time.
It is quick and easy to complete a
transaction at this online
retailer’s website.
The level of personalization at
this online retailer’s site is about
right, not too much or too little.

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This online retailer’s website has
good selection.
The online retailer is willing and
ready to respond to customer
needs.
When you have a problem, the
online retailer’s website shows a
sincere interest in solving it.
Inquiries are answered promptly.
Strongly
disagree
Strongly
agree
This online retailer’s website
provides a "one-stop shop" for
my shopping.
that I have just visited is easy to
use.
This website does not satisfy a
majority of my online shopping
needs.
It is easy to interact with the
online retailer that I have just
visited.
The choice of products at this
website is limited.
It is easy to become skillful at
using the online retailer’s
website I have just visited.
This website does not carry a
wide selection of products to
choose from.
Learning to operate the online
retailer’s website I have just
visited is easy.

46
My interaction with the online
retailer’s website I have just
visited is clear and
understandable.
that I have just visited is
flexible to interact with.
Strongly
disagree
Strongly
agree
If a certain product was not available at
the online retailer, it would make little
difference to me if I had to choose
another retailer.
I consider myself to be highly loyal to a
specific online retailer.
When another retailer is on sale, I will
generally purchase it rather than the one
I usually choose to buy things.
I feel like my privacy is protected at this
online retailer’s site.
I feel safe in my transactions with this
online retailer’s website.
The online retailer’s website has
adequate security features.
The online retailer has practices that
make returning items quick and easy.
The online retailer shows as much
concern for customers returning items as
for those shopping for new ones.
The online retailer takes care of product
exchanges and returns promptly.
The return policies laid out in this
website are customer friendly.

47
Strongly
disagree
Strongly
agree
In the future, I intend to use online
shopping websites for purchases.
If you were trying to buy a product tomorrow (available online and offline) how likely would
you be to shop online?
If you were to shop a product online tomorrow, how likely are you going to buy on:
Extremely
likely
Likely
Neither likely
nor unlikely
Unlikely
Extremely
unlikely
eBay.com
Walmart.com
Target.com
Amazon.com
How old are you? _________________
__Male __Female

48
Thanks for your time!
The researcher sent out 120 copies of questionnaires randomly at Boston University
George Sherman Union on several week days and used 100 useful questionnaires in data
analysis. By useful questionnaires, here we refer to questionnaires that are completed or only
missed one or two questions.

49
Analysis of Measures
To make sure that all the measures are capturing what they ought to capture precisely
and completely, the researched conducted a confirmative process following “the
measurement model” mentally and in SPSS.
Qualitative Assessment
From qualitative perspective, the researcher checked carefully that items within one
construct are of enough amount of overlap in meaning. Since the measures come from
Marketing Scale Handbook (Bruner, 2009) that have already been proved to be overlapped
with each other, the researcher double checked the level to which they overlap in meaning in
the same construct and found these measures within one factor are qualitatively capturing the
concept well.

50
Quantitative Assessment
Since the predictors we are using are all multi-item predictors, before concluding that
they are useful to analyze for influences on the dependent variable (intention to shop online),
we have to test the validity and reliability of the measures.
We first want to make sure that there are no outliers that will lead errors to the data
previous to testing the validity and reliability of the factors. In this case, the outliers should
be items that do not have enough samples, have concentrations in results, or have wrong data
entries or are in the different direction with rest part of scales.
We then ran a frequency analysis to test if there was enough variation allowing us to
adequately capture the respondent's’ mind as well as enough samples allowing us to make
meaningful predictions. If the results concentrated in one or two choices (for example,
“strongly disagree”), or if the sample size is lower than 90, then that data would not be used.
The data here are in a five-point measurement scale where the results were interval data
ranging from 1 to 5. The results of the frequency analysis are attached in appendix 1. Note
that items considered to be negative have already been recoded reversely into the other side
of direction to be used in frequency analysis. According to the results, all selected questions
have close to 100 valid samples and there is no concentration in results. Therefore, we
continued to the next step.
Validity Test
In order to make sure the items are actually measuring the same thing, the researcher
ran an inter-item correlations test to quantify the overlap among all the items under the 15

51
constructs. The purpose of doing that is to see: 1) if there is enough amount of overlap
between or among items within one group, and 2) if there is too much overlap between or
among items that are not in the same factor. These two conditions will both cause errors in
the survey design. If there is not enough overlap between two items in one group, we
consider that they are not measuring the same concept and possibly need to be removed; if
there is too much overlap between two items in different concepts, we consider that there are
errors and likely need to be taken out. We first analyzed their overlaps qualitatively based on
the meaning of the items and found them well related to each other under one construct. Then
the researcher tested all items in SPSS in the same correlation analysis as the table attached in
appendix 2.
According to the results of correlation test, we found several red flags raising that
need to be noticed.
Here are the results of inter-item correlation test within each construct, and red flags
that need to pay attention to.
Construct 1

52
In “Need for cognition” construct as shown in above table, “I like tasks that require
little thought once I have learned them” and “I only think as hard as I have to.” is of moderate
overlap mathematically with two of three rest items (.36, .25, and .38, respectively).
Therefore, we are going to keep an eye on them.
Construct 2

53
In “dependency of computer” construct, the item “The computer is as essential in my
home as is any other household appliance” is of lower overlap with the other three items
within the same construct, but still strong.
Construct 3
In “usage of internet” construct, “In a typical week, I visit dozens of sites” is of low
overlap with “compared with most people, I think I spend a lot if time on the internet” and

54
“Outside of the time I spend with e-mail, I consider myself to be a ‘heavy user’ of the
internet”. For this construct, it is also to be noticed that the measures are more likely to be
measures for dependent variables, therefore if the construct is good to use still needs to be
decided regarding the factor analysis results.
Construct 4
In “attitude towards shopping” construct, “Sometimes I am a bit reckless about what
I buy”, “I often visit stores just for something to do, rather than to buy something specific”
and “I take my time when shopping for even small items such as toothpaste” has small
overlap with all other variables within the same concept; “I carefully plan my purchase” and

55
“I consider shopping a big hassle” have weaker correlation mathematically with other
variables within the same construct.
Construct 5
The results of inter-item correlation for the construct of “ability to shop online”
indicate that variable “Using the internet to shop challenges me” has small correlation with
the other two predictors within the same construct (r=.27 and .33, respectively) while the
other two items are strongly correlated (r=.60).
Construct 6
From the inter-item correlation test of construct “Perceived advantages of online
shopping”, we can see that the three items of moderate correlation with each other closely
from the perspective of mathematics (r=.35, .37, and .39, respectively).

56
Construct 7
Within Construct 7, items are strongly correlated with each other (all correlation
values are over .50).
Construct 8
Within construct 8, the two predictors are correlated at a high level (r=.52).
Construct 9

57
For “Perceived website experiences” construct, correlation test results suggest that
except for item “it is quick and easy to complete a transaction at this online retailer’s
website” and “This online retailer’s website has good selection” has a strong correlation
(r=.56), other correlation values are moderate mathematically.
Construct 10
Within construct 10, measures are strongly correlated with each other (r=.58, .48, and
.82, respectively).
Construct 11

58
For “Perceived product assortment”, except for “This online retailer’s website
provides a ‘one-stop shop’ for my shopping” is of low correlations with other three items
while rest three items are all correlated with each other at a strong level.
Construct 12
The results of inter-item correlation suggest that for items under “Perceived ease of
use” construct, “The online retailer’s website that I have just visited is easy to use” and “it is
easy to interact with the online retailer that I have just visited” is of relatively lower
correlations with other items.

59
Construct 13
According to the result of the correlation test, except the value of correlation of item
“If a certain product was not available at the online retailer, it would make little difference to
me if I had to choose another retailer” and “when another retailer is on sale, I will generally
purchase it rather than the one I usually choose to buy things” is of a moderate level, other
correlations under “Consumer loyalty” construct are negligible (.05, and -.02, respectively).
Construct 14
Within Construct 14, all three items are strongly correlated with each other (r=.72, .64
and .71, respectively).

60
Construct 15
Measures in Construct 15 shows a strong correlation with each other (r= .65, .65, .59,
.66, .72, .72 respectively).
Except for red flags within each factor, there are still some other problems that need
to be noticed at this stage. According to the results of the correlation test, some variables are
correlated at a high level with items from different constructs as shown in the following table:

61
The researcher decided to proceed to next step with the red flags found in the inter-
item correlation test in mind, and consider results from factor analysis and reliability test as
well as check meanings of items qualitatively again to determine if any action, such as
removing items or constructs out of the measurements, combining constructs and etc., will be
done to adjust the grouping solution.
Factor Analysis
To test if the proposed groupings are the best solution to capture the ideas we want to
know for the survey, we then ran a series of factor analyses among the 15 constructs. Since
the sample size is 100, we could only conduct factor analysis with four factors at a time
maximum.
Since there are 15 constructs given by the theoretical framework, we decided to test
them starting with two factors, and then add one every time after we fixed any problems in
the previous grouping solution.

62
Step 1
The first factor analysis adopted two fixed factors “Need for recognition” and
“Dependency on computer”, which are shown below:
According to the results, the explained variance is 64.31% (>50%). We found 1_1,
1_2, 1_3, and 1_4 fall into one group and 2_1, 2_2, 2_3, and 2_4 fall into the other group.
Step 2
Next we added three variables from construct “Internet usage” to our second factor
analysis, and made three groupings.

63
According to the results, the explained variance is 66.53% (>50%). We found 1_1,
1_2, 1_3, and 1_4 fall into one group, 2_1, 2_2, 2_3, and 2_4 fall into one group and 2_5,
2_6, and 2_7 fall into the other group.
Step 3
We then added 11 items under the construct “Attitude towards shopping” to the factor
analysis for four factors.

64
Based on the results, where the explained variance went down to 56.90% (>50%), we
found that construct 2 and 3 were combined into one factor. At the same time, variable 2_5 is
of similar amount of factor loading in factor 2 and 3; 2_7 is of similar amount of factor
loading in factor 1 and 2. Given the fact that 2_5 and 2_7 is of relatively low correlation with
each other (.38, <.50), and that question 2_5, 6, 7 were placed together in the survey right
after the construct of “dependency on computer” in the questionnaire, we believe there might

65
be prompting and cause and effect impact. Thus, we decide to deleted the whole construct of
“internet usage”.
We also found that construct 4, “attitude towards shopping” was divided to two
different factors: items from 3_1 to 3_7 belong to factor 1, and items from 3_8 to 3_11
belong to factor 4. 3_9 is found to be of similar amount of factor loading in factor 1 and 3.
Predictor 3_10 and 3_11 are found to be double barrel questions after carefully checking their
meanings again. Considering that correlation results suggesting low overlap among these
items, we reached the decision to remove items from 3_8 to 3_11.
Step 4
Then we fixed the problem by running a fourth factor analysis with three proposed
factors.

66
The explained variance went up to 62.23% (>50%) in this factor analysis. We found
1_1, 1_2, 1_3, and 1_4 fall into one group, 2_1, 2_2, 2_3, and 2_4 fall into one group and
3_1, 2, 3, 4, 5, 6, 7 fall into the other group.
Step 5
Next we started a new series of factor analysis with constructs “ability to shop
online” and “perceived advantages of online shopping”.

67
According to the result of the factor analysis, the explained variance is to 61.61%
(>50%). We found 4_1, 4_3, and 4_5 fall into one group, 4_2, 4_4, and 4_6 fall into the other
group.
Step 6
We then involved 7 items from “Perceived trust in specific online retailer” construct
to the factor analysis, and increased proposed factor number to three.

68
According to the results of the factor analysis, the explained variance is to 66.11%
(>50%). We found 4_1, 4_3, and 4_5 fall into one group, 4_2, 4_4, and 4_6 fall into one
group, 6_1, 2, 3, 4, 5 fall into the other group.
Step 7
Next we added two items from “Website design”.

69
From the result of the above factor analysis, we found that 4_1 has similar amounts of
factor loadings in factor 2 and 4 (.47 and .59), and 6_7 has similar amount of factor loadings
in factor 1 and 4 (.57 and .61). Since variable 4_1 “Using the internet to shop challenges
me” has low correlation with the other two predictors within the same construct, we decided
to remove it. For 6_7 “This online retailer is presenting information fast” is confusing in
meaning, and was therefore deleted. 6_6 is not going to be used in the following tests in that
it became a single item measure.
Step 8
We then fixed the issues by removing all the redundant items.

70
(>50%). We found 4_3, and 4_5 fall into one group, 4_2, 4_4, and 4_6 fall into one group,
6_1, 2, 3, 4, 5 fall into the other group.
Step 9
Then we added five items from “Website experience” construct.

71
From the factor analysis (explained variance = 65.28%, >50%), we can see that item
7_1 has similar factor loadings in factor 1 and 4 (.43 and .51), 7_3 has similar factor loadings
in factor 1, 2, and 4 (.58, .33 and .22). 7_5 fall into factor 1, which it has no overlap in
meaning with. Combining the red flags raised in correlation test stage (too much overlap with
items outside of the same construct), we decided to remove the whole construct of “Website
experience”.
Step 10
We took these items out and added three items under construct of “Perceived
costumer services”.

72
(>50%). We found 4_3, and 4_5 fall into one group, 4_2, 4_4, and 4_6 fall into one group,
6_1, 2, 3, 4, 5 fall into one group, and 7_6, 7_7, and 7_8 fall into the other group.
Step 11
Then we opened a new set of factor analysis by testing constructs “Perceived product
assortment” and “Perceived ease of use”.

73
We found from the factor analysis that 8_1 falls into factor 1 which it does not have
overlap in meaning qualitatively (explained variance = 59.02%, >50%), so we decided to
remove it. 8_4 is found to have similar factor loadings in both factors. Referring to its
correlation results, we thought it might be the word “interact” is still different from “use” in
some way, so we decided to take out 8_4 and 8_10 at this time.
Step 12
Then we fixed above problems and proceed to add one more construct “Consumer
loyalty” in.

74
According to the factor analysis results, the explained variance is 65.73% (>50%). We
found that 9_2 is standing out and not belonging to any of the three groupings. Given the fact
that in inter-item correlation results, “I consider myself to be highly loyal to a specific online
retailer” is of negligible overlap with the other two items under the same construct (.05 and -
.02 respectively), and the fact that we realized item 9_1 is a double negative question and 9_3
is confusing in meaning, we decided to take out the entire construct of “consumer loyalty”.
Step 13
We fixed the problem and added construct “Perceived privacy”.

75
The explained variance is 73.83% (50%), and we found 8_3, 8_5 and 8_7 fall into one
group, 8_2, 8_6, 8_8 and 8_9 fall into one group, 9_4, 9_5 and 9_6 fall in to the other group.
Step 14
Then we added four items from the last construct “Perceived return policies” to the
factor analysis.
Here we found 8_6 has comparably close factor loadings in factor 1 and 2. Since it
does not have overlap in meaning with items under “Perceived return policies”, we decided

76
to remove it to avoid coefficients. At the same time, 8_9 is a double barrel measure that is
ought to be deleted.
Step 15
Here is the last factor analysis test after fixing the above problem.
We found the explained variance is 78.36% (>50%), 8_3, 8_5 and 8_7 fall into one
group, 8_2 and 8_8 fall into one group, 9_4, 9_5 and 9_6 fall into one group, 9_7, 9_8, 9_9,
and 9_10 fall into the other group.
Therefore, the final factor analysis test results will be shown in the following table.
Construct Items
Need for cognition 1_1, 2, 3, 4
Dependency on computer 2_1, 2, 3, 4
Attitude towards shopping 3_1, 2, 3, 4, 5, 6, 7
Ability to shop online 4_3, 5
Perceived advantages of online shopping 4_2, 4, 6
Perceived trust in specific online retailer 6_1, 2, 3, 4, 5
Perceived customer services 7_6, 7, 8
Perceived product assortment 8_3, 5, 7
Perceived ease of use 8_2, 8
Perceived security 9_4, 5, 6
Perceived return policies 9_7, 8, 9, 10

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Reliability test
We wanted to make sure that the factors were measuring the given constructs and also
check the extent to which each item was measuring the construct. We therefore tested the
reliability of the constructs. If the reliability of a certain factor is lower than .50, we consider
it is not a reliable construct that measures what it intent to. Besides, if the value of reliability
test of a construct raises greatly after deleting certain item in it, we consider that item is
legging other items and have to be removed.
The results of the reliability test are listed below:
From the table we can see the reliabilities of the 11 constructs are .76, .84, .88, .75,
.64, .88, .83, .90, .80, .69, .87, and .89 respectively. We therefore conclude that the 11 factors
are reliable in predicting respondents’ intention to shop online (α>.50). Note that for Factor
7, item 7_6 is removed because it is legging the construct.
Therefore, the final grouping solution after validity and reliability test is shown
below:

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Revision of survey
After conducting the confirmative tests for measures to be used in survey
questionnaire that help capture the constructs, we find there are two major issues to be
settled: erroneous measures, and the order of questions in the survey.
Measurement level errors
Construct “Attitude towards shopping”, and “Perceived ease of use” involves double
barrel questions; Construct “Attitude towards shopping” and “Customer loyalty” have
questions ambiguous in meaning; “Customer loyalty” also has one double negative question.
Order caused error
Measures in construct “Internet usage” are placed after “Dependency on computer”,
which may prompt respondents and result in a cause-effect impact.
Others
Some measures have no overlap in meaning with other measures in different
constructs, but end up with large factor loadings in factors other than the one that they
originally belonged to.
To correct above errors, the researchers removed measures cause errors and
randomized the order of the questions under each section.

80
Conclusion
This project is a pre-test for Amazon.com to conduct a market research to know what
drives college students across the United States to shop online. Since ecommerce industry in
the U.S. is a highly competitive field with flux of existing and new rivals, this will help the
client to better understand elements that motivate college students to and keep them from
shopping with a certain online retailer. Through exploring these predictors and their
influences on students’ intention to shop online or shop with a certain online retailer, the
client can accordingly develop solutions to cope with college student market.
Although errors are found in this pre-test of measurements, the value of the project is
to test and improve the instrument. After fixing all the problems in the original survey
questionnaire, the valid and reliable revised survey can better help with capturing precise data
from the respondents.
To make full use of the revised survey, the client need to use a probability sample to
generalize conclusions that can represent the population (college students across the U.S.).
The sample size also need to be much larger than the sample size we used in the pre-test
project. Following data collection stage, the client has to conduct multiple regression test to
look into how influential each construct is on college students’ intention to shop online.
Based on the results of the multiple regression, the client will be able to segment the
population and find potential consumers. The client is also enabled to profile those
consumers and generate concrete and actionable solutions to target on each group from the
research.

81
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90
Appendix 3
Revised Survey Instrument
Student Opinion Survey
We are conducting this study to learn about students’ opinions concerning a variety of
current topics. Thank you for taking the time to complete our survey. Your responses are
anonymous.
Our first questions are general ideas. For each of the following statements, please
tell us how well it describes you by checking the box corresponding with your choice.
Strongly
disagree
Strongly
agree
I would rather do something that requires
little thought than something that is sure to
challenge my thinking abilities.
I like tasks that require little thought once I
have learned them.
I only think as hard as I have to.
I try to anticipate and avoid situations where
there is a likely chance I will have to think in
depth about something.
The next questions are about computers and the internet (including using multiple
devices to get online). For each of the following statements, please tell us how much you
agree or disagree with each of the following statements by checking the box corresponding
with your choice.
Strongly
disagree
Strongly
agree
The computer is as essential in my
home as is any other household
appliance.
It would be difficult to imagine life
without a computer
The computer has saved me time.

91
The computer has become part of
my daily routine.
Please continue to next page.
The next questions are about shopping. For each of the following statements, please
tell us how much you agree or disagree with each of the following statements by checking the
box corresponding with your choice.
Strongly
disagree
Strongly
agree
I often buy things spontaneously.
“Just do it” describes the way I buy
things.
“Buy now, think about it later”
describes me.
Sometimes I feel like buying things
on the spur of the moment.
I buy things according to how I feel
at the moment.
I carefully plan most of my
purchases.
Sometimes I am a bit reckless
about what I buy.
The next questions are about shopping online. For each of the following statements,
please tell us how much you agree or disagree with each of the following statements by
checking the box corresponding with your choice.
Strongly
disagree
Strongly
agree
Shop online is more convenient than shop
in a traditional retail store.
Using the internet to shop provides a good
test of my skills.
Shop online saves more time than shop in a
traditional retail store.

92
I found that using the internet to shop
stretched my capabilities to my limits.
When shopping online, it is easier to
browse items than shopping in a traditional
retail store.
Which online retailer you go most to shop?
__Walmart.com __Amazon.com __Target.com __eBay.com __Other, please
specify_______
Please keep the above answer in mind to finish the rest of the questionnaire.
The next questions are about this particular online retailer you choose. For each of
the following statements, please tell us how much you agree or disagree with each of the
following statements by checking the box corresponding with your choice.
Strongly
disagree
Strongly
agree
This specific online retailer delivers
what it promises.
The online retailer has practices that
make returning items quick and easy.
Learning to operate the online
retailer’s website I have just visited
is easy.
The online retailer takes care of
product exchanges and returns
promptly.
Over time, my experiences with this
online retailer have led me to expect
it to keep its promises, no more and
no less.
I feel safe in my transactions with
this online retailer’s website.

93
The online retailer shows as much
concern for customers returning
items as for those shopping for new
ones.
This website does not satisfy a
majority of my online shopping
needs.
Strongly
disagree
Strongly
agree
The online retailer’s website that I
have just visited is easy to use.
This online retailer doesn’t pretend
to be something it isn’t.
I feel like my privacy is protected at
this online retailer’s site.
This website does not carry a wide
selection of products to choose
from.
The online retailer’s website has
adequate security features.
This online retailer’s product claims
are believable.
The choice of products at this
website is limited.
The return policies laid out in this
website are customer friendly.

94
Strongly
disagree
Strongly
agree
In the future, I intend to use online
shopping websites for purchases.
If you were trying to buy a product tomorrow (available online and offline) how likely would
you be to shop online?
If you were to shop a product online tomorrow, how likely are you going to buy on:
Extremely
likely
Likely
Neither likely
nor unlikely
Unlikely
Extremely
unlikely
eBay.com
Walmart.com
Target.com
Amazon.com
How old are you? _________________
__Male __Female
Thank you for your time!

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