Exploring Online Consumer Behaviors
By Jake Kohl
The Internet has been accessible to the
public for over twenty years. It came upon the scene and has exploded in
popularity like few things have ever done in the history of the world. Since
the introduction of the World Wide Web, the interest in the value of commerce
and individuals has been growing. Skeptical at first, online consumerism has
steadily increased and along with it has come some positive and negative
behaviors. The purpose of this research is to understand how individual behaviors
affect online consumerism. According to Perner (2008), consumer behavior is
defined as “the study of individuals, groups, or organizations and the
processes they use to select, secure, use, and dispose of products, services,
experiences, or ideas to satisfy needs and the impacts that these processes
have on the consumer and society” (Perner, Lars, 2008). By identifying the
behaviors that support buying online and those which do not, businesses can
help to increase profits and will help to assure their share of the market, as
electronic trade may well out-step traditional buying in the not too distant
future.
There are many variables to consider
when outlining behaviors of Internet consumerism. According to Vazquez and Xu
(2009), online consumer behavior is affected by three main things:
1. “Attitudes
towards online shopping
2. Motivations,
such as price, convenience and hedonic motivations
3. Online
information search” (p.409).
If a person is positive about the
experience of shopping on the Internet then that attitude will affect the
outcome of purchasing online. Also, online consumers feel more in control when
they can search with relative ease, prices and special offers. This price
comparison is in itself, a great motivational tool. The fact that more
information is available online concerning products also allows the consumer to
feel that better information, will lead to better decision making on their part,
thus, allowing for more internet buying.
Research was conducted through a self-given
online questionnaire; important data was collected concerning the scale items
of attitude, motivations and searches of information. The group consisted of
students in three different age categories. The first were between 15 and 24. The
second group was between 25 and 34 years old. The last group fell between the
ages of 35 and 44 years old. The 35 and 44
year old group was rated as first in Internet buying. The 25 and 30 year old
group were next, followed by the younger group. A further study was done
through the use of email and administered questionnaires. Two hundred students
were surveyed in person and 300 students were asked to fill in an email survey.
Data analysis using a quantitative approach was used and reliability analysis
was formulated and used to test the data obtained. Canonical analysis was also
used to help understand the data and build a framework around online consumer
behavior.
The analysis showed that 49.8% of
those surveyed were women and 50.2% were male. The group under age 24 made up a
little less than two thirds of the total. Those over 24 accounted for the rest.
Respondents who had used the Internet for five years or more represented 79% of
the study. The results clearly showed that online shopping motivations, information
searches, and attitude all had a significant effect on online purchases. (Vazquez and Xu, 2009 p. 412)
Other variables to consider in online
consumer behavior are online experience, sexual preference, and the primary
place in which the Internet is accessed (Koyuncu and Lien, 2003, p. 721). They
concluded that consumers, who had more experience with the internet, felt more
comfortable navigating it. Consumers also felt safer in using the internet at
their residence or on the job, rather than community computers. Sexual
preference, according to their research, showed that bisexuals bought online in
greater numbers than heterosexuals. This behavior may have resulted because of
the bias against this portion of the population that is present in society.
There were over 10,000 participants. The total amount of samples used was 8,717.
Data such as an individual’s demographic; economics, sexual preferences, and
primary places of online access were collected. The findings showed 7,026 considered
themselves online shoppers, while 1,691 did not. The average for education was
considered, “some college” for both groups. Online shoppers’ income was between
$50 and $74K. The non-online shoppers’ income was between $30 and $40K. The
average ages of both groups were between 36 and 40 years old. Almost 90% were
white and 60% were male. A little under half of all the participants were
married. The conclusion of this research clearly identified that both the
primary place to access the Internet and sexual preference had very important
positive effects on online shopping.
Unlike shopping in a store on-site,
making purchases online has other positives associated with it. According to Ammeter
and Kim (2008), they explain how online shoppers have greater access to
communicate with those they are buying from (p. 9). This communication can take
place in such ways as bulletin boards and e-mails. This gives consumers a
perception of personalized assistance. In contrast to this scenario, is the
fact that we are losing our ability to maintain a high level of customer
service on-site. The employees many shoppers encounter seem more to be filling
a spot than actively engaging in helping consumers with purchases or becoming knowledgeable
about what they sell.
Online consumerism is not however without
its apprehensions. Research by Swinder (2008) brought about concerns regarding,
“four consumer online concerns, identified as:
1. Privacy
2. Security
3. Credibility
4. Virtual
experience (p. 339).
Each of these factors is considered
when people think of making online purchases. Privacy issues, security,
credibility and virtual experience have all shown to have negative effects on
consumer purchasing (2008). It is relatively simple for information to be
collected from consumers whenever one logs onto a site or makes a purchase.
Some information, such as name, address, phone number and credit card, is
gathered through direct questioning and other information is gathered through
tracking software. This transference of information makes some consumers
nervous and they do not want to take the risk. Another point to consider is the
credibility of the person or business in which one is dealing with. Questions
arise about trust, description of merchandise, warranties, shipment, returns,
and follow-up correspondence. Although this concern, though valid - according
to research, had a very limited negative effect on consumer buying habits
(2008).
Another negative behavioral pattern well documented is that of
compulsive buying tendencies. These tendencies to over buy can have detrimental
effects on the consumer, notably affecting monies, feelings, and relationships.
According to “The Relationship Between Consumers’ Tendencies to Buy Compulsively
and Their Motivation to Shop and Buy on the Internet,” somewhere between 5 and
9 percent of America’s population could be identified as people who have a
propensity to compulsively buy (Kukar-Kinney, Ridgway, & Monroe, 2009).
Motivators of this type of behavior include the very key ingredients of online
shopping. These motivations are the following;
a) Items
may be purchased at any time
b) Shopping
can be done frequently
c) A
broader variety exists
d) Purchases
may be brought in private
Methods
The actual detailed research conducted involved many aspects. The first
method of research was a survey of over 300 people in 42 states. As quoted from
the article, “the sample consisted of 98.5% women, 63% of the respondents were
married, the average age was 53 years, and the average household income was
$82,000” (Kukar-Kinney, Ridgway, & Monroe, 2009, p. 300). The penchant to
buy compulsively was measured using a buying scale that included six focuses.
These scale items included, unopened packages at home, labeled by others as a
shopaholic, how much time actually spent shopping, buying unneeded items,
buying unplanned items, and if the surveyed considered themselves an impulse
buyer.
Research was also conducted using a 22
statement survey in which the surveyed agreed or disagreed with the following
statement, “In comparison to retail stores, I shop on the Internet when buying
clothing and accessories for myself because” (Kukar-Kinney, Ridgway, &
Monroe 2009, p. 301). The 22 statements included areas that linked to, “buying
unobserved, product variety, social interaction, and immediate positive
feelings.” (p. 301) An analyses of the information was obtained through a series
of “linear regression analysis, with individual shopping and buying motivations
in the role of the dependent variable, and compulsive buying index as an
independent variable” (p. 302).
The final research was defined as
cluster analysis. Taken into account were such categories as demographics, age,
gender, income, education level, marital status, average income spent at retail
and internet stores, frequency of purchase, and the number of credit cards
used. The findings of each method was charted and analyzed with easy to
understand tables and terminology. In keeping with the theme of the method
section, the interpretations and conclusions made by these researchers were
very detailed and data supported. The results showed that compulsive online
consumer behavior was in part explained by motivations of the shopper. All
motives set forth in this study exhibited an important positive connection to
the compulsive buying scale except for one and that was the product variety
motive. The overall findings concluded, as the researchers had hypothesized
that
compulsive
buying strongly affected consumers’ decisions to make purchases using the
Internet.
This research group acknowledges that
one of its weaknesses was the sampling of consumers used. Subjects were
relatively wealthy women, who frequented expensive internet retailers. This
assessment represents a bias in the general population. The research also only
tracked the behavior of women. Compulsive online consumer behaviors are not
gender specific and therefore this research, in my opinion, is somewhat flawed.
Another weakness noted in this study was the amount of people surveyed in the
first example with a total number of a little over 300. I do however think that
one of this study’s greatest strengths was the broad base of surveys conducted
(2009).
Research conducted by Janda (2008)
identified differences between gender concerning online behaviors. The main
differences account for shopping behaviors, attitudes to technology, and
processing of information. Women were found to be more venerable to risks and
perceived risks as higher than that of the male population. It was also found
that women used the Internet less often and were less confident about their
online ability. Women were found to enjoy the experience of shopping more than
men. Women leaned more toward the sites which provided information and
education about items.
The data for this gender research was
gathered through surveys that were handed out. The opinions were taken from a
quota sampling of different age groups. Responses totaling 404 were collected.
The sample included a total of 196 men and 208 women. The median age was 32.8
years old. Another noted point was that the participants each had a history of
Internet usage for about five years (Crutsinger, Jeon, & Kim, 2008). This
is valuable research and asserts that these differences must be addressed in
order for online distributors to appeal to both sexes in a meaningful way.
In research done by Crutsinger,
Jeon, & Kim, they identified seven motivators of online auction
participants:
1. Search
costs
2. Product
assortment and price
3. Brand
equity
4. Transaction
costs
5. Customer
orientation
6. Perceived
quality
7. Social
interaction (2008, p. 31).
The study of
online auctions was done through a questionnaire, based on 36 auction
motivators, online behavior, and demographics of participants. The Likert scale
was used to determine responses. These participants totaled 410 and were
selected from a pool of college students. Data was retrieved from 341
responses. There were 74.8% female and 42.6% labeled as
white. The ages
ranged from 18 to 40 years old. The collected data revealed 90.9% were regular
users of the Internet. A low 20% revealed they had no online auction
experience. The remaining who did have experience with online auctions were
identified as follows; 5.3% used the Internet daily, 15.2% weekly, 29.9%
monthly, and 29.3% said one to two times per year. An interesting note to this
research was that although most of those surveyed had participated in online
auctions, the majority of them (80.6%) conveyed that they had never sold
anything online (Crutsinger, Jeon, & Kim, 2008).
According to this research the following
results showed that, “search costs were the most important motivation, followed
by product assortment/price, brand equity, transaction costs, customer orientation,
and perceived quality – social interaction was the least important motivation
associated with online auction behaviors” (Crutsinger, Jeon, & Kim, 2008,
p. 36).
College students
are very involved as internet participants; studies like these show the need
for businesses to value online auctions to increase their customer base. This
research, however did not addressed the negative component of online auction
consumer behavior. These types of behavior may fall into one of two categories,
such as impulse buying or compulsive buying. Further research would need to be
done in order to fully understand online consumer
auction
behavior.
A major part of
esthetics is how information is arranged on the web page. Too much information
can overwhelm a consumer, too little can decrease consumer confidence. Stibel
(2005) conducted research which included this topic of interest. Tests
performed, showed how online information presentation failed in many ways. “Category information presented in an
alphabetical list allowed consumers the ability to navigate to their
destination much faster than when they were asked to traverse a hyperlinked
hierarchy” (p. 149). Simplicity is the key. Consumers want to navigate with the
least amount of effort. Clarity in the web design gives consumers the
confidence in their ability to do so. This research led Stibel to identify a
mental model of consumers, which concluded that people wanted information
presented in concise and understanding ways. The ability of online businesses
to do this is imperative because it leads to a “more intuitive and compelling
online experience (p.149). There
is a sub-group of online consumers that have been recently identified. This
group has been termed, the “net-geners or net generation.” This term is defined
as, “individuals born between 1977 and 1997 and is the first generation to grow
up surrounded by digital media and the Internet” (Donghyun, Kim & Ammeter, 2008,
p.7). This group understands technology and is comfortable with Internet
commerce.
Conclusion
The net-geners are
the first generation that will actually surpass the baby-boomers in population
size. Because of their knowledge and their numbers, it is safe to say that
business as usual is in for a transformation. As the elderly portion of our
population die and new individuals are born, this new way of doing business
will be the reality that is known throughout life. The sky is indeed the limit
in the progression of online consumerism.
References
Janda,
S. (2008). Does Gender Moderate the Effect of Online Concerns on Purchase
Likelihood?. Journal Of Internet Commerce, 7(3), 339-358.
doi:10.1080/15332860802250401.
Jeon,
S., Crutsinger, C., & Kim, H. (2008). Exploring online auction behaviors
and motivations. Journal of Family and Consumer Sciences, 100(2), 31-40.
Retrieved from http://search.proquest.com/docview/218160218?accountid=12085.
Applied Economics.
35.6 (Apr. 15, 2003) 35, 721-726.
Monika
Kukar-Kinney, Nancy, M. R., & Kent, B. M. (2009). The relationship between
consumers' tendencies to buy compulsively and their motivations to shop and buy
on the internet. Journal of Retailing, 85(3), 298-307. doi:
http://dx.doi.org/10.1016/j.jretai.2009.05.002
Stibel,
J. (2005). Mental models and online consumer behaviour. Behaviour &
Information Technology, 24(2), 147-150.
doi:10.1080/01449290512331321901
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