1. User-Centric Approaches to Research in
Cyberpsychology: A Review of the Literature
Jake D. Skinner
Sacred Heart University

2. Abstract
This paper presents a brief history of and introduction to the sub-field of
cyberpsychology with the intention of highlighting the importance of motivation in online
behavior. Focus is given to the distinct areas of focus: the psychological and physiological
effects of digital systems on users and the motivations for engaging in specific online behaviors.
Following is a literature review that highlights contemporary research in understanding
behaviors in digital systems and a discussion on their merits and methodological faults.
Consideration is given to possible avenues of future research.
Keywords: cyberpsychology, literature review, online behavior, internet, adolescent,
depression, social media, video games, Facebook

3. Introduction
Humanity is trending toward a point of digital saturation, where a comparable level of
computer technologies will be available to a majority of people around the globe. When the
National Science Foundation turned over what would become the internet to private enterprise
and removed restrictions on its commercial use, the number of internet hosts increased
exponentially within one year (FCC, 2005). Since 2000, there has been a rise in per capita
internet accessibility within nearly every country (ITU, 2014). The world’s largest social media
site, Facebook, is now available in 70 languages and has a total of 1.31 billion active users each
month, up from 1.11 billion in March of 2013 and 100 million in 2008 (Statistic Brain, 2014).
Cyberpsychology is an emerging sub-field of psychology concerned primarily with the
psychosocial analysis of cyberspace (Riva & Galimberti, 2001). The range of this sub-field has
essentially become divided into two distinct areas of focus: the effect of digital systems on the
mind and how people interact with and within them. As such it has a broad scope of study in
largely unexplored behaviors and cognitions related to existing and future technologies.
Research into the effects of specific digital systems on the mind is a subject with wide
applications. Studies have been published which examined the possibility of both positive and
negative effects of exposure to computer technology, such as the use of virtual reality aids in the
treatment of eating disorders (Cesa et. al., 2013) and how aggression may be affected by certain
types of video games (Adachi & Willoughby, 2011). Ongoing efforts to study these interrelations
have created opportunities for psychologists to challenge preconceptions and misconceptions
regarding new technologies through methodology and experimentation and to build upon the
works of predecessors and peers. While this line of research can inform as to the health benefits

4. Table 1: Percent of population using the internet per country from 2001-2013 (ITU, 2014)
2001 2003 2005 2007 2009 2011 2013
Argentina 9.78 11.91 17.72 25.95 34.00 51.00 59.90
Australia 52.69 63.00 69.45 74.25 79.49 83.00
Austria 39.19 42.70 58.00 69.37 73.45 78.74 80.62
Azerbaijan 0.31 8.03 14.54 27.40 50.00 58.70
Belgium 31.29 49.97 55.82 64.44 70.00 81.61 82.17
Canada 60.20 64.20 71.66 73.20 80.30 83.00 85.80
Chile 19.10 25.47 31.18 35.90 41.56 52.25 66.50
China 2.64 6.20 8.52 16.00 28.90 38.30 45.80
Colombia 2.85 7.39 11.01 21.80 30.00 40.35 51.70
Croatia 11.56 22.75 33.14 41.44 50.58 57.79 66.75
Czech Republic 14.70 34.30 35.27 51.93 64.43 70.49 74.11
Denmark 42.96 76.26 82.74 85.03 86.84 89.81 94.63
Egypt 0.84 4.04 12.75 16.03 25.69 39.83 49.56
Finland 43.11 69.22 74.48 80.78 82.49 88.71 91.51
France 26.33 36.14 42.87 66.09 71.58 77.82 81.92
Germany 31.65 55.90 68.71 75.16 79.00 81.27 83.96
Greece 10.94 17.80 24.00 35.88 42.40 51.65 59.87
Iran (I.R.) 1.48 6.93 8.10 9.47 11.07 21.00 31.40
Ireland 23.14 34.31 41.61 61.16 67.38 74.89 78.25
Israel 17.38 19.59 25.19 48.13 63.12 68.87 70.80
Italy 27.22 29.04 35.00 40.79 48.83 54.39 58.46
Japan 38.53 48.44 66.92 74.30 78.00 79.05 86.25
Korea (Rep.) 56.60 65.50 73.50 78.80 81.60 83.76 84.77
Lebanon 6.78 8.00 10.14 18.74 30.14 52.00 70.50
Mexico 7.04 12.90 17.21 20.81 26.34 37.18 43.46
Morocco 1.37 3.35 15.08 21.50 41.30 46.11 56.00
Netherlands 49.37 64.35 81.00 85.82 89.63 91.42 93.96
Norway 64.00 78.13 81.99 86.93 92.08 93.49 95.05
Russia 2.94 8.30 15.23 24.66 29.00 49.00 61.40
Saudi Arabia 4.68 8.00 12.71 30.00 38.00 47.50 60.50
Singapore 41.67 53.84 61.00 69.90 69.00 71.00 73.00
South Africa 6.35 7.01 7.49 8.07 10.00 33.97 48.90
Spain 18.15 39.93 47.88 55.11 62.40 67.60 71.57
Switzerland 55.10 65.10 70.10 77.20 81.30 85.19 86.70
Turkey 5.19 12.33 15.46 28.63 36.40 43.07 46.25
United Arab Emirates 26.27 29.48 40.00 61.00 64.00 78.00 88.00
United Kingdom 33.48 64.82 70.00 75.09 83.56 85.38 89.84
United States 49.08 61.70 67.97 75.00 71.00 69.73 84.20
Venezuela 4.64 7.50 12.55 20.83 32.70 40.22 54.90
Viet Nam 1.27 3.78 12.74 20.76 26.55 35.07 43.90
Percentage of Individuals using the Internet

5. and risks associated with general use of digital systems, it does not focus on specific qualities of
the user experience that may have a direct effect on the hypothesis being tested.
The observation of human interaction in digital systems has offered an intriguing
opportunity to explore social, dissonant, and role-adopted behaviors. It has helped us to
understand that there may be a dissociation between the choices a person makes in a digital
system, such as a game, and those they may make in a physical setting based on the role they
take on in the digital environment (Happ, Melzer, & Steffgen, 2014). It also sparks discussion on
the nature of communication and whether physical interaction is more, less, or similar in
importance to online interaction (Riva & Galimberti, 1998).
An advantage to the study of human behavior in digital settings is the ability to control
the system and apply test variables where it may be impossible to do so in a physical setting.
More importantly, the qualia of user interaction with online and solo digital systems is easier to
examine and document (Riva & Galimberti, 2001). It can also be directly correlated to
discoveries of health benefits and risks from the use of digital systems. This makes the study of
user behavior more specific – and, in turn, more accurate – in examining health concerns
regarding digital systems.
The purpose of this paper is to identify and review studies conducted on behaviors and
motivations of people in digital systems, indicate the benefits of research into the user
experience, and link such studies to research on the health impact of digital engagement. A
systematic search of PsycINFO and PsycARTICLES was conducted in addition to library
research using terms that referred to the subject matter (“cyberpsychology,” “virtual reality,”
“internet, “online”) with a focus on “behavior,” “online behavior,” and “interaction.”

6. The articles identified in this review are examples of cyberpsychological research that
explore the motivation of online behavior. First to be explored is the nature of adolescent
engagement with the internet, followed by studies that examine identification with social groups
and individuals in digital environments. Next is a study that sought to determine the effect of a
user’s physical space on their use of cyberspace among a cohort in Singapore. The review of
literature concludes with mitigating factors that drove people to quit Facebook versus people
who preferred to use it.
Review of Contemporary Literature
The nature of adolescent engagement with the internet is not well understood. Anderson
and McCabe (2012) sought to fill the gap of knowledge in this area by conducting an interpretive
study across two developmental age demographics and three research waves. The study was
based on existing models of adolescent social development in physical settings and intended to
discover similarities and differences between established models and situational behaviors on the
internet. Participants in the study were asked to report on their experiences following the
conclusion of their wave, during which their responses were categorized and reported on
according to similarity.
One of the key features of adolescent peer interaction on the internet was self-
socialization, in that the participants constructed their own expectations of their role behavior as
the internet influenced them in return. Adolescents could build their own environment and
socialize without the constraints previous generations had. A broader social context that allowed
adolescents to meet people around the globe, anonymity gained by being removed from a

7. physical body, and perceptions of “hot” and “cool” contexts supported the self-socialization
process by the participants.
Adolescents in this study also showed that they were able to learn how to gain freedom
and make connections. For the adolescents, attaining freedom was more about escaping
uncomfortable rules and norms. When making connections with other people online, it typically
reflected an absence of such relationships offline.
Similar research conducted prior among participants from the United Kingdom, Spain,
and Japan examined how young people identified with online social groups versus offline social
groups. Localized versions of a survey were uploaded to regional directories of the same website
and attracted 11,255 responses from all age demographics. The number was reduced after
cleaning to 9,675 and further reduced to 4,299 when the researchers selected surveys from
participants age 12-30.
The survey asked respondents to report their identification with family, a particular game
community, other online groups, offline groups, and their residential neighborhood. Responses
among the three regions were consistent in rating family identification first and identification
with the game community a close second. (Lehdonvirta & Räsänen, 2011).
Feinstein, et.al. (2013) conducted online surveys of participants that assessed social
comparison, rumination and depressive symptoms both in general and on Facebook. The surveys
were administered three weeks apart and allowed respondents to access the survey from
anywhere. The Social Comparison on Facebook section used an 11-item self-report measure that
presented respondents with incomplete sentences that they were required to fill in with a word

8. provided on the survey. Responses on this section were coded such that a total score between 11
and 110 could be obtained in this section on each survey. The General Social Comparison
section was an 11-item self-report measure that asked the participant to respond to questions on a
scale of 1 (strongly disagree) to 5 (strongly agree). Rumination was measured via the
Rumination Responses Scale (RRS), a 22-item self-report measure that assesses how frequently
individuals experience or engage in various thoughts, feelings, and actions during a depressed
mood. Depressive symptoms were assessed using the Center for Epidemiological Studies-
Depression Scale (CES-D), a 20-item self-report measure assessing past week experience of
depressive symptoms.
The results of this study showed that social comparison on Facebook, rumination, and
depressive symptoms were positively and significantly correlated to each other. Responses in the
first survey indicated that there was a modest correlation between general social comparison and
Facebook social comparison, but the second survey showed an insignificant correlation between
the two. When considering the data as a whole, a strong correlation is formed and, accounting for
a general tendency to engage in social comparison, negative comparison to others on Facebook
leads to increased rumination, thus increasing depressive symptoms.
Goby (2003) created a survey to discover whether there was a correlation between the
types of online interaction people engaged in and the amount of physical space they could
expand into. Her research was conducted electronically in Singapore through a distributed
questionnaire. The results showed a definite correlation between house size and online social
activity, with 14% of those living in large-sized houses preferring not to go out while 4% of
those living in small-sized houses preferred not to go out. While smaller household space was a

9. factor in discouraging internet socialization, larger numbers of household members also
encouraged participants to leave their home to socialize. A perceived lack of public space was
also examined, though no correlation could be found within the sample between such perceptions
and online socialization.
A 2012 study on the rationale behind users quitting from social media site Facebook was
carried out to illuminate the differentiating characteristics between those who quit Facebook and
those who maintained accounts. An online questionnaire was made available and Facebook users
were invited to participate via the social media platform. Recruitment of Facebook quitters was
achieved by visiting a website representing the online initiative ‘Quit Facebook Day’ and posting
an invitation to the survey in the website’s discussion forum. Facebook quitters averaged 31
years of age with a range of 11-75 years, tended to be men (71.5 percent male) and lived in 47
different countries. Facebook users averaged 24 years of age with a range of 15-63 years and
lived in 41 different countries. Both groups averaged similar hours per day on Facebook
(quitters: 1.9. users: 1.8) and possessed their account for similar amounts of time (quitters: 26
months, users: 29 months).
The survey tested for privacy concern, internet addiction, and dimensionality on the Big
Five personality scale through use of the Mini International Personality Item Pool (Mini-IPIP).
Though age correlated with lower internet addiction scores, those who quit Facebook were had
higher scores on the internet addiction test. Additionally, Facebook quitters were more concerned
with the privacy of their information and more conscientious overall. The study’s authors suggest
that, since personality traits were strongly correlated with scores on privacy concerns and

10. internet addiction, they may have influenced the likelihood of quitting Facebook (Stieger,
Burger, Bohn, & Voracek, 2012).
Discussion
The studies listed here, though hugely important in helping to understand motivational
factors in online behavior, rely on quasi-experimental design to obtain the results. More often
than not, the use of surveys and other self-report measures provided data points rather than
quantifiable, verifiable observations. It is in this sense that the effect of digital systems on the
mind and body has greater methodological proof, as 1:1 values can be easily established and
independently verified.
As in other fields of psychology, there are methodological hurdles the cyberpsychologist
must face when designing a research project. A standardization of equipment is a significant
confound in the execution of research, especially if the participants in a study are remotely
participating. Unless the study is carried out in controlled conditions, there is no way to ensure
each participant will receive the same stimuli at the same time and under the same conditions due
to different hardware, software, and internet connections. Participants are also typically
unmonitored which makes the validation of information impossible, especially in online
communities where monikers and gender switches are common. Knowing, willing participants of
online studies are also often self-selected which reduces the likelihood of achieving a
representative random sample (Riva & Galimberti, 2001).
It is therefore important to incorporate experimental methods into understanding online
behaviors and their motivations. Proactively seeking appropriate, representative samples and

11. applying experimental methodology to research similar to those discussed here could help to
explain specific psychosocial milestones or triggers that affect online activity. For example,
asymmetry has been examined in the prefrontal cortical pathway when individuals suffering
from major depressive disorder perceive rejection (Beeney, Levy, Gatzke-Kopp, & Hallquist,
2014). Building on the 2012 Feinstein et.al. research, a study could be conducted that requires
participants to spend an hour a day using monitored computer stations to browse Facebook while
connected to an electroencephalograph (EEG). Such a study could help to identify the neural
pathways activated during a depressed person’s activity on Facebook. Additionally, keystrokes
and mouse clicks could be recorded to analyze what the participant is looking at and compared
against the EEG readings.
Ultimately, the use of additional technology may help to bolster research in this area.
Magnetic resonance imaging (MRI) could be used to monitor brain blood flow in-vivo during
online social activities or during solo digital experiences. As alluded to previously, EEGs could
indicate brainwave activity during studies so researchers could rely less on self-report.
Electrocardiograms could be used to determine if heart rate affects the way people browse the
internet or socialize in myriad digital settings.
The creation of scenarios in the digital world that can be repeated and applied to multiple
test subjects is key to validating experiments on motivation in digital environments. Researchers
should not be hesitant to affect the physical state of their subjects, provided it falls within the
ethical guidelines of the American Psychological Association and the local Independent Review
Board. Testing salivation in a hungry participant after exposure to food in a virtual reality when
compared against a recently fed participant could be a valid experiment to conduct. Exploring

12. receptivity of ideas, such as advertising or propaganda, in an interactive play experience could
also be an avenue of study.
With the number of people gaining access to the internet growing every year,
cyberpsychology will only increase in importance, contributions and scope. The current state of
cyberpsychology is positive with an overall positive contribution and high outlook. However, the
experimental method should be employed more consistently in common studies to gather more
consistent data.

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