Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Using Demographic Migration Theory to Explore Why People Switch
Between Online Games
Avus C.Y. Hou
Department of
Information Management
National Taiwan
University
[email protected]
Ching-Chin Chern
Department of
Information Management
National Taiwan
University
[email protected]
Abstract
Online gaming has become a popular leisure-time
activity in resent years. In this study, we adopted the
Push-Pull-Mooring Model, which analyzes human
migration behavior based on the Demographic
Migration Theory, to explain why players switch from
an existing game to a new one. An empirical survey
was conducted to collect data from 574 online
gamers and then this data was analyzed using the
Structural Equation Modeling (SEM). The results
indicated that the Push-Pull-Mooring Model can be
extended to explain the switching intentions of online
game players. It appears that the "Mooring effects"
have a stronger influence on players’ switching
intentions than "Pull effects", while "Push effects"
have no influence. Finally, this article discusses the
implications of our findings and offer possible
avenues for management at online game providers to
understand their customers better.
Keywords: Switching Intention; Online Gaming;
Push-Pull-Mooring Model
1. Introduction
As more families gain access to broadband, online
gaming is becoming a mainstream leisure activity on
the Internet. The online game market has grown
rapidly in recent years. In US, for example, the value
of the market has escalated to $4 billion dollars in
2008. Similarly, in Asia-Pacific, the market value is
estimated to US$1,840 million in 2008, compared to
US$760 million in 2003 [29].
Online game providers generate revenue from two
sources: subscription-fee and sale the powerful
virtual items to players. These two sources are not
related to selling the software license directly to the
players, so the problem of software piracy does not
arise. This special feature of online game business
guarantees the profits of the game providers.
Therefore, numerous companies enter into the market
to share the pie, which escalates competition [5]. Due
Houn-Gee Chen
Department of
Information Management
National Taiwan
University
[email protected]
Yu-Chen Chen
Department of
Business Administration
Soochow University
[email protected]
to high involvement of online gaming, players need
to expend a great deal of efforts training themselves
for achieving higher game skill, and then they can
enjoy the games afterward [1]. Hence, players are
usually absorbed in one game for a certain period of
time. After the period, most players will switch
between games at some state because they have
tendencies of pursuing fresh entertainments. Hence,
to attract players to switch to a new game or to retain
players to stay in the current game are both important
issues for online game providers. However, studies
related to these two issues are still insufficient [3]. A
comprehensive work is thus needed to understand
more about this topic.
The online game creates a virtual world to allow
gamers to perform virtual roles of their choices with
develop online carrier with other players who share
common interests. Therefore, gamers are like
residents in a virtual world when they are in game
playing. When players switch between games, they
analogically switch between different virtual worlds
and thus the switching behavior can be treated as
migration among virtual worlds. Therefore, it is
appropriate that we can analyze switching between
online games with human migration model which
scholars frequently used to explain migration in real
life.
Researchers in the marketing field explain
consumers’ switching behaviors by using the
Push-Pull-Mooring (PPM) model as a theoretical
framework [9]. We also adopted the PPM model to
explore online game players’ switching intentions.
Empirical data were collected from 574 online
gamers and were assessed and analyzed by Structure
Equation Modeling (SEM) through LISREL. The
contribution of this paper has two folds. First, we
have shown that the PPM model, which is used to
analyze the population migration behavior in the real
world, can also be applied to explain the gamer
migration behavior in the virtual world but with
modification. Second, this study provides a
comprehensive model to explain the reasons why
gamers stay in the current game or switch to a new
978-0-7695-3450-3/09 $25.00 © 2009 IEEE
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
online game. These findings offer useful insight for
online game providers to understand their customers
better, and thereby develop more effective business
strategies.
The remainder of this paper is organized as
follows. Section 2 explains the background of the
Push-Pull-Mooring migration model. In Section 3,
we present our hypotheses and explain the rationale
behind them. In section 4, we discuss the methods
used to analyze the data. The results of hypothesis
testing and Structure Equation Modeling (SEM)
through LISREL are detailed in Section 5. Finally, in
Section 6, we summarize our findings and consider
their theoretical and managerial implications.
2. Theoretical Background
In this research, we consider the switching
intentions of online gamers as a type of migratory
behavior in virtual worlds. People migration is a
longitudinal topic in demography, and people’s
movements to new destinations are considered as
switching behavior of living locations [16]. The
Push-Pull-Mooring Model (PPM Model), which is
the guideline for migration study, considers the
factors that push people to leave one place (their
original home) and the factors that pull them to
migrate to a new location [41]. In addition, the model
contains a number of variables that can either hold
latent migrants in their place of origin or facilitate
migration to the new destination [44]. In this section,
we introduce the concepts of the PPM model and
explain how we apply it to the phenomenon of online
game switching.
2.1 Migration
Demographists defined migration as “the
movement of a person (a migrant) between two
places for a certain period of time” [11]. According to
time period, migration can be divided into temporary
and permanent migration. The temporary means
people have leaved origin and work in other places
during the decades, while retire or elder then return to
origin. Permanent migrations are people who leave
their place of origin forever [30]. As a result, business
trip and holidays would not be included in migration
[32].
Researchers also classified the attributes of
migrants: Voluntarily migrants and involuntary
migrants (refugees) [30]. Voluntarily migrants are
who can freely choice their destination and migration
process. Alternatively, refugees have no choice and
must to migrate due to natural disasters or wars, no
matter he/she like to migration or not [11]. Similarly,
in the discipline of marketing, consumer switching
behavior can be also divided into voluntarily and
involuntary switching. Voluntarily switching occurs
because of core service failure, staffs response to
service failure, or pricing issues [35]. Involuntary
switching arises because the customer moved or
his/her provider closes.
2.2 Push-Pull-Mooring Model of Migration
The statement of people migration is influenced
by push-pull effects that can retrace to 1885 by
Rubenstein’s publication 'Law of Migration' [41]. To
date, push-pull model has become the main theory for
interpreting the migration of people [30]. It regards
migration as the consequence of interaction between
Push effects of the place of origin and pull effects of
the destination.
Push effects are the negative factors that impel
people to leave their place of origin; for example, a
lack of working opportunities, a decline in natural
resources or the prices paid for them, and natural
disasters. Pull effects are the positive factors of the
destination that attract people to it; such as, better
development and working opportunities, higher
income, and comfortable climate [41].
The push-pull model can be used to analyze most
types of migration behavior; however, it does not
account fully for the migration of individuals. For
example, if certain push factors and pull factors
operate in the place of origin and the destination
respectively, why do not all households move and
how can return migration be properly explained [44]?
In such situation, migration is not caused not only by
Push-Pull effects but also by personal factors. It is
probably the reason that Walmslry [53] questions the
assumptions about the relationship between economic
conditions and migration. He claims that migration is
related more to reasons of a personal kind, such as a
better life style.
A numbers of scholars have recognized that
normative and psychosocial variables were important
in migration decision. Lee [41] introduced the
construct of “intervening obstacles” and “personal
factors” into the discussion of push-pull forces.
Furthermore, Longino [42] identified ‘mooring’ as
representing the relationship group factors that are
most important in forming a decision to migrate or
not to migrate. Mooring may be seen as both cultural
and spatial factors through which a person or
household gains access to psychological wellbeing.
Moon [44] added the concept of ‘mooring’ into
Push-Pull model as Push-Pull-Mooring Model (PPM)
for explain population's migration. PPM model
reminds researchers that the personal factors and
cultural signals are important to movement.
Bansal et al., [9] argued that customers switching
among service providers are alike to consumers
migrant among service providers. They found PPM
model provided a unifying framework for position
the factors which found in service switching
literatures, and advised the researchers to apply PPM
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
model for understanding consumer switching
behavior. We believe, it is more appropriate to apply
PPM Model in online game switching. That is
because of people may be hard to recognize service
providers as “distinct worlds” due to all providers are
within same living space. Alternatively, the design
purposes of online game are to create virtual worlds
then make players ‘role-playing’ and interactivity
with others within the virtual worlds. Players
engaged within an online game are like to be a
resident in the virtual world. While players switching
to a new game play as other roles, he/she can
obviously feel stay in distinct environment and get
whole new experience.
3. Hypothesis
This research aims at understanding players’
switching intention (SI). Figure 1 presents the
research model. The factors impel players to leave
away the online game (Push effects): low enjoyment
(EN), dissatisfaction of the online game provider
(SA), and perceived insufficient participant (CP).
Factors attract players toward to the new online game:
alternative attractiveness (ATS). Factors to bind
gamers leave away (mooring effects): switching costs
(SC), social relationship (SR), and prior experience
(PE). This scission will discuss why we choice these
concepts and their position in PPM online game
switching model.
EN
SA
Push
CP
H1
SC
SR
Mooring
H3
SI
PE
H2
Pull
ATS
Figure 1. The research model
3.1. Push Effects
According to migration literature, people are
likely to move due to negative factors of their
original place [41]. For instance, lack opportunities of
career development: lack of work, no chance to marry,
lack natural resources or unable to buy, and disasters
may germinate the thought of migrating [41].
Because there is lack of works for understand why
players leave online games, we borrowed ideas form
literature that why people join a game and several
possible factors from online service research.
Players participate online gaming voluntarily to
feel optimal experience and perceive enjoyment [15;
26]. When players enter in the flow state, they
become absorbed in a game and feel in control [26].
There exists an incremental mechanism in the flow
experience [18]. Peoples will re-enter to an activity
for experience flow again and again [24]. This
mechanism is important because players expect to
experience flow by re-enters the online gaming; more
times they entry, the stronger sense of enjoyment they
fell. Therefore, player will continuous to be the
resident in the virtual world, she/he will not leave
away the online game. Oppositely, players emerge
switching to other games from perceive low
enjoyment in current online games.
Except for enjoyment, satisfaction of online game
provider is another vital factor to keep gamer stay.
Satisfaction is frequently used for weight and keeps
customers either offline or online service contexts [17;
36]. Though customer satisfaction and customer
loyalty is not always positive, but dissatisfy
customers will aggressively seek for alternativeness
to switch [25; 35]. Online game industry is kinds of
the online services. They offer online entertainment
technology for people use in leisure time [26].
Players vary in game experience and computer skills,
therefore, 24 hours call services are needed to answer
questions and solve problems such as inaccessibility
to the server, account theft, and stolen game items. If
the players perceive low satisfaction of online game
provider, he/she may emerge the intention to change
a game.
Information products often have the characteristic
of network externality. That is, the value of a
technology to a user increases with the number of
adopters [50]. An online game can be classified as a
type of information products and thus it inherits the
characteristic of network externality. The more
players play the same online game and interact with
each other, the greater playfulness of the game they
sense [26; 6]. Previous studies have shown that the
number of participants in an online game positively
influences the entertainment value perceived by the
players [4]. That is, the more gamers participate in an
online game, the much more playfulness they
perceived. Conversely, when players feel there are
insufficient participants in an online game, they may
consider leaving. According to the above discussions,
we hypothesize that:
H1: The lower the perceived enjoyment,
satisfaction with the online game provider, and
perceived insufficient participants in an online game,
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
the greater the likelihood players will switch to
another online game.
3.2. Pull Effects
According to the migration theory, people are
likely to move if destination offers a better quality of
life than their original home, such as the better work
opportunities, higher income, and good schools [41].
Similarly, in service researches, customers are likely
to switch when providers can offer a better service
quality than current one, even though the price is
higher [9; 17; 35]. Online gaming is seen as a
technology to offer people online entrainment
services [26]. Study reported that players switching to
a new game when attractiveness is fresh for whom to
perceive [3]. Hence, we hypothesized that:
H2: The higher the attractiveness of another
online game, the greater the likelihood that
customers will consider switching to it.
3.3 Mooring Effects
Because the complexity of migration decisions, it
is not sufficient to consider push and pull factors
merely independently. Intervening obstacles or
mooring effects were suggested to be considered in
migration [41; 44]. Mooring effects are described as
both cultural and spatial factors through which a
personal gains access to psychological well-being.
Mooring factors are influenced by cultural signals
that are perceived as being important, motivating the
people to stay or migrate to a destination.
The economic condition is a vital factor within
mooring effects. People are likely not to migrate
when the cost is high. In marketing studies, switching
costs are mostly to present as the costs to expense in
switching. Switching costs refer to the costs of
switching from one product to another competing
product [49]. The higher the switching costs, the
more difficult it is to switch brands or products even
though with low adoption costs [13; 21; 37; 38].
While player switches the game, he/she will face lose
of accumulation of current game. Such as, game
experience level, game items can not apply in another
game; hence, players are likely not to switch due to
high switching costs.
The presence of family or friends at a location will
motivate the person to stay put or migrate to a new
location [30]. Online game is a game added to the
Internet system, which allows many players to
interact in virtual space; furthermore, gamers can
organize virtual teams that make players to gather
together for achieve goals (i.e., defeating monsters
and finding treasures), and can offer a place for
players to interact with each other [14]. Therefore,
social interaction need is important factor for
participate the online gaming. Once individual
recognize that he/she belongs to specific team, he/she
will likes to continue playing the game and it will not
be easy to leave.
Prior experience is important factor to migration
too. Individual with migration experience is possible
to migration again compare to a person who without
experience of migration [40]. In case of people have
been moved away the place that he/she grew up, the
experience makes he/she easier to migrate. Because
of overcoming the first time migration situation and
got success, migrants have learned skills for future
migration, and are more likely to migration again
[40]. This is consistent with marketing study finding
that buyers’ prior switching experience influences
their subsequent behavior intentions [22]. Hence,
Players are more likely to switch when they have
prior switching experience. The above statements
lead to our last hypothesis:
H3: The likelihood that players will intend to
switch online game is lower when their switching
costs are higher, their social relationships are strong,
or they have not switch often in the past.
4. Research Method
4.1. Measurement
In the measurement design, all questionnaire items
are adopted from existing literatures and the items are
showed in appendix A with 8 constructs including 28
questionnaire. Items are slightly modified to suit for
the context of online game switching.
In the push effects, the scale items for enjoyment
(EN) were assessed with Ghani and Deshpande’s [23]
enjoyment scale. Satisfaction of online service
providers (SA) was measured with five items from
Oliver and Swan’s [47] scale. Critical participant was
measured with three item scale proposed by Hsu and
Lu [26]. The pull effect was operationalzed as
alternative attractiveness (ATS), which was measured
with Bansal et al. [9] scale. The mooring effects
included switching costs, social relationship, and
prior experience. Switching costs (SC) were assessed
with Ping’s [48] scale. Social relationship (SR) was
measured with four items used by Hsu and Lu [27].
Prior experience (PE) was base on Bansal and Taylor
[8]. Switching intention (SI) was measured by work
of Oliver and Swan. Except two parts were used for
collecting the gamer experience and human
demographic, all items were measured on the 7-point
Likert scales or 7-points differential semantic scales.
4.2. Data Collection
The questionnaire was first discussed with two
professors for verify the context validity. After that, a
pretest was conducted to test the measurement before
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
large survey. Pretest samples were faculties, staffs,
and undergraduate students in a private university of
technology. They were all having experience of
game-playing. The purpose was to access the length
of the measurement and wording of the items. In total,
95 samples were got and the pre-reliability test was
constructed by SPSS 13. All constructs’ Cronbach's
alpha value are greater than 0.8, shown the
instrument with high reliability initially [45].
Empirical data were collected from a field survey.
Samples were self-selected by placing messages on
two large game-related websites in Taiwan, including
Bahamut, (http://www.gamer.com.tw) and Gamebase
(http://www.gmaebase.com.tw). Messages were
announced for seeking volunteer who has online
game experience to be the respondents, especially in
kind of MMORG. The message stated the purpose of
this study, provided a hyperlink to the questionnaire.
As an incentive, cash coupon with value of US$2
dollars was offered to each respondent through mail.
5. Results
The survey yielded 635 responses. After removing
those with unanswered items, we had 574 usable
respondents. Approximately 76 percent of the
respondents were male. The majority of respondents
were 19 years old or less (50%), and 43% were
between 20 and 29 years old. Approximately 86
percent of the respondents were students whose
expenditure on subscription fee was equal or less than
US$10 dollars or less per month.
5.1. Measurement model
SPSS 13 were used for verity the reliability of the
measurement. As shown in Table1, all scales were
adequate given by Nunnally’s suggestions [45]. For
critical participant (CP), Cronbach's alpha value was
0.642, showing medium reliability of items which use
to measure this concept. For all the other concepts,
Cronbach's Į value was exceeding than 0.8. These
results indicated high reliability of measurement.
Table 1. Scale reliabilities
Concepts
Items
Switching Intention (SI)
3
Scale
Reliability
0.946
Enjoyment (EN)
4
0.909
Satisfaction (SA)
5
0.950
Critical Participant (CP)
3
0.634
Switching Costs (SC)
4
0.859
Social Relationship (SR)
4
0.929
Prior Experience (PE)
Attractiveness (ATS)
2
3
0.892
0.808
The confirmatory factor analysis (CFA) was
performed by using LISREL 8.54 with
maximum-likelihood estimation (ML) to test the
construct validity of questionnaire for switching [34].
To access the model, multiple fit indexes were
reported. However, since chi-square may be
inappropriate index to assess the fit of models with
large sample sizes [43], five additional fit indices are
also reported: chi-square/degree of freedom
(Ȥ2/df=2.17, Ȥ2=911.56 df=420) [34], goodness-of-fit
index (GFI=0.90) [34], Normed fit index (NFI
=0.96) , comparative fit index (CFI, CFI=0.98) [10],
root mean square error of approximation
(RMSEA=0.042) [51]. Overall model fit indexes
were better than recommendations then indicated the
CFA model was consistent with the data. More, to
access the discriminate validity by using average
variance value (AVE), one item (CR1=0.3) belong to
critical participant was removed due to it makes AVE
of critical participant less then 0.5. The correlation
matrix to present construct validity was showed in
Table 2.
5.2. Hypothesis test
The structural model was tested by LISREL 8.54
with maximum-likelihood estimation [34]. A
two-step process was followed to assess the PPM
model. First, a second-order factor model was
analyzed, with enjoyment, satisfaction, and critical
participant reflective of the second-order factor
labeled push effects, and switching costs, social
relationship, and prior experience reflective of a
second-order factor labeled mooring effects. The pull
effects construct was captured by the alternative
attractiveness concept alone. Second, the factor
scores of first-order variables, including EN, SA, CP,
SC, SR, PE, and ATS were used to assess the
structural model up to intention. To assess the results
of SEM analysis, constructs were all within
acceptable range shown the good fitness between our
model and sample, except chi-square/degree of
freedom is little higher than suggestion value
(X2=1653.96, P=0.0; df=308, X2/df=5.37, NFI =0.93,
CFI=0.94, GFI=0.92, RMSEA=0.082). Figure2
shows the results of the research model and the
standardized path coefficients of the constructs and
concepts. The pull effects on switching intention was
significant, hence supported hypothesis 2. Mooring
effects has negative force to significantly influence
switching intentions; thus H3 is supported. Besides,
all concepts which we proposed to interpret mooring
effects were all representing the mooring force.
However, the push effects that make players leave a
game was not significant; therefore, H1 is not
supported.
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Table 2. Correlation Matrix of constructs
SI
EN
SA
CR
SC
SR
PE
ATS
SI
0.857*
EN
0.101
0.725
SA
0.020
-0.249
0.792
CR
0.033
0.195
-0.109
0.845
SC
-0.322
-0.104
-0.064
-0.191
0.614
SR
-0.060
-0.266
-0.025
-0.142
0.315
0.725
PE
0.418
0.012
-0.084
0.036
-0.191
0.096
0.815
ATS
0.300
0.086
-0.095
-0.091
-0.032
-0.030
0.095
0.604
*Diagonals represent the AVE value (average variance extracted), while the other matrix entries
indicate the squared correlations.
EN
0.8*
0.31**
(2.4)
SA
Push
0.24
(2.34)
CP
0.05
(1.07)
0.45*
SC
-0.81***
(-10.32)
SR
0.11
(2.46)
Mooring
0.24
(2.34)
PE
SI
0.09
(2.56)
Pull
1.0*
ATS
Figure 2. Results of SEM analysis
* fixed
** t-value in parentheses, >1.96 is significant, path
coefficients are outside.
*** path coefficient with negative value indicate a
negative relationships between variables.
6. Conclusion and Limitations
The purpose of this study was to extend the PPM
model to explain the online gamers’ switching
intention in the virtual worlds (online games).
Specifically, “mooring effects” have a stronger
influence on the switching intentions of online game
players than “pull effects”. However, “push effects”
do not have a significant influence on switching
intentions.
Mooring efforts have the most influence on online
gamers’ defection. In switching literatures, mooring
effects are frequently represented by switching costs
[13; 17; 25; 33]. Our study proposes factors like
social relationship and prior experience also represent
the mooring effects. These factors have not received
much attention in the literature related to online
service; however, in this study, we have shown that
they are important aspects of mooring effects. Future
online service research should investigate these
variables further.
Because gamers are usually students without high
income, they do not have a lot of money to spend on
online games. Intuitively, switching cost should not
be an important factor in the mooring effects.
However, to our surprise, the switching cost with a
factor loading of 0.45 is the most important factor in
the mooring effects. This indicates that, even though
gamers do not spend great deal of money on online
games, they still consider switching between games
may generate high switching costs. This is because
switching costs also include the value of property in
the virtual world, such as game skill levels and
accumulated powerful items, which would be lost by
switching to another game [12]. It is likely that most
gamers are like teenagers tend to seek attention and
recognition by playing game. For example, when the
gamers reach a higher gaming level, they gain the
respect of their fellow players. Hence, online game
player are willing to invest great deal of efforts in a
game in order to win the respect and acceptance of
their fellow players. However, switching to a new
game would means losing the respect and property
accumulated in the current game, which may lead to
feeling of regret.
Pull effects have the second strongest influence on
online gamers’ switching intentions. This study
characterizes the pull effect as “alternative
attractiveness” and measures it in the questionnaire in
terms of the enjoyment, value, and satisfaction of a
new online game. These measurements were taken
from the literatures relevant to game-playing and
service switching [8; 26; 33]. The result of this study
suggest that online game providers need to attract
players by using the following “pull-forces” to draw
players away from their current games: design an
online game with playfulness and novelty; lower the
subscription fee or even offer free online game; and
offer toll-free hot line for solving the problems that
arise in a game.
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Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Unexpectedly, push effects do not significantly
influence the switching intentions. That is, players
will stay with the current online game, even when
they perceive the “push-force”. The probable reason
is that players do not want to lose the property
accumulated in the current game and they may
choose to reduce the frequency of login or spend less
time to play the current game.
Though this study provides several insights into
online game switching decisions, it has several
limitations. Our study used the convenience sampling
approach to collect data; hence, the potential
applications of the findings are limited. Due to
budget limitations and time constraints, we could not
collect more samples. In addition, it is also possible
that some other factors may be added in construct of
pull effect. Further research should focus on this
construct for formulate much “push forces” in order
to attract players to switch.
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8
Proceedings of the 42nd Hawaii International Conference on System Sciences - 2009
Appendix A
Survey Instrument
Switching Intention (SI)
Rate the possibility that you would switch from “current
game” to a new game within 6 months (please answer
below)
Unlikely…Likely
Improbable…Probable
No chance…Certain
Prior Experience (PE)
PE1. In the past, I have often switched online games
(SD… SA)
PE2. I have a lot of experience in switching online games
(SD…SA)
Enjoyment (EN)
Please describe your enjoyment feelings while online
game-playing (please answer below)
Interesting…Uninteresting
Fun…Not fun
Exciting…Dull
Enjoyable…Not enjoyable
Satisfaction (SA)
Overall, how do you feel about the service provided to you
by “my online game provider”?
Displeased…Pleased
Disgusted…Contented
Dissatisfied…Satisfied
They do a poor job…They do a good job
Unhappy…Happy
Critical Participant (CP)
CP1. Most people in my group play an online game
frequently (SD…SA)
CP2. Most people in my community play an online game
frequently (SD…SA)
CP3. Most people in my class/office play an online game
frequently (SD…SA)
Attractiveness (ATS)
ATS1.ATS1.All in all, competitors would be much more
fair than “my current online game provider” (SD…SA)
ATS2.Overall, competitors’ policies would benefit me
much more than “my current online game provider’s”
policies (SD…SA)
ATS3. I would much more satisfied with the service
available from competitors than the service provide by
“my current online game provider” (SD…SA)
Switching Costs (SC)
SC1. On the whole, I would spend a lot of time and money
to switch from “my current online game” (SD…SA)
SC2. Generally speaking, the costs in time, money, effort,
and grief to switch from “my current online game”
would be high (SD…SA)
SC3. Overall, I would spend a lot and lose a lot if I
switched from “my current online game” (SD…SA)
SC4. Considering everything, the costs to stop playing
“my current online game” and start up with a new
online game would be high (SD…SA)
Social Relationship (SR)
SR1. I fit in well with the online game members (SD…SA)
SR2. I like the members of the online game (SD…SA)
SR3. In general, online game members act as a cohesive
unit (SD…SA)
9