Information & Management 48 (2011) 313–319

Understanding use continuance in virtual worlds: Empirical test of a researchmodel

Stuart J. Barnes 1,*

Norwich Business School, University of East Anglia, Chancellor’s Drive, Norwich, Norfolk, NR9 4DE, United Kingdom

A R T I C L E I N F O

Article history:

Received 23 June 2009

Received in revised form 31 January 2011

Accepted 25 July 2011

Available online 31 August 2011

Keywords:

Virtual world

Continuance intention

Automatic behavior

Habit

Hedonic

Utilitarian

Second Life

A B S T R A C T

We decided to examine why people continue to use virtual worlds by studying a real example: the

popular virtual world of Second Life. This involved building and testing a model of virtual worlds based

on habit from both the habit/automaticity and instant automaticity perspectives, the latter based on

utilitarian and hedonic goals. The results (for a sample of 339 users) suggested that continuance

intention for the virtual world was driven by perceived usefulness, enjoyment, and both perspectives of

automatic behavior, which together provide considerable explanatory power for both habit and

continuance intention. We conclude with implications for practice in this new area of inquiry.

� 2011 Elsevier B.V. All rights reserved.

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1. Introduction

The notion of a ‘virtual world’ is a relatively new phenomenonthat has emerged from the juxtaposition of recent advancements incomputer graphics, online gaming, and social networking technol-ogies. A ‘virtual world’ may be defined as ‘‘an electronicenvironment that visually mimics complex physical spaces, wherepeople can interact with each other and with virtual objects, andwhere people are represented by animated characters’’ [2]. Suchideas were, until relatively recently, restricted to rather crudegraphical representations with limited interactivity. However,virtual worlds have become very sophisticated and increasinglyrealistic – often having advanced 3-D rendering, in-worldcurrencies, avatar and object customization and development,property ownership and permissions, text and voice communica-tion, and social networking tools – and the number of virtualworlds has increased dramatically in the past five years.

If we restrict our discussion to virtual worlds that are notmassively multiplayer online games [4], there were well over 100virtual worlds in existence or under development as of the end of2010: 37 worlds aimed at the under 10 s (the largest being

* Tel.: +44 1603 456161; fax: +44 1603 593343.

E-mail address: stuart.barnes@uea.ac.uk.1 The help of Mario Menti from GMI Inc. is gratefully acknowledged in this

project.

0378-7206/$ – see front matter � 2011 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2011.08.004

Poptropica with 110 million users and Moshi Monsters with 22million), 31 aimed at the groups from 10 to 14 years (the largestbeing Stardoll with 69 million users followed by Neopets with 63million), 28 aimed at the groups from 15 to 25 (including Habbowith 175 million users and IMVU with 57 million), and 16 aimed atthe over 25 s (the largest being Second Life with 22 million users).Some of these are centered around a specific brand (e.g. BarbieWorld and Lego Universe) while others are more general (e.g.,Multiverse and Blue Mars). Further, they vary considerably in theircontent focus, including content creation (e.g. Second Life andActive Worlds), Socializing/chat (e.g. Utherverse and IMVU),fashion/lifestyle (e.g. Frenzoo and Stardoll), education (e.g.Whyville and Medikidz), music and media (e.g. vMTV and vSide),sports (e.g. Football Superstars and Empire of Sports), and gaming(e.g. Gaia and Club Penguin).

Second Life, the well known, broadest, virtual world platform,began life in 2001. It has grown rapidly from less than 53,000registered accounts at the end of 2005 to more than 20 million atthe end of 2010. During 2009, user-to-user transactions totaledUS$567 million. Residents owned 2.08 thousand square kilometersof land and spent 105 million hours in Second Life in quarter 3 of2010, more than 26 times that of quarter 3 of 2005. Maybe there iscontinuous use of virtual worlds but in quarter 3 of 2010 only789,000 unique residents of Second Life logged-in more than onceper month.

Virtual worlds can be considered to be IS [27]. However, thenature of virtual worlds is quite different from that of traditional IS.

S.J. Barnes / Information & Management 48 (2011) 313–319314

They are subtly different from massively multiplayer online role-playing games (MMORPG) such as World of Warcraft andRuneScape [3]. Both share the same characteristics, but MMORPGtypically provide pre-defined user paths while virtual worldsattempt to provide strong levels of freedom in a simulated world inwhich participants use their imagination to follow their own pathin the virtual environment [12]. Herein lies the complexity ofvirtual worlds. Virtual worlds emerged from the MMORPGtradition where games were originally designed to providepleasurable experiences and user self-fulfillment, and can havean element of hedonic IS. However, recent social virtual worlds alsoperform a more utilitarian role, enabling many tasks such ascollaboration, entrepreneurial or commercial endeavor, customi-zation and creation of objects and other content, development ofsimulated environments, and educational and training, as well associal networking, possibly fulfilling both hedonic and utilitarianmotives (for example, utilitarian in an education or businesscontext but hedonic when used with friends). Thus virtual worldsare in fact multipurpose IS [15].

Academic research into virtual worlds is still at an early stage.Much of the research has focused on conceptual discussion, review,etc. [9,21]. One area of research that has proved popular was that ofusing virtual worlds in education. One of the few empirical ISstudies in this area was that of Phang and Kankanhalli [22], whoapplied flow and social translucence of technology theories tomodel the individual and technology-related perceptions thatinfluence learning outcomes. Another area examined was market-ing and consumer behavior in virtual worlds [13]. Most of thesestudies were, however, limited.

The study by Guo and Barnes developed a comprehensivemodel for explaining the purchasing of virtual items in Second Lifebased on extrinsic and intrinsic motivators (n = 250). Similarly,Verhagen et al. used a large-scale survey (n = 1627) in Second Lifeto examine attitudes towards the use of virtual worlds using bothintrinsic and extrinsic drivers, finding that both are important inexplaining attitudes.

A few recent studies have also attempted to examine theacceptance of virtual worlds by users. Shen and Eder [24]completed a small-scale study of Second Life user acceptance(n = 77) using an extended TAM model and concluded that, fortheir small sample, the results had limited generalizability.Similarly, Fetscherin and Latteman [11] developed a TAM-basedmodel of Second Life acceptance. However, although a reasonablesample was collected (n = 249), some aspects of validity were weakand R2 values were not reported. To the best of the author’sknowledge, no study has yet attempted to explain the continuanceof virtual world use using empirical data.

From a practical point of view, given that there are an increasingnumber of choices of virtual worlds and that their financial successdepends on building a critical mass of users that return to thevirtual world [28], an important area of investigation is howcontinuance behavior is developed. For example, although SecondLife has a large number of reported residents, its number ofcontinuing residents is a fraction of that amount. The reason forthis is unclear.

Thus, the research question for the study is: ‘‘Why do userscontinue their usage of virtual worlds?’’

We provided a unified habit model for explaining continuancebehavior in the unique context of virtual worlds. Our aim was totest a unified theory based on the two key theoreticalperspectives of habit formation – habit/automaticity [16] andinstant automaticity [1], which combines drivers from twoperspectives: utilitarian (perceived usefulness) [17] and hedonic(enjoyment) [25]. Clearly, understanding use continuance be-havior will help to drive the development and growth of virtualworld platforms.

We adopted a hypothetico-deductive approach in the positivisttradition, because we are testing whether a unified model, with anexisting theoretical base, could help to explain behavior in virtualworlds.

2. Theory and research model

IS continuance behavior that occurs after an IS use becomesunconscious behavior and thus a normal routine activity [6,20]. Itcomes as the result of several individual decisions to continueusing a particular IS [18]. A key element in the literature inexplaining continuance behavior is automatic behavior or habit,which has become a key construct in explaining continuancebehavior [19]. The overall foundation for the model is the salienttheoretical debate on habitual behavior, which suggests two mainperspectives: the instant activation perspective and the habit/automaticity perspective. These perspectives are here combinedinto a single, unifying model and further combine the utilitarianand hedonic views to derive antecedents of habit under the instantautomaticity perspective.

The hedonic and utilitarian goals in IS behavior are integratedinto the instant automaticity habit theory using constructs fromthe most robust acceptance theories. TAM assumes that perceivedusefulness is the principal utilitarian driver of behavior [29]. This isa core determinant of behavioral intention. Similarly, hedonicgoals have largely focused on the construct of enjoyment. Thesetwo constructs are also supported as determinants of behavioralintention by recent research on virtual world user behavior.

2.1. Utilitarian user acceptance

Based largely on a utilitarian view, traditional models of useracceptance of IS have included perceived usefulness and perceivedease of use as key variables influencing behavioral intention to use.Perceived usefulness is a strong predictor of behavioral intentionsin many contexts. In addition to the traditional application of theconstruct to business contexts, it has also been shown to be apredictor in new media contexts including the Internet, the Weband mobile commerce. However, while perceived usefulness hasconsistently proven to be an important construct in longitudinaladoption to post-adoption behavior and in prior continuancestudies, perceived ease of use has not. As Bhattercherjee stated:‘‘ease of use has an inconsistent effect. . . which seems to furthersubdivide and become non-significant in later stages’’. This hasbeen confirmed in numerous other studies. Hence, we did notinclude perceived ease of use in our model.

Thus, from the utilitarian perspective, we proposed:

H1. Perceived usefulness positively influences continuance intention.

2.2. Hedonic user acceptance

At the individual user or consumer level, the focus of this study,virtual worlds can be construed as having a hedonic element whereusers experience pleasure or fun when using the system. Thesemay include socializing, romantic encounters, shopping fordesirable personal items, playing games, fantasy, the creation ofan alter-ego and other experiences that may contribute to buildingself-esteem. Recently, several authors have argued that virtualworlds should be construed as multipurpose IS because theycontain both hedonic and utilitarian components [14].

Individuals often seek sensations on multiple sensory channels,and virtual worlds can provide such experiences. This is a traitcharacteristic of hedonic IS in general – developers may employanimated images, colors, sounds, and esthetically appealing visuallayouts to encourage prolonged use.

S.J. Barnes / Information & Management 48 (2011) 313–319 315

The consumer behavior literature has determined strongsupport for repurchase and use intentions driven by bothutilitarian and hedonic value in products or services [5].

Thus, we proposed:

H2. Enjoyment positively influences continuance intention.

Fig. 1 depicts the hedonic and utilitarian views of usecontinuance. Both of these views are included in the researchmodel.

2.3. Habit

Use continuance can be predicted by the extent to which abehavior has become automatic because of prior learning; thushabit is considered a moderator between intention and actualbehavior, with a direct effect on actual behavior and an indirecteffect on behavior that determines intentions. Continuanceintentions have shown to be an effective proxy for measuringactual behavior [26]. Thus we posit:

H3. Habit positively influences continuance intention.

Explanations of how habit is formed have been provided via twomain perspectives. The prevailing view has been a reason-orientedframework in which user evaluations determine user intentions touse. Under this Instant Activation Perspective (IAP), automaticprocessing is an accelerated type of conscious processing wheregoals are effortlessly retrieved. Virtual worlds have both utilitarianand hedonic goals as core drivers under the IAP. Many studies havesuggested relationships between goal-directed behavior and habit.Hence, if a user enjoys an experience with a virtual world he or sheis more likely to be positive and decide to engage in such behaviorin the future. Thus, we posit:

H4. Habit is positively influenced by perceived usefulness.

H5. Habit is positively influenced by enjoyment.

In contrast to the IAP view, contemporary research inpsychology has questioned the comprehensiveness of traditionalmodels in providing an accurate account of automatic use. In theHabit/Automaticity Perspective (HAP), repetition of the samebehavior over time creates a set of mental links that are hard-wired, linking situational cues and behavior. Thus behavior canoccur without the process of establishing associated goals; this istermed ‘‘habitual goal-directed consumer behavior.’’ A key aspectof the HAP is that habit is reinforced with more use and thisdisplaces conscious evaluation. Under this view, if a user of a

Fig. 1. Hedonic and utilitarian views of behavior; different perspectives on habit

formation and behavior.

virtual world has a high frequency of past behavior then automaticprocessing drives behavior and conscious processing becomes lessimportant. Thus we proposed:

H6. Habit is positively influenced by frequency of prior usage.

Fig. 1 summarizes the two different perspectives on habitformation in our research model. The creation of habit requires astable context conducive to its formation through repetition orpractice; we believe that such a context exists when focusing onindividuals’ behavior with respect to a single system.

In addition to the core hypotheses of our model, it includedthree control variables that could potentially affect IT users’reactions to a virtual world: user age and gender and target systemexperience, which has been shown to reduce the relationshipbetween evaluations and intentions. All three of these relation-ships were modeled as direct relationships on continuanceintention, while system experience was also modeled as amoderator, in line with Kim et al.

The full research model tested is shown in Fig. 2.

3. Study design and method

3.1. Data collection

Data collection involved the use of two traffic ‘bots’ in SecondLife operating at busy traffic points. Each was essentially an avatarthat delivered a survey advertisement and a URL in text form.Details of the survey were provided and respondents were asked toIM (instant message) the bot. Thus respondents initiated contactand were given details of the survey and the URL at which theycould obtain it. The Web survey used QuestionPro. To collectsufficient responses, each bot was placed at a high-traffic locationselected from Second Life’s ‘popular locations’ list: Freebies Worldand Platinum World. The locations were chosen to be as generic aspossible (providing content to appeal to both genders and differentages); each focused on providing both free and paid-for digitalcontent and on generating traffic through paid ‘camping’ activities(where individuals are paid small amounts of money for time spent‘sitting’ at a particular location).

A small monetary incentive (250 Linden Dollars, the in-worldcurrency of Second Life, worth approximately US$0.95) wasprovided to respondents of each completed survey. A non-conditional incentive was used, since there was evidence thatthis was likely to improve response rates and that incentives didnot bias sample composition or data quality and were more likelyto attract hard-to-reach groups, by providing motivation.

The survey ran for approximately six months. After removingduplicates and invalid responses we had a total of 339 surveyresponses (duplicate responses were captured in QuestionPro andhad the same response details). A test for response bias for earlyand late respondents to the survey was conducted, examining

Fig. 2. Research model. Note: *** denotes p < 0.001; ** denotes < 0.01 and * denotes

p < 0.05.

Table 1Survey items for testing the model.

Survey item Source

Frequency of prior usage [18]

1. In the last 7 days, how much time would you say you spent

using Second Life?

2. In the last 7 days, how many times did you use Second Life?

Perceived usefulness [18]

1. Second Life is of benefit to me

2. The advantages of Second Life outweigh the disadvantages

3. Overall, using Second Life is advantageous

Continuance intention [6]

1. I intend to continue using Second Life rather than use any

alternative technology

2. My intentions are to continue using Second Life rather than

use any alternative technology

3. If I could, I would continue my use of Second Life

Habit [18]

1. Using Second Life has become automatic to me

2. Using Second Life is natural to me

3. When faced with a particular task, using Second Life is an

obvious choice for me

Enjoyment [25]

1. I would describe my overall experience of using Second

Life as...

2. Enjoyable to disgusting

3. Exciting to dull

4. Pleasant to unpleasant

5. Interesting to boring

Table 2Respondent characteristics.

Characteristic Frequency Percent

Gender

Male 198 58.4%

Female 141 41.6%

Age

18–24 years 114 33.6%

25–34 years 102 30.1%

35–44 years 72 21.2%

45–54 years 36 10.6%

55–64 years 13 3.8%

65+ years 2 0.6%

How long have you been using Second Life?

Less than 1 month 115 33.9%

More than 1 and less than 3 months 61 18.0%

More than 3 and less than 6 months 47 13.9%

More than 6 and less than 12 months 56 16.5%

More than 1 year and less than 2 years 46 13.6%

More than 2 years 14 4.1%

In the last 7 days, how much time would you say you spent using Second Life?

1. Less than 1 h 17 5.0%

2. Between 1 and 4 h 80 23.6%

3. Between 4 and 10 h 96 28.3%

4. Between 10 and 30 h 70 20.6%

5. Between 30 and 60 h 44 13.0%

6. More than 60 h 32 9.4%

In the last 7 days, how many times did you use Second Life?

1. Not at all 6 1.8%

2. Once 25 7.4%

3. Twice 28 8.3%

4. Three or four times 72 21.2%

5. Five or six times 68 20.1%

6. Seven or more times 140 41.3%

S.J. Barnes / Information & Management 48 (2011) 313–319316

gender, age, and time using Second Life (SL). No significantdifferences were found between the two groups.

3.2. Measurement

All constructs and scale items used here were adopted frompreviously validated sources; Table 1 shows the measurementitems and sources of each.

3.3. Data analysis

The PLS path modeling technique with reflective indicators inSmart-PLS 2.0 [23] was used to assess validity and reliability of thedata. This is better equipped to handle formative measures andmoderating relationships [7,8]. Modeling moderating relation-ships (such as controls) in PLS requires adding moderatingvariables as direct relationships to outcome variables and thencalculating interaction variables based on the predictor variables.PLS requires minimal demands on measurement scales, sampledistribution, and sample size. It excels at causal-predictiveanalysis, in which hypothesized relationships are complex.

4. Results

4.1. Respondent characteristics

Table 2 shows the demographics of the sample. Of the 339responses, approximately three-fifths were male. The median agewas 25–34 years, with only a third of the sample being 35 years orover. The average use experience was between one and threemonths, with around two-thirds using the virtual world for lessthan six months. Actual usage was high, with a median usage ofbetween 4 and 10 h per week, over five or six sessions.

4.2. Tests for validity and reliability of the measures

Table 3 examines convergent validity. The loadings of themeasures on their respective constructs ranged from 0.877 to0.946, with all being significant at the 0.1% level. Further, all of theconstructs fulfill the recommended levels for reliability (measured

by composite reliability and Cronbach’s a) and average varianceextracted (AVE), for which all values were higher than therecommended cutoff of 0.50; thus, all the values of AVE arestrong. Similarly, the values of composite reliability and Cron-bach’s a are very good, being well above the recommendedminimum reliability values of 0.70, with all but one item forCronbach’s a above the 0.8 mark.

Tables 4 and 5 show discriminant validity. A standard test wasused: the square root of average variance extracted for eachconstruct was compared with the correlations between it and otherconstructs. Each construct shared greater variance with its ownmeasurement items than with constructs having different mea-surement items. We then used the cross-loading method to showthat measurement items loaded higher on their own construct thanitems for other constructs using a confirmatory factor analysis.

In order to ensure that multicollinearity was not an issue wecomputed the variance inflation factor (VIF) between each of thevariables, running separate analyses for one variable as thedependent variable and all others as independent variables. Thevalues of VIF ranged from 1.21 to 2.34 and the values of toleranceranged from 0.43 to 0.83. None of the values of the VIF exceededthe recommended maximum level of 5, nor did any of the tolerancelevels fall below the suggested cut-off of 0.2; thus multicollinearitydid not appear to be an issue.

Common method bias was examined using Harman’s singlefactor test. The first factor explained only 35% of the variance andthus common method bias did not appear to be present.

Overall, these tests provided us with a high degree of confidencein the scale items used in testing our model. Descriptive statisticsare given in Table 6. Note that the scores of items for frequency ofprior behavior range from 1 to 6. Other construct variables rangefrom 1 to 7, using a Likert scale. All mean scores across variableitems were above the neutral score, with enjoyment (mean = 5.6)and continuance intention (mean = 5.1) scoring over 5. Standarddeviations for the measures ranged from 1.23 to 1.45.

Table 4Correlations between constructs (diagonal elements are square roots of the average variance extracted).

CONTINT ENJOY PRIORUSE HABIT AGE GEN EXP PU

Continuance Intention (CONTINT) 0.904

Enjoyment (ENJOY) 0.433 0.930

Frequency of Prior Use (PRIORUSE) 0.364 0.282 0.897

Habit (HABIT) 0.645 0.405 0.448 0.896

Age (AGE) 0.062 0.035 0.082 0.017 n.a.

Gender (GEN) 0.110 0.115 0.110 0.109 �0.154 n.a.

System Experience (EXP) 0.054 0.069 0.201 0.229 0.118 �0.117 n.a.

Perceived Usefulness (PU) 0.691 0.453 0.369 0.667 0.056 0.110 0.158 0.909

Note: Age, gender and system experience are single-item measures.

Table 3Psychometric table of measurements.

Construct Item Loading St. Error. t-Value

Continuance intention

AVE = 0.818 Continuance Intention 1 0.924 0.012 81.50

CR = 0.931; CA = 0.889 Continuance Intention 2 0.912 0.013 71.19

Continuance Intention 3 0.877 0.017 50.52

Enjoyment

AVE = 0.865 Enjoyment 1 0.937 0.011 85.90

CR = 0.962; CA = 0.948 Enjoyment 2 0.926 0.011 88.59

Enjoyment 3 0.946 0.008 115.68

Enjoyment 4 0.910 0.016 58.22

Habit

AVE = 0.803 Habit 1 0.891 0.017 49.80

CR = 0.924; CA = 0.877 Habit 2 0.917 0.011 85.50

Habit 3 0.879 0.014 68.82

Perceived usefulness

AVE = 0.827 Perceived Usefulness 1 0.920 0.010 91.01

CR = 0.935; CA = 0.895 Perceived Usefulness 2 0.894 0.016 52.38

Perceived Usefulness 3 0.914 0.010 92.47

Frequency of prior use

AVE = 0.804 Prior Use 1 0.909 0.013 74.38

CR = 0.892; CA = 0.757 Prior Use 2 0.885 0.020 43.61

Note: CR = Composite reliability; CA = Cronbach’s Alpha; AVE = Average variance extracted.

Table 6Descriptive statistics of construct variables.

N Minimum Maximum Mean Std. Deviation

PRIORUSE1 339 1 6 3.4 1.36

PRIORUSE2 339 1 6 4.7 1.36

CONTINT 339 1.00 7.00 5.1 1.24

PU 339 1.00 7.00 4.8 1.23

HABIT 339 1.00 7.00 4.6 1.45

ENJOY 339 1.00 7.00 5.6 1.41

Table 5Loadings and cross-loadings for measures.

Continuance intention Enjoyment Habit Perceived usefulness Frequency of prior use

CONTINT1 0.924 0.406 0.594 0.616 0.346

CONTINT2 0.912 0.345 0.581 0.613 0.306

CONTINT3 0.877 0.421 0.575 0.644 0.335

ENJOY1 0.389 0.937 0.366 0.408 0.254

ENJOY2 0.421 0.926 0.385 0.416 0.264

ENJOY3 0.423 0.946 0.409 0.448 0.285

ENJOY4 0.374 0.910 0.342 0.411 0.242

HABIT1 0.552 0.376 0.891 0.553 0.446

HABIT2 0.608 0.360 0.917 0.630 0.403

HABIT3 0.573 0.353 0.879 0.611 0.356

PU1 0.602 0.419 0.621 0.920 0.389

PU2 0.628 0.410 0.567 0.894 0.299

PU3 0.654 0.407 0.630 0.914 0.319

PRIORUSE1 0.317 0.215 0.423 0.300 0.909

PRIORUSE2 0.338 0.295 0.379 0.366 0.885

S.J. Barnes / Information & Management 48 (2011) 313–319 317

4.3. Test of the research model

Fig. 3 presents the results of PLS path modeling (CentroidWeighting Scheme) in Smart-PLS. The shaded items are those thathave been added to Bhattacherjee’s continuance model. A poweranalysis in G*Power 3.0 [10] shows that the sample (n = 339) issufficient for explaining even small population effects (f2 � 0.059;a = 0.05; b = 0.2) in our model, with a power of 0.999 for moderate

Fig. 3. Results of PLS analysis.

S.J. Barnes / Information & Management 48 (2011) 313–319318

population effects (f2 = 0.15). The sample size is not sufficient for astrong explanation with covariance-based path modeling, where itonly explains medium to large population effects.

The data supports all relationships in the model. Habit appearsto be significantly determined by both the instant automaticity andhabit/automaticity perspectives. From the IAP, perceived useful-ness is a significant determinant of habit (b = 0.542; t = 11.40;p < .001) while enjoyment is significant but to a lesser extent(b = 0.097; t = 2.29; p < .05). From the HAP, the frequency of priorusage behavior is also a significant determinant of habit (b = 0.221;t = 5.04; p < .001). Together, these two perspectives explain 50.0%of the variance in habit.

Overall, the research model explains a considerable level ofvariance in continuance intention – 57.2%. All three positeddeterminants contribute to explaining behavioral intention tocontinue use. In sum, users build their behavioral intentions tocontinue to use the virtual world of Second Life based on autilitarian assessment of perceived usefulness, a hedonisticassessment of enjoyment, and a disposition of habit, apparentlybuilt on both cognitive and automatic processes.

Clearly, system experience reduced the effect of enjoyment oncontinuance intention; value judgments about enjoyment becameless important for more experienced users. Similarly, moreexperienced users had generally less intention to continue usingthe virtual world.

5. Discussion and conclusions

5.1. Implications for theory

Our study examined use continuance in the context of virtualworlds: specifically Second Life. The results of hypothesis testingare shown in Table 7. To the best of the researcher’s knowledge,this model is the first to attempt to combine habit theories to

Table 7Results of hypothesis testing.

Hypothesis Result

H1: Perceived usefulness positively influences continuance intentionConfirmed

H2: Enjoyment positively influences continuance intention Confirmed

H3: Habit positively influences continuance intention Confirmed

H4: Habit is positively influenced by perceived usefulness Confirmed

H5: Habit is positively influenced by enjoyment Confirmed

H6: Habit is positively influenced by frequency of prior usage Confirmed

explain continuance behavior in virtual worlds. Using a sample of339 respondents we found strong support for the validity andreliability of all constructs. Furthermore, our model is tested usingPLS: all three of the determinants of continuance intention(perceived usefulness (H1), enjoyment (H2) and habit (H3)) wereclearly supported in the model, along with the extended relation-ships proposed to explain habitual behavior (H4, H5 and H6).

Our research offers strong support for both of the theoriesoffered to explain habitual behavior in the context of systems use:the instant automaticity perspective (IAP), tested via H4 and H5,and the habitual/automaticity perspective (HAP), tested via H6.Numerous other studies have found that past use is related tosubsequent use; few have attempted to compare the IAP with theHAP. Kim et al. found that HAP was the strongest of the habittheories, finding that past use strongly correlates with habit/automaticity. We did not find that the latter perspective exhibitedmuch stronger effects than the former, but that both perspectivesplayed an important role in forming automatic behavior in virtualworld users. Together, these provide a comprehensive explanationof habit (R2 = 0.500).

Our study also lent support to both the utilitarian and hedonicviews of system use in virtual worlds. Interestingly, the utilitarianview was dominant over the hedonic view. This suggests thatSecond Life is more utilitarian than hedonic.

System experience played a significant role in moderating theeffect of enjoyment on continuance intentions, and it also had asignificant direct relationship with intentions. The moderatingeffect of system experience in reducing the relationship betweenevaluations and intentions has been reported in other contexts.Apparently, more experienced users do not seek enjoyment as adriver of future continuance intention to the same extent as lessexperienced users. Perhaps hedonic value is a driver for initialadoption of the system but that this falls off with experience and isreplaced by other goals.

5.2. Implications for practice

Our research has provided some clear implications for virtualworlds in terms of how to build continuance in users. Althoughhedonic value is significant, the largest drivers appear to be utilitarianvalue and both types of habit formation. Developers should createfunctions that provide unambiguous utilitarian benefits that drivecontinuance directly and through habit. Hedonic interactions areimportant, but certainly not at the expense of utilitarian benefits.

S.J. Barnes / Information & Management 48 (2011) 313–319 319

Users of virtual worlds clearly place an emphasis on hedonicand utilitarian value derived, and these are areas within whichdevelopers can contribute to the success of virtual worlds. Otheraspects, such as habit, are dispositions of the individual and are notdirectly as controllable; however, they can be manipulatedindirectly through the design of the virtual world. Applicationswith clear value to users are important in driving high frequency ofuse and thereby habit and continuance via HAP. Similarly, strongfunctionality that provides instrumental value contributes strong-ly to building habitual behavior via IAP.

Utilitarian value can be enhanced by creating additionalinstrumental value for users through increased functionality andcontent. This could include, for example: tools for communicationand collaboration, for entrepreneurial or commercial endeavors,utilities for customization, and creation of objects and othercontent.

Hedonic value can be enhanced by adding animated images,colors, sounds, etc. Interfaces should be fluid and intuitive –thereby creating greater possibilities for automatic behavior.Further, interfaces and content should create pleasure and arousalin the user, evoking positive emotional reactions.

5.3. Limitations

Our research is limited to the extent that we have focused solelyon a single system. However, we believe that our results aregeneralizable beyond Second Life. Our study can also be consideredlimited in terms of aspects of our sample. Firstly, the size of thesample may be questioned. It demonstrated extremely strongexplanatory power for moderate population effects (f2 = 0.15;power = 0.999), but was limited to moderately small populationeffects overall (power > 0.80 for f2 > 0.059). Related to this issue,the mode of data collection, traffic bots linked to a Web survey,could be considered limited because the actual population isunknown and the sampling approach was self-selection. The ageprofile of the sample may, for example, have limited the strength ofthese moderating relationships. We had no control over thedemographic or other characteristics of the respondents.

5.4. Conclusions

This paper represents an early attempt to understand behaviorin virtual worlds from an IS perspective. There is much to learn inthis new environment and what is clear is that IS theory needs todevelop further to assess virtual world behavior. Virtual worlds areclearly quite different from other domains, which tend to be largelyutilitarian in use motivation. Further, they appear too complex tobe the focus of one discipline.

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Stuart J. Barnes is Chair and Professor of Management

in the Norwich Business School at the University of East

Anglia. He received his PhD from Manchester Business

School. His primary research interests center on the

successful utilization of new information and commu-

nications technologies by businesses, governments and

consumers. He has published five books (one a best-

seller for Butterworth-Heinemann) and more than a

hundred and fifty articles including those in journals

such as Communications of the ACM, the International

Journal of Electronic Commerce, Communications of

the AIS, Data Base, and Information & Management.