Knowledge Work Through Social Media Applications: Team Performance Implications of Immersive Virtual Worlds

This article was downloaded by [RMIT University]On 24 September 2013 At 1509Publisher Taylor amp FrancisInforma Ltd Registered in England and Wales Registered Number 1072954 Registeredoffice Mortimer House 37-41 Mortimer Street London W1T 3JH UK

Journal of Organizational Computing andElectronic CommercePublication details including instructions for authors andsubscription informationhttpwwwtandfonlinecomloihoce20

Knowledge Work Through Social MediaApplications Team PerformanceImplications of Immersive Virtual WorldsMark E Nissen a amp Richard D Bergin aa Naval Postgraduate School Monterey California USAAccepted author version posted online 20 Nov 2012Publishedonline 04 Mar 2013

To cite this article Mark E Nissen amp Richard D Bergin (2013) Knowledge Work Through Social MediaApplications Team Performance Implications of Immersive Virtual Worlds Journal of OrganizationalComputing and Electronic Commerce 231-2 84-109 DOI 101080109193922013748612

To link to this article httpdxdoiorg101080109193922013748612

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Journal of Organizational Computing and Electronic Commerce 23 84ndash109 2013 ISSN 1091-9392 print 1532-7744 online DOI 101080109193922013748612

KNOWLEDGE WORK THROUGH SOCIAL MEDIA APPLICATIONS TEAM PERFORMANCE IMPLICATIONS OF IMMERSIVE VIRTUAL WORLDS

Mark E Nissen and Richard D Bergin

Naval Postgraduate School Monterey California USA

Knowledge is key to competitive advantage and the social reach of knowledge can amplify its power in terms of organizational performance Leveraging such amplification social media applications are expected broadly to improve the performance of organizational work which highlights teams groups and organizations as particularly appropriate units of analshyysis The technological intermediation of knowledge work within and among such social aggregations of people remains a complex endeavor however This pertains in particular to immersive 3D environments supporting virtual social and game worlds As with other social media applications such virtual worlds can amplify knowledge broadly but they also offer potential to approach the sensations of face-to-face (F2F) interaction in terms of presshyence copresence psychological engrossment and affective experience In this exploratory study we employ both quantitative and qualitative research techniques to investigate how teamwork performed through virtual worlds can approach that accomplished via F2F intershyactions This leads to important findings insights guidelines and suggestions for expanded research as well as enhanced practice

Keywords competitive advantage immersiveness information systems knowledge exchange presence social media technological intermediation virtual world

1 INTRODUCTION

A great many scholars have long viewed knowledge as key to competitive advantage (Cole 1998 Grant 1996 Spender 1996) Nissen (2006) explained how knowledge enables effective action effective action drives superior performance and superior performance supports competitive advantage Both tacit and explicit knowledge are considered important widely whether together as separate dimensions or apart as complementary types (Keane and Mason 2006) and the social reach of knowledge is deemed by many to amplify its power in terms of organizational performance (Nonaka 1994)

Leveraging such amplification the comparatively recent advent and continued proshyliferation of Web 20 social media applications many of which extend social reach (Nahapiet and Ghoshal 1998) and facilitate knowledge exchange (Kaplan and Haenlein

This article not subject to US copyright law Address correspondence to Mark E Nissen US Naval Postgraduate School Graduate School of

Operational amp Information Sciences 1411 Cunningham Road Room GW2006 Monterey CA 93943 USA E-mail MNissennpsedu

84

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85 KNOWLEDGE TEAMWORK THROUGH SOCIAL MEDIA VIRTUAL WORLDS

2010) through technological intermediation are expected broadly to improve the perforshymance of organizational work (Becerra-Fernandez and Sabherwal 2001 Choi Lee and Yoo 2010 Martiacutez-Moreno et al 2009 Maznevski and Chudoba 2000 Montoya Massey and Lockwood 2011 Samarah Paul and Tadisina 2007) This highlights teams groups organizations and other levels of social aggregation as particularly appropriate units of analysis

For several instances without central coordination of their activities or interactions social networking (eg Facebook) microblogs (eg Twitter) and collaborative projects (eg Wikis) enable geographically and temporally distributed people to communicate and collaborate in near-real-time often (Goel Junglas and Ives 2009 Palen Hiltz and Liu 2007 Sandelowski 2000 Sarcevic et al 2012) demonstrative and instructional videos (eg YouTube) allow unknown (to the content creators) participants to learn by reviewing knowledge-based activities being performed in addition to reading and hearing explanashytions about them all via persistent media (Dennis et al 1998 Dennis Fuller and Valacich 2008) simulation and game technologies that facilitate knowledge transfer (eg for trainshying aircraft pilots to fly for instructing business managers on decision making for teaching people to play chess and other board games) although not considered by all as ldquoWeb 20rdquo or ldquosocial mediardquo applications per se enable people to experience directly and practice knowledge work first hand albeit in synthetic environments (Aldrich 2005 Brown and Adler 2008) and immersive 3D environments supporting virtual social (eg SecondLife) and game (eg World of Warcraft) worlds which represent social media applications also (Kaplan and Haenlein 2010) enable users to sense social presence copresence psychologshyical engrossment and affective experience reminiscent of direct physical and face-to-face (F2F) interaction on multiple levels (Short 1976 Witmer 1998)

Further the educational psychology literature is replete with experiments comparshying the efficacy of technologically intermediated distance learning (eg videotape TV broadcast with duplex audio video teleconference web-based instruction)mdashwhich focuses directly on extending the social reach of knowledgemdashwith respect to the venerable F2F classroom teaching environment (Clark 1983) Most studies show no significant differences (Russell 1999 Allen et al 2004)

After reviewing this literature Bates and Poole (2003) summarized that ldquothe research evidence indicates clearly that technology-based teaching can be just as effective as faceshyto-face teachingrdquo (p 19) indicating that technology can play an important and equally effective role in extending the social reach of knowledge Moreover they go further by noting how technology enables some pedagogical techniques that are infeasible in the classroom and they suggest that in some respects technologically intermediated distance education can be even better than its classroom counterpart (p 23) The implications in terms of social media are exciting

The technological intermediation of knowledge work within and among such social aggregations of people remains a complex endeavor however (Malhotra and Majchrzak 2009 Malhotra and Majchrzak 2012) and ensuring superior or even equivalent perforshymance is far from guaranteed Indeed notice how the qualified language (eg ldquomost studies show no significant differencesrdquo ldquocan be just as effectiverdquo ldquoin some respectsrdquo) suggests that multiple factors other than technological intermediation affect performance Notice furshyther how such language refers to relatively well-established technologies (eg videotape TV video teleconference web-based instruction) Although well-established and long-supported it remains unclear how much of this education psychology knowledge will apply directly to technological intermediation via social media applications

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86 NISSEN AND BERGIN

Indeed many questions regarding the efficacy of social media capabilities and interfaces require continued research to understand the underlying phenomena and to guide both system development and organizational practice This pertains in particular to immersive 3D environments supporting virtual social and game worlds As with other social media applications (esp social networking microblogs collaborative projects) such virtual worlds can amplify knowledge broadly Moreover they also immerse participants into virtual worlds that become real to them (Nissen 2010) and they offer potential to approach the sensation of F2F interaction in terms of presence copresence psychological engrossment and affective experience

This makes immersive virtual worlds particularly interesting to investigate in terms of understanding their potential impact on knowledge work performance particularly at the team group or organization level where performance amplification is expected to be most pronounced We formalize this interest through two primary research questions (1) How can virtual world social media applications enable team performance of organizashytional knowledge work comparable to levels attainable via F2F interaction (2) What are the key characteristics of such applications that enable comparable performance

In this exploratory study we investigate team performance of organizational knowlshyedge work through virtual world social media applications We draw from the relatively recent literature describing virtual worlds to characterize key aspects of these applicashytions and interfaces and we employ both quantitative and qualitative research techniques to investigate how teamwork performed through virtual worlds can approach that accomshyplished via F2F interactions Then we present statistical results augmented by qualitative analysis which leads to important findings insights guidelines and suggestions for expanded research as well as enhanced practice

2 BACKGROUND

In this section we provide background information regarding virtual worlds We begin by elaborating what we mean by ldquovirtual worldsrdquo and progress to describe them in relatively general terms We then describe a specific virtual world instance to provide more detailed insight into this class of social media applications

21 Virtual Worlds

The term virtual world means many things to many people and there is little general agreement regarding what constitutes much less defines a virtual world Some see virtual worlds in the background for instance reflecting little or no difference with commonplace technology applications (Lehdonvirta 2010 McLennan 2008 Porter 1997 Sicart 2010) Others as a contrasting instance view virtual worlds in the foreground as unique and distinct from the real world (Chidambaram and Zigurs 2001 Ellaway and Topps 2010) Blending and balancing these views (Lehdonvirta 2010 Castronova 2005 Wankel and Malleck 2010 Yee 2006b) we see virtual worlds through a middle ground lens as computer-mediated environments that participants perceive to be distinct from the real world but that immerse users affectively in such environments and that exhibit spillover effects (eg cultural economic perceptual social) between the real and virtual worlds

For instance organizations are spawning wholly within in virtual worlds (Chidambaram and Zigurs 2001) Here the organization and its environment exist solely

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within technological artifacts (Castronova 2005) More than simply metaphor for organizashytion (Morgan 1997) organizational environment and technology meld into one confluent contingency that blurs the line between what we consider real and virtual (Wankel and Malleck 2010 Yee 2006b) Teams and organizations within such virtual worlds are real in the sense that collectivities of people band together to accomplish (at least partially) shared goals (Scott 1995) but they are virtual in the sense that they have no presence or counterparts outside of their graphically rendered environments They are real in the sense that participating people perceive them as functioning teams and organizations subject to structuration (Giddens 1984) ldquoas socially constructed entities with various aspects of organizational life being negotiated through organizational policies and through everyday interaction among individualsrdquo (Chidambaram and Zigurs 2001 p 134) but they are virshytual in the sense that such teams organizations policies and interactions take place only within technology enabled virtual worlds

Further many serious organizations (eg marketing architecture real estate) are emerging within virtual worlds (Oravec 2001) and the ldquopopulationrdquo (Wankel and Malleck 2010 p 2) and per capita ldquogross domestic productrdquo (Castronova 2005 p 19) in some virtual worlds exceed those of major nations across Europe Asia and elsewhere Also virtual worlds have few physical constraints (eg teleportation is a common mode of transshyportation death is inconvenient but temporary altering onersquos appearance unrecognizably requires only a few mouse clicks) on what organizations can accomplish (Teigland 2010) and ldquomoneyrdquo within many virtual worlds is traded continuously via active (albeit mostly underground) markets with ready exchange rates to major real world currencies (eg US dollars)

Moreover advances in graphics technology and cinematic engagement enable unparshyalleled levels of immersiveness that can induce sustained psychological engrossment in virtual worlds (Ellaway and Topps 2010) More than computers as theater (Laurel 1991) users in many virtual worlds write and enact their own scripts constitute the audience as well as the cast and come to think of computational representations of themselves (eg via computer avatars) in emotional and personally identifiable ways (Castronova 2005 Yee 2006) As one of several multiple realities (Schutz 1971) or frames of experience (Goffman 1974) a virtual world has meaning to its inhabitants (Fine 1983 p 217) as the real world does

Indeed time investments made by people in some virtual worlds are comparable to or exceed those in real world organizations For instance tens of millions of people spend 20ndash30 hours a week (ie equivalent to part-time employment) in virtual worlds (Yee 2006a) and these are not just kids playing video games after school virtual worlds are inhabited by people of all ages with the average participantrsquos age estimated between 27 and 31 (Chidambaram and Zigurs 2001 Castronova 2005 Yee 2006a) but reflecting considerable variation (eg including grandchildren and grandparents alike)

Plus emotional commitments to organizations in virtual worlds can exceed those associated with physical organizations in the real world For several instances roughly 20 of participants in one survey report a virtual world as their ldquoreal worldrdquo (Castronova 2005 p 2) nearly a third of participants in another survey report that experiences in virtual worlds are more rewarding satisfying and frustrating than counterparts in the real world are and nearly half report that participation in virtual worlds improves their real world leadership skills (Yee 2006a 309 pp 322ndash323) Many participants characterize time spent in virtual worlds ldquoas a second jobrdquo and for some participation in virtual worlds is ldquomore stressful and demanding than their actual jobsrdquo (Yee 2006b pp 69ndash70)

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This has real economic and organizational consequences Quite distinct from most organizations in the physical world people in such virtual worlds pay real money volshyuntarily for the privilege of engaging in them Beyond just fantasy worlds (Fine 1983) or unproductive environmentsmdashlike the mythical islands of lotus eaters encountered by Odysseus (Homer 2008)mdashvirtual worlds have many attributes that make them real and serious organizations emerging within them merit serious scholarly examination (Baym 2000)

To reiterate we see virtual worlds as computer-mediated environments that particshyipants perceive to be distinct from the real world but that immerse users affectively in such environments and that exhibit spillover effects between the real and virtual worlds This enables us to make operational contrasts between virtual worlds virtual environments and physical environments When using the term virtual world we refer to the kinds of immersive psychologically engrossing computer-mediated environments that participants experience with affect characteristic of F2F interactions in the real world Alternatively when using the term virtual environment we refer to any technology enabled social media application Clearly virtual world is a subset of virtual environment as are emailing texshyting social networking microblogging and the kinds of other social media applications outlined In contrast we use terms such as physical environment physical world and real world when referring to F2F interactions without technological intermediation

22 Virtual World Instance

In this section we draw from continuing ethnographic work (Nissen 2010) and describe one very popular virtual world instance to provide more detailed insight into this class of social media applications SecondLife (SL) SL claims a population of roughly 20 million inhabitants (Linden Labs 2010) and appears to be the most popular virtual world social media application (Schijns 2007) hence it enjoys broad usage and provides good general insight into immersive virtual worlds for this investigation

SL is a persistent massively multiuser virtual social world that is rendered in three dimensions with motion and that permits users to represent themselves within such world via computer avatars Users are able to create and dress relatively elaborate avatars to resemble nearly any humanoid move them freely throughout the environment and use them to interact with other users via their corresponding avatars Most people create avatars that resemble themselves in appearance and they come to identify personally with their avatars referring to them in first-person (eg ldquoI went to the mall yesterdayrdquo) for instance (Castronova 2005 Yee 2006b)

The SL virtual world as visualized from the perspective of a userrsquos avatar is immense very many orders of magnitude greater than what can be ldquoseenrdquo through any one avatarrsquos eyes at one time (eg equivalent to land extending well beyond the horizon) In terms of geography this world is comprised of myriad ldquoislandsrdquo which can be reached by flying (eg avatars can fly run walk stand and sit) or more commonly teleporting (eg one can input the map coordinates of a destination and travel there instantaneously)

Once at a destination users can maneuver their avatars through 3D virtual renderings of buildings streets malls buses rivers lakes oceans skies fields mountains valleys and like representations of artifacts common in the real world Users can move and look around in all directions within this virtual world they see their avatar and those of other users within a viewing distance and perspective that looks very similar to what we experience

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daily in the real world Most artifacts within this virtual world resemble equivalent artifacts in the real world and many artifact builders seek to replicate the real world closely

As with most popular social media applications (esp social networking and microblogging) users communicate principally through text chat and one can incorposhyrate links to webpages images music videos social media applications and other online resources outside of SL Indeed one can use most popular social media applications from within SL In this sense it can be viewed as subsuming many other such applications It is also straightforward to overlay voice communication (eg VOIP) on top of these other modalities and SL avatars come with a rich set of emotive actions (eg laughing shrugshyging yawning) and body language (eg turning one avatarrsquos back to anotherrsquos walking away standing closely during a conversation) Hence this is a very media-rich application (Daft and Lengel 1986) and the sensations of presence and copresence develop readily when immersed within this virtual world and interacting with other users through such rich multiple communication modes

The sensations of presence and copresence can be compelling in this virtual world One is aware of course that the world is virtual and that interaction with other people via avatars is not the same as F2F conversation but the rich immersive 3D experience is psyshychologically convincing and engrossing on several levels Being able simultaneously to view avatars that look like recognizable people in the real world move at will to interact via avatar with different users engage in conversations via voice and text express emoshytions via avatar body language travel with others through the virtual world participate in group activities and experience jointly in-world multimedia interactions (eg including social networking and online video) creates convincing sensations of presence and coshypresence (Nissen 2010) Figure 1 provides a screenshot of multiple usersrsquo avatars gathered together and interacting via text voice body language movement and vision in a mall area

Figure 1 Second Life screenshot Mall area gathering (color figure available online)

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Like many virtual worlds SL is persistent and as a massive multi-user online envishyronment it enables interaction between millions of users Buildings yards malls pools streets fire hydrants rivers buses and like virtual world artifacts are rendered equivashylently to all users (ie every user from every computer and in every location around the real world visualizes the same artifacts in the same way) and are available to all users at all times whether or not any particular user is logged on and accessing the environshyment Each user is able to change his or her avatar at will so the corresponding userrsquos avatar will appear differently to others but the shared graphically rendered environment inhabited by avatars does not change as a result Such persistence and massive interaction serve to differentiate SL and like virtual worlds from most console single-player and like games and environments that are instantiated and viewable only when a particular user is engaged persistence along these linesmdashalbeit via much richer and more immersive 3D virshytual environmentsmdashis similar to the manner in which the content of a social networking application (eg Facebook) remains online and can be viewed the same way by myriad users regardless of whether the author or any particular viewer of such content is online

Although persistent such environment is dynamic however For instance users have the ability to purchase and build on ldquolandrdquo on one or more ldquoislandsrdquo (ie with real world currency such as US Dollars although basic access to SL is free some more advanced capabilities such as buying land require real world money to be spent outside of the virtual world) and build artifacts such as those listed If a particular user views an open field on a Friday and a different user constructs a building in that area over the weekend then the former user will see a building in that field when he or she returns to the environment on Monday Hence this virtual world is persistent yet dynamic

Further there can be considerable spillover between the SL virtual world and the physical world which enables SL to effectively subsume many popular social media applishycations For the specific SL virtual world instance examined in this investigation we purchase virtual land construct virtual buildings and create virtual tables chairs comshyputer monitors white boards projection screens and like appurtenances common to office and classroom environments in the physical world

Within these virtual world creations user avatars can enter move around in and move between such buildings and within any particular room onersquos avatar can sit in chairs view common content on white boards (eg user-written notes) and projection screens (eg online content from outside SL) Moreover one can even use virtual computer monitors to access in-world the same social networking microblogging collaborative projects and like social media applications available in the physical world Thus the SL virtual world instance examined through the present study embeds common social media applications within an immersive 3D experience that induces affective engrossment through immersive presence and copresence

3 RESEARCH DESIGN

As noted we employ both quantitative and qualitative research techniques to invesshytigate how teamwork performed through virtual worlds can approach that accomplished via F2F interactions Quantitatively we conduct a pilot laboratory experiment to assess comparative team performance directly through both virtual world and F2F interaction This experiment complements a parallel exploratory study comparing F2F interaction with teamwork intermediated by technology that combines aspects of other popular social media applications (eg similar in respects to a combination of social networking agrave la Facebook

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microblogging agrave la Twitter and collaborative projects agrave la Wikis see Bergin et al 2010) We elaborate on the corresponding research design in considerable detail next

Qualitatively we build on the continuing ethnographic work discussed previously and immerse ourselves as participant observers (Spradley 1980) to understand the virtual world social media application and teamwork context together via an emic perspective (Bernard 1998) Particularly as we emphasize the key roles played by immersion and presence in the study this perspective is indicated as important by numerous researchers of virtual worlds and like parallel frames of experience that have meaning to their inhabitants (Fine 1983 Baym 2000 Ducheneaut and Moore 2005) Qualitative analysis is intended to both augment and complement the respective quantitative results and etic perspective obtained via experimentation

31 Laboratory Experiment Overview

For this pilot laboratory experiment we employ the ELICIT1 multi-user countershyterrorism intelligence game to examine how task performance in a highly immersive 3D virtual world compares with that in the F2F physical environment Consistent with our research questions we conduct this laboratory experimentation in a task environment reflecting team performance of organizational knowledge work Specifically we utilize a two-treatment contrast experiment design

The ELICIT game simulates an organization tasked with discovering and identifying the details associated with (fictitious) terrorist plots which provides an apt task environshyment for examining knowledge work by teams seeking competitive advantage the better and faster that participants share and process knowledge the better and faster they are able to detect (and hopefully foil) competitorsrsquo attacks We begin by describing the ELICIT experiment apparatus and task environment We then outline the participants groups protocols controls manipulations and measurements used for experimentation

32 Experiment Apparatus

The ELICIT game is instrumented (Parity Communications Inc 2006) and proshyvides an experiment apparatus that is particularly useful for analyzing knowledge work and exchange activities in a team environment ELICIT is a Java-based software platshyform that can be used to run multiteam experiments in an organizational context This ELICIT experiment apparatus has configurable scenarios that focus on the task of discovershying and integrating key details (esp who what where and when) of fictitious terrorist plots ELICITrsquos instrumented task environment captures and time stamps all participantsrsquo knowlshyedge exchange activities The environment also generates detailed transaction logs that can be used to measure individual and team performance Considerable research by several different research teams has been conducted to date using ELICIT and the experiment apparatus has become very stable and well-understood during this time

33 Task Environment

To elaborate on the overview the ELICIT task environment requires a team of particishypants performing the roles of intelligence analysts in an organizational setting to collaborate

1ELICIT Experimental Laboratory for Investigating Collaboration Information-Sharing and Trust

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and identify the details of a fictitious and stylized terrorist plot The fictitious terrorist plot is described through a set of clues called ldquofactoidsrdquo that have been developed systematishycally The gamersquos design in this sense is similar to the Parker Brothersrsquo board game Clue in that it requires each participant to analyze clues and combine his or her assessments with other participantsrsquo to identify key aspects of the fictitious plot Participants are told that all factoids can be assumed to be true and consistent and they are instructed to trust in factoid veridicality that is no misinformation is introduced into the experiment Participants are told additionally that there is a correct solution to the problem However not all factoids are equally important in terms of solving the plot but participants are not informed of this aspect of the task environment

Each factoid describes one or more aspects of the plot (eg a clue regarding which terrorist organization is involved) which is shared with and used by others This represents the kind of collaborative activity associated broadly with knowledge work in general and the type of knowledge exchange performed often via social media applications Indeed counterterrorism organizations in the real world are beginning to adopt some social media applications today (Cacas 2012)

Each participant has access to a set of five ELICIT functions (1) List individually known factoids (2) Post individually known factoids to a common site (3) Pull shared factoids from a common site (4) Share individually known factoids directly with another player and (5) Identify the fictitious plot details

Factoids are distributed among the participants in a timed sequence of three steps each participant receives two clues initially followed by one after five minutes of play and another after ten minutes have elapsed The factoid knowledge distribution is designed so that no single participant can solve the problem individually and that the team of parshyticipants cannot solve the problem until after the final distribution In other words the participants must collaborate and share knowledge to solve the problem and they are required to do so until all knowledge has been distributed Evidence from previous expershyiments (Parity Communications Inc 2006) suggests that the game requires roughly one hour to play effectively Hence the task environment is sufficiently rich and complex to challenge a team of participants for a substantial amount of time yet it is also sufficiently self-contained and straightforward to permit effective play within an hour This represents a nice combination in terms of supporting laboratory experimentation

34 Participants

Participants in this study are comprised principally of information sciences graduate students at a major US university All participants have undergraduate college degrees in addition to professional work experience and they self-select in response to open campus announcements describing the nature of our study As information sciences gradshyuate students all are technologically experienced and many are highly proficient Many participants engage regularly in social media interactions (esp social networking and microblogging) and some also have experience with virtual worlds (esp SecondLife)

To help reduce the coordinative burden participants are organized into four subteams Each subteam is responsible for one set of details associated with the terrorist plot For instance Subteam 1 is responsible for the ldquowhordquo details (eg which terrorist organization is involved) of the plot Subteam 2 is responsible for the ldquowhatrdquo details (eg what the likely target is) and so forth for ldquowhererdquo and ldquowhenrdquo Participants are assigned randomly to these subteams

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35 Protocols and Manipulations

This two-treatment contrast experiment involves game play in two separate modes In one mode to examine performance in an immersive virtual world participants manipushylate individualized avatars within SecondLife using networked computer devices Building on prior work to integrate ELICIT into the SL virtual world (Hudson and Nissen 2011) each participantrsquos SL avatar has a virtual workstation of its own situated within a virtual room through which ELICIT can be played in-world Each participant uses his or her avatar to play the ELICIT game and collaborate with other participants via their avatars within the SL virtual world In the other mode to examine performance in the physical environshyment participants play through F2F interaction in real world rooms equipped with physical tables pencils papers and white boards In both modes protocols and manipulations are designed to be very consistent with prior experiments using ELICIT and hence reflect well-established methods We summarize protocols and manipulations for each mode next

351 Virtual world One week prior to the experiment participants are instructed on how to set up SL accounts and create individual avatars for the game these people are encouraged and given ample opportunity to ask questions and seek out technical assistance as necessary Participants then report to a networked classroom on their assigned day for the experiment Once seated participants are allotted roughly 30 minutes to spend time ensurshying they can log into the network access and operate their avatars and play the ELICIT game effectively in the SL virtual world Although not a training session per se all particishypants have substantial time to practice using moving and communicating via avatars and the next set of activities does not begin until every participant expresses confidence in his or her ability to use SL ELICIT proficiently Researchers and technicians are available to assist participants and they roam through the laboratory to look over participantsrsquo shoulshyders answer questions assist with problems and verify that everyone is ready to begin the game (They also observe participantsrsquo activities and behaviors listen to their questions and comments and use like qualitative data-collection techniques to gain insight into the experiment)

Participants teleport to the SL virtual world island used for experimentation maneushyver their avatars through the experiment rooms set up for the experiment and have them sit down in virtual chairs to interface in-world with represented computer monitors spaced around 3D tables as displayed in Figure 2 Each participantrsquos avatar has its own represented computer monitor but all participants in each room can view and communicate in-world with the other avatars and all can see a common presentation screen on the virtual wall via their avatars inside

Clever application programming interfaces (APIs) enable full Internet connectivity and functionality through the virtual computer monitors For instance each participant can use his or her avatar to interact with the virtual monitor and accomplish any activities associated with Internet use2 (eg access Web sites engage search engines use social networking sites send and receive webmail) Participants use this same capability to play the ELICIT game via avatars

Once everyone is verified as being ready participants are given ten minutes to read a set of instructions pertaining to both the experiment and the ELICIT environment (eg Post Pull other ELICIT game functions) and they are encouraged to ask questions After

2Interestingly avatars within SL could conceivably use their virtual monitors to access SL functionality and hence have avatars within SL controlling other avatars embedded more deeply within SL and so forth

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Figure 2 Experiment room within SL Virtual World (color figure available online)

participants finish reading the instructions they have ten minutes to discuss their approach to the problem-solving scenario with others on their subteams Then they take a short break before beginning the experiment tasks

Once the game begins each participant receives unique factoids in the three phases noted previously Factoids are time stamped and appear automatically on participantsrsquo ELICIT screens after which they can be listed posted pulled shared and analyzed As noted all Post Pull Share and Identify actions are time stamped and logged by the game server

Participants are instructed not to communicate with one another outside of the SL virtual world during game play but they have access to the rich and immersive 3D graphshyical environment text chat emotive actions of avatars (eg waiving yawning clapping running flying) and like features of a virtual social world Although we have the technolshyogy (eg VOIP) to enable voice communication within the SL virtual world we do not utilize such capability in this exploratory study This is consistent with most prior expershyimentation using ELICIT Hence participants are immersed within the SL virtual world complete with affective and emotive avatars but verbal communication is limited to text3

as with most popular social media applications Follow-on research can assess the impact of incorporating in-world voice communication

The simulation takes approximately one hour for participants to Identify (ie make their most informed guesses about) the plot details Participants are instructed before the experiment to Identify only once during game play and they are motivated to perform well individually and as a team For instance participants are told that their team performance

3As with other parallel experiments participants are all seated in physical classrooms for the experishyment so some real world communication outside of the technological intermediary (ie SL in this experiment) is inevitable

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will be compared with that of other teams and that they have only one hour to collaborate and complete the task before the (fictitious) terrorist attack takes place In this way we encourage teamwork as well as individual effort and we stimulate two dimensions of comshypetition one against other counterterrorism intelligence organizations another against the terrorist organizations planning attacks This dynamic of multidimensional competition is consistent with numerous reports characterizing intelligence agencies in the United States and other countries (National Commission on Terrorist Attacks 2004)

352 Physical world As noted participants report to a physical classroom on their assigned day for the experiment In great contrast with the SL virtual world above however such classroom is complete with physical desks tables chairs pens papers whiteboards and like familiar appurtenances The photograph displayed in Figure 3 shows an experiment room set up within the physical environment of a university campus Physical rooms such as this served as models for development of their immersive virtual counterparts displayed previously

Further as noted previously once seated participants are allotted ten minutes to read a set of instructions pertaining to both the experiment and the physical environment and they are encouraged to ask questions Once participants finish reading the instructions they have ten minutes to discuss their approach to the problem-solving scenario with others on their subteam Then they take a short break before beginning

Once the game begins each participant receives four unique factoids in the same three phases summarized here factoids are delivered physically on slips of paper instead of electronically within the in-world ELICIT game but the content distribution and timing

Figure 3 Experiment room within physical environment (color figure available online)

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are identical Indeed we go to great lengths to control potential confounds and to isolate differences to the contrasting ELICIT environments game play in the SL virtual world versus the physical world is made as comparable as possible As noted the simulation takes approximately one hour for participants to Identify (ie make their most informed guesses about) the plot details Participants are instructed to Identify only once during game play and they are motivated to perform well individually and as a team

We should note here that roughly half of the participants play the ELICIT game twicemdashfirst in the physical world then again in the SL virtual worldmdashwhereas the other half play only one time To address potential concerns with learning and bias the same open recruiting methods are used to attract participants to both experiment sessions all participants are assigned randomly to the various subteams in each session different yet structurally equivalent versions of the game are played each time and the two experiment sessions are conducted four months apart Nonetheless some learning across experiment sessions by the participants who play twice may confound our results somewhat and some bias (eg based on prior experience education or cultural background) seems inevitable

However results from another research teamrsquos ELICIT experiment (Nissen and Leweling 2010) suggest otherwise Despite allowing all participants to play (different yet structurally equivalent versions of) the game four times plus measuring hypotheshysized learning and testing directly for its significance the kind of anticipated performance improvement over time and across games did not obtain Moreover these researchers employed the same kinds of recruiting and randomization techniques used in our present study and negligible bias was reported Although we cannot rule out learning or bias effects in terms of this exploratory study neither are we overly concerned about them Future researchers could allow or control carefully for cross-game learning and participant bias along with testing for any significant effects but results of other research suggest that structurally equivalent versions of the game are sufficiently different to impede such learnshying at least across small numbers of repetitions (eg four or fewer) and that our recruiting and randomization techniques help to mitigate concerns with bias

36 Measurements

Continuing to follow prior ELICIT experimentation we operationalize performance as a two-dimensional dependent variable comprised of (1) speed (ie time to identify plot details correctly) and (2) accuracy (ie correct identification of plot details) These depenshydent measures are informed by literature in the psychological and organizational domains that suggest a trade-off exists between time and accuracy in tasks requiring high cognishytion andor advanced motor skills at both the individual and teamgroup levels of analysis (Beersma et al 2003 Elliott Helsen and Chua 2001 Guzzo and Dickson 1996 Meyer et al 1998 Plamondon and Alimi 1997 Rogers and Monsell 1995)

In the first component speed pertains to how long it takes a participant to submit his or her identification of the terrorist plot details The time for each participantrsquos idenshytification is logged to the nearest second by the software (or by stopwatch in the physical treatment group) Specifically each participantrsquos elapsed time is recorded when he or she uses ELICIT to Identify the plot (virtual world mode) or submits a piece of paper with the Identify information on it (physical world mode) Because speed is important in this task environment lower times reflect superior performance in terms of this component

The accuracy component of performance refers to correct identification of details associated with the fictitious terrorist attack (ie Who What Where and When) Every

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participantrsquos Identify action is scored with a value of 1 for each correct answer to the Who What Where and When aspects of the solution The sum (maximum of 4) is then normalized on a [0ndash1] scale Because accuracy is important in this task environment higher accuracy scores reflect superior performance in terms of this component

4 RESULTS

In this section we first summarize statistical results from the experiment Afterward we outline several observations pertaining to participantsrsquo task performance steps time-lines and behaviors observed during the experiment sessions We then discuss key findings and integrate insights from our qualitative study to help interpret and understand the statistical results

41 Statistical Results

Table 1 presents descriptive statistics In the table ldquoSLrdquo refers to game play within the SL virtual world and ldquoF2Frdquo refers to game play within the physical environment To enhance interpretation we use bold font to highlight the higher performing score in each SL-F2F contrast Speed is faster in the F2F condition (ie ldquoID Timerdquo = 2554 secshyonds) for instance than it is for SL (ie ldquoID Timerdquo = 2972 seconds) Indeed notice that F2F scores are higher also across all four components of accuracy as well the composite accuracy score

Table 2 summarizes significance tests With our two-treatment contrast experiment design we employ the basic t-test of independent samples to assess significance of differshyences in means between SL and F2F results Given the open recruiting approach participant turnover randomization techniques and long delay between experiment sessions as disshycussed previously this statistical method is appropriate for our exploratory study Each row of the table summarizes a contrast between SL and F2F For instance the first row summashyrizes the difference noted previously in identification time between the SL and F2F groups (ie 418 = 2972 ndash 2554 seconds) We also report t statistics (t) degrees of freedom (df) and significance levels for 2-tailed tests for each contrast To enhance interpretation we use bold font to highlight the four results that are significant at the 05 level

Table 1 Descriptive statistics

Variable Mean N Standard deviation Standard error

ID Time SL (in Seconds) 2972 13 2691 746 ID Time F2F (in Seconds) 2554 14 2792 746 Who Score SL 62 13 506 140 Who Score F2F 79 14 426 114 What Score SL 38 13 506 140 What Score F2F 79 14 426 114 Where Score SL 08 13 277 077 Where Score F2F 79 14 426 114 When Score SL 23 13 160 044 When Score F2F 33 14 320 086 Composite SL 33 13 251 070 Composite F2F 67 14 344 092

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Table 2 Results of significance tests

Variable Mean value t df Significance (2-tailed)

ID Time Who Score What Score Where Score When Score Composite

418 minus170 minus401 minus709 minus103 minus346

3953 minus949

minus2234 minus5160 minus1040 minus2965

25 25 25 225 25 25

001

352

035

000

308

007

The difference in identification speed is highly significant (p = 001) Accuracy conshytrasts for correctly identifying ldquowhordquo and ldquowhenrdquo are not significant (p = 352 and p = 308 respectively) but contrasts are significant for identifying ldquowhatrdquo (p = 035) and in particushylar ldquowhererdquo (p = 000) Additionally the accuracy contrast for the composite identification score is significant (p = 007) as well In summary our results show that the F2F treatshyment corresponds to significantly faster and more accurate performance overall than that measured for the SL treatment In words people interacting physically in the F2F task environment outperformed those interacting via avatars in the immersive SL virtual world across both speed and accuracy dimensions

42 Participant Behaviors

As noted here we outline several observations pertaining to participantsrsquo task perforshymance steps timelines and behaviors observed during the experiment sessions In general such steps timelines and behaviors conformed very closely to the manners in which they were planned and as they are described in the research design presented Indeed our research design controls expressly for many aspects of participantsrsquo tasks timelines and behaviors

As noted in the research design for instance participants received new knowledge in terms of factoids that were released to them in three phases (ie at Time 0 5 minutes and 10 minutes) regardless of whether they were assigned to the F2F or virtual world treatshyment With the receipt of each new factoid participants would generally read and make some kind of note of it In the virtual world condition participants would use the in-world ELICIT interface to List each factoid in a working area selectively Post relevant factoids to a common site for viewing by teammates and Share them with specific teammates directly Individual participants would also pull factoids posted by other teammates to their common site In the F2F condition the sequences and behaviors were very similar except that parshyticipants would use pencils and papers to note summarize and compare factoids received directly and they would use a common whiteboard to list factoids deemed relevant for sharing with other teammates

Given that all factoids were distributed within the first ten minutes of the experishyment most of participantsrsquo time was spent analyzing the factoids they were aware of (eg received directly pulled from the common site shared by teammates) and exchanging knowledge with teammates Participants were instructed before the experiment regarding the need for collaboration to solve the fictitious terrorist plot and as noted in the research design they had ten minutes to discuss their approach to the problem-solving scenario with others on their subteams As time passed progressively more participants in both

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experiment conditions appeared to reach saturation and develop confidence in their abilshyity to identify the plot details Participants in the virtual world condition used the ELICIT interface to Identify plot details whereas those in the F2F condition did so via paper

There was little to report in terms of decision making Each group discussed its general approach to the task before the experiment began and participants in each group collaborated during game play as noted However we did not observe participants in either condition voting or employing like approaches to reaching some kind of consensus regardshying plot details Nonetheless much of the verbal discussion in the F2F condition related directly to how the various factoids interrelated and what the most likely plot details appeared to be suggested by them Alternatively as noted previously interactions in the virtual world condition focused instead principally on factoid exchange This difference provides a useful segue to the key findings and insights discussed next

43 Key Findings and Insights

From our perspective as investigators we observed both groups working together as teams exchanging knowledge following organizational rules identifying key aspects of the terrorist plots and completing the counterterrorism intelligence tasks within an hour From this qualitative perspective team knowledge work performance through the immersive virtual and F2F physical worlds appears to be quite comparable even though performance differences are statistically significant Hence we see a difference in terms of efficiency and efficacy but not feasibility although performance is faster and more accurate via F2F interaction than within the SL virtual world such team knowledge work is clearly feasible in both the physical and virtual worlds

In addition four key interrelated findings and insights are particularly noteworshythy in light of our research questions (1) virtual world experience (2) task environment (3) teamwork and (4) interface fidelity We discuss each in turn

431 Virtual world experience Considering our primary research question we gain insight into how virtual world social media applications may enable team performance of organizational knowledge work comparable to levels attainable via F2F interaction One key appears to center on usersrsquo experience with virtual worlds Recall for instance how we conducted our first experiment session in the F2F physical environment Participants in this first session appeared to be very much at ease and relaxed going into the laboratory and during the experiment session Despite ELICIT being foreign and requiring some learning by participants to play the game and work as newly formed teams to perform within the laboratory constraints every participant appeared to have immense experience interacting with others through F2F communication (eg probably having done so most of their lives)

Alternatively as we observed the second session of the experiment it was clear that the SL virtual world represented a new experience for many participants in the study We did not collect quantitative data specific to participantsrsquo prior experience with SL or other virshytual worlds but some informal hand raising before the experiment (eg to help identify the people who would need the most technical assistance preparing for the experiment) revealed considerable ignorance and naiumlveteacute Although several participants reported being familiar with SL (eg having developed and used avatars previously) and some of them were very experienced (eg having logged hundreds of hours in-world) roughly a third of participants indicated no SL experience prior to creating avatars for this experiment

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Indeed participants claiming relatively high prior SL experience levels could be observed during the experiment to be posting pulling and sharing factoids actively via avatars in the SL ELICIT virtual world sharing knowledge aggressively interacting flushyidly with other participants via avatars and working in a focused manner toward solving the terrorist plot These participants appeared to be productive and their knowledge work activities appeared to be highly focused on task performance

In considerable contrast participants claiming relatively little or no prior SL expeshyrience levels seemed during the experiment to be somewhat preoccupied withmdasheven distracted bymdashthe avatars being used to represent them within the SL virtual world In fact one participant appeared to be notably disturbed by the inability to get an avatar to wear clothes (eg the avatar was visibly naked in-world except for carrying an accessory handshybag) Other SL inexperienced participants could be observed repeatedly modifying refining and moving their avatars (eg changing appearance switching in-world outfits intermitshytently standing up flying around sitting down again) in addition to laughing giggling and communicating with others (in the physical world) about topics that did not appear to be focused on uncovering the terrorist plot

This finding provides some insight into why F2F interaction reigned supreme over virtual world teamwork in the experiment Participants with less or no experience appeared to have difficulty focusing on the task at hand Moreover such difficulty may have been magnified by the very same aspects of virtual worlds that make them particularly attractive as social media applications (esp the potential to approach the sensations of F2F interaction in terms of presence copresence psychological engrossment and affective experience) As noted for instance immersion within a virtual world can be a highly compelling and engrossing experience Where someone is compelled toward and engrossed in behaviors that are oriented toward task accomplishment such experience can offer benefits beyond those available via other social media applications and virtual environments Alternatively where someone is compelled toward and engrossed in behaviors that distract them from the tasks at hand such experience can impede effective performance possibly more so than with less-compelling and less-engrossing technological intermediaries

432 Task environment For additional insight we look back to our discussion of educational psychology experiments Recall results indicating how technologically intershymediated teaching has been equally and even more effective (in some cases) than F2F classroom instruction (As both classroom and distance teachers ourselves we have direct experience with this domain as well) When comparing these studies with the protocol for our ELICIT experiment we notice that the task environments associated with the former extend over much longer periods of time (eg quarters semesters years) than the hour corresponding to the latter

This suggests that F2F interaction may have advantages over technologically intershymediated knowledge work and exchange when task performance becomes increasingly time-critical Alternatively when time constraints loosen and performance time frames dilate virtual world social media applications may increase in terms of comparative efficacy with respect to F2F interaction in the physical world In other words task environments that are less time-critical in nature may enable virtual world social media applications to deliver the kind of potential that makes them appear attractive but in collaborative time-critical task environments their comparative efficacy appears to be suspect

Moreover both F2F interaction and teamwork through the virtual world in this expershyiment involved collocation of participants in physical classrooms Whereas such physical

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collocation is required for F2F performance the virtual world participants could have been distributed anywhere on the globe (with an Internet connection) just as well This insight extends beyond the research questions addressed through this study but it elucidates the time and cost of travel as an important consideration F2F interaction reigned supreme over organizational knowledge teamwork intermediated by virtual world social media applicashytions in this study but we evaluated only task performance (ie speed and accuracy) Were we to include travel time and cost in the study we would likely observe some important tradeoffs (eg concerning task performance time and cost) between the two collaboration modes

433 Teamwork Further regarding our primary research question another key appears to center on usersrsquo ability to work effectively in teams During the F2F session for instance teammates were seated around tables and were able to see many diverse facial expressions read seemingly nondeliberate body language and benefit from other nonverbal cues in addition of course to being able to talk to one another When one participant for instance appeared to be just sitting quietly and unproductively at the table while others were busy analyzing clues on sheets of paper and the whiteboard a teammate interceded and asked if something was wrong The response suggested that this participant was unsure how to approach factoid analysis and hence was having difficulty integrating the myriad clues into a likely plot So the teammate diverted from his own problem-solving task and suggested filling in a matrix to help the other person organize factoids

This turned out to be highly appropriate in the ELICIT task environment where as in many teamwork contexts the team may only be as effective as its weakest link For instance even one important factoid left unshared can potentially preclude a team from solving the problem Although we stressed the importance of ldquoteamworkrdquo and ldquoknowlshyedge sharingrdquo to participants none of the experiment instructions suggested that teammates should try to help one another in manners along such lines Nonetheless this participant knewmdashpresumably through decades of prior experience with group interactionmdashhow to both recognize and intercede in a teammatersquos problem As elaborated next we did not observe equivalent behavior in the SL virtual world

This finding provides further insight into why F2F interaction reigned supreme over virtual world teamwork in the experiment Recall that participants were recruited openly for the study and selected at random for placement into different groups As such they had to form new teams comprised of unfamiliar people plan how to accomplish the experiment tasks via newly formed teams and learn how to work together all simultaneously with performance on the task of solving the terrorist plot Recall further that such work was performed in a time-critical task environment as noted previously In this particular ELICIT task environment the SL virtual world did not appear to support participantsrsquo ability to form and learn to work together on new teams as well as F2F interaction did Alternatively it is conceivable that for other task environments (esp those involving well-established teams or a longer time period for teammates to work together) virtual world social media applications may fare better

434 Interface fidelity This teamwork finding also provides insight into our secshyond research question particularly where teamwork as opposed to individual performance is important The SL virtual world is highly immersive Once participants appeared to gain proficiency in terms of maneuvering and communicating in-world through their avatars we observed behaviors consistent with the kinds of presence copresence psychological engrossment and affective experience noted in the literature summarized previously For

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instance particularly for participants with little or no prior SL experience in overhearshying comments they would make to themselves and others we noticed subtle but persistent shifts in how these people referred to their avatars At first most such participants referred to avatars in third person (eg ldquothose thingsrdquo ldquothat characterrdquo ldquomy avatarrdquo) but over time these same people began using the first person (eg ldquoIrsquom flyingrdquo ldquolook at me nowrdquo ldquoI [my avatar] canrsquot turn to see the [common projection] screenrdquo) In contrast participants with prior SL experience reflected this same kind of affect from the beginning

However even such a rich immersive 3D interface lacks the resolution and fidelity of F2F interaction in the physical world Participantsrsquo avatars resembled their users recshyognizably and they could exhibit deliberate emotive actions and other nonverbal cues but inadvertent facial expressions and subtle body language were beyond the capabilities of avatars within this SL virtual world Although the interface appeared to generate considershyable presence copresence psychological engrossment and affective experience something was different than with F2F interaction in terms of supporting the kinds of organizational knowledge teamwork discussed previously

Moreover such difference may not have hinged solely on the absence of voice comshymunication in the virtual world case Recall for instance how the struggling teammate did not cry out or ask for help in other vocal ways rather another person noticed nonverbal cues suggesting a problem requiring intervention Coupled with usersrsquo comparative lack of experience with SL and other virtual worlds relative to that of F2F interactionmdashin addishytion to the time-critical task environment and participantsrsquo lack of prior experience working with one another as teammatesmdashit was probably much more difficult within the SL virtual world to notice the kinds of subtle yet important nonverbal cues discussed previously with respect to this struggling teammate Even if participants understood that it was important to help teammates who were experiencing difficulties limitations of the SL interface (esp with respect to F2F interaction) may not have enabled them to do so readily

This finding provides additional insight into why F2F interaction reigned supreme over virtual world teamwork in the experiment The SL virtual world interface did not support the kinds of nonverbal perceptual activities that appeared to be important for productive organizational knowledge teamwork in this experiment In particular our obsershyvations and findings suggest that richness of avatar detail avatar expressiveness nonverbal cuing inadvertent communication and like interface design factors may be critically important in terms of enabling participants to sense and intercede to mitigate teammatesrsquo work difficulties

5 CONCLUSION

Knowledge is key to competitive advantage and the social reach of knowledge can amplify its power in terms of organizational performance Leveraging such amplishyfication social media applications are expected broadly to improve the performance of organizational work which highlights teams groups and organizations as particularly appropriate units of analysis The technological intermediation of knowledge work within and between such social aggregations of people remains a complex endeavor however This pertains in particular to immersive 3D environments supporting virtual social and game worlds As with other social media applications such virtual worlds can amplify knowledge broadly but they also offer potential to approach the sensations of face-toshyface (F2F) interaction in terms of presence copresence psychological engrossment and affective experience

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In this exploratory study we employ both quantitative and qualitative research techshyniques to investigate how teamwork performed through virtual worlds can approach that accomplished via F2F interactions Quantitatively we conduct a pilot laboratory experishyment to assess comparative team performance directly through both virtual world and F2F interaction and we summarize statistically significant evidence that team performance of organizational knowledge work via F2F physical interaction exceeds that accomplished through our immersive SL virtual world

We also elucidate considerable qualitative insight to help explain why such quantishytative results obtain to address our research questions to inform the design of follow-on experiments and to guide organizational implementation in practice In particular four key interrelated findings and insights are particularly noteworthy in light of our research questions (1) experience with virtual worlds in general (2) time criticality of the task environment (3) teamwork (esp within a particular virtual world but also via F2F team interaction) and (4) fidelity of the virtual world user interface

If we seek to generalize these insights across all social media applications (eg social networking microblogging collaborative projects in addition to virtual worlds) as a distinct class of technological intermediaries then we may wish to look more closely at variables such as social media experience (eg peoplersquos experience with social media applications in general) for instance as we design select and implement particular social media applications As a testable research proposition people coming to organizations with social media experience in general (eg growing up with such technologies employing social media outside of the workplace feeling as natural interacting with others via social media as via F2F) are likely to outperform those learning to use such applications anew regardless of which specific application is involved

People working with social media applications will require experience with their enabling technologies before being expected to accomplishmdashmuch less amplify knowledge and performance through extended social reachmdashthe kinds of knowledge development and sharing tasks envisioned by many in the modern Web 20 workplace This suggests that the potential for competitive advantage based on such workplace social media integration may hinge greatly on how peoplersquos social media experience levels in one particular orgashynization compare to levels present in competitor organizations Hence hiring people with ample experiencemdashor training people to increase their skill levelsmdashregarding social media applications envisioned for workplace integration emerges as an important consideration in terms of knowledge-based competitive advantage in this domain This appears to represhysent a critical consideration for organizations interested in implementing any class of social media applications

Further we learn how the time-critical task environment designed into this experishyment may have disadvantaged team knowledge work performance via virtual worlds more than would be the likely case for tasks performed under looser time constraints If we seek to generalize across task environments then we may wish to look more closely at time-criticality (eg how much time pressure participants sense) for instance As a testable research proposition the less time-critical the task environment associated with technologshyical intermediation of collaborative knowledge work the more effective virtual world and other social media applications may become with respect to F2F interaction For organishyzations interested in implementing virtual world social media applications results suggest also that they should examine critically the task environments targeted for social media support looking in particular for environments that leverage the capabilities of (and do not prejudice against) social media applications

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Although peoplersquos virtual world experience may never match levels amassed in terms of (natural and everyday) F2F interaction selecting tasks that are appropriate for intermeshydiation via virtual world (and other) social media applications becomes prominent in terms of teamwork efficacy In other words many tasks may lend themselves well to social media intermediation but many others may simply be accomplished best via F2F interaction instead Of course where F2F interaction is infeasiblemdashor prohibitively expensivemdashone or more technologically intermediated means of interaction will be required Travel time and travel cost represent seemingly important complementary considerations to tradeoff against the apparent performance advantages associated with F2F interaction versus virtual world social media applications

Experience appears to play an important role with respect to the task environment also but results point to team experience as a key performance factor Notwithstanding the kind of individual virtual world experience discussed previously it may be insufficient for people to train and practice working via virtual worlds when doing so to accomplish only individual organizational tasks Although individual task performance is clearly important for organizational knowledge work in order to extend the reach of knowledge through social media applications effective teamwork will remain critical This mirrors how even a sports team comprised of all-star players can fail abjectively without abundant training and practice as a team As a testable research proposition teams of people with substantial prior experience working together as teammates are expected to outperform other parshyticipants with no or negligible such experience through either F2F interaction or virtual world intermediation Further teams of people with substantial prior experience working together as teammates within virtual environments are expected to outperform other particshyipants with no or negligible such experience through both F2F interaction and virtual world intermediation

Moreover results from the experiment suggest that amplified organizational perforshymance through social media applicationsmdashand the corresponding potential for sustaining knowledge-based competitive advantagemdashin the workplace could depend directly on how teams are assembled (eg composition and team experience of teammates) Even if a particular organization can gain some competitive advantage say via first-mover impleshymentation of social media applications in highly productive ways such organization may not be able to sustain its competitive advantage over other implementing organizations that integrate the technology more deftly into work teams Alternatively where such first-mover organization can identify the most appropriate social media applications for various team task environments and can assemble teams of knowledge workers that are able to intershyact fluidly facilitate teamwork and help overcome performance difficulties within such environments any resulting competitive advantage may become less ephemeral and offer greater potential in terms of sustainability

Continuing with the idea of generalization if we seek to limit it to only virtual world social media applications as a class then we may wish to look more closely at interface design (eg representational fidelity relative to F2F interaction) for instance Particularly in teamwork contexts as noted where the team may only be as effective as its weakest link users may need to observe teammates to diagnose performance problems that they may be having and to intercede with corrective information and action where necessary Since many of the cues suggesting such performance difficulties can be subtle inadvertent and nonverbal higher fidelity avatar representation within virtual worlds (eg including more even unintentional cues) appears to represent an important design factor also

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This would appear to be particularly important where teammates have negligishyble experience either with virtual world interaction or working together in teams (or both) Richness of avatar detail avatar expressiveness nonverbal cuing inadvertent communication and like interface design factors may be critically important in terms of enabling participants to sense intercede and mitigate teammatesrsquo work difficulties As a testable research proposition the more that such virtual world social media applishycations can replicate the perception of F2F interaction and teamwork the more that people lacking experience with virtual worldsmdashand even people with such experience but who have not worked together on teamsmdashmay be able to employ their natural cogshynitive and social abilities to work effectively despite their relative lack of virtual world experience

Like all research investigations this exploratory study is subject to common limshyitations (eg pertaining to the pilot experiment design apparatus task environment measurements generalizability) and we are limited like most in terms of how much progress can be achieved through a single study and articulated via one published article Indeed our study raises as many research questions as it answers and its exploratory nature necessarily lacks the kind of precision and control characteristic of highly well-informed theory-rich positivistic laboratory experiments conducted to test hypotheses about mature topics Further this study focuses expressly on leveraging social media applications for competitive advantage in the workplace as such it does not address the broad and active proliferation of social media in peoplersquos personal lives which is admittedly important but beyond the scope of this investigation

Nonetheless our integration of quantitative work with qualitative analysis of a pilot laboratory experiment produces insights and explanations beyond the statistics themselves We generate numerous informed suggestions and propositions for follow-on research to extend the results of the present investigation and we outline guidelines for practical applishycation by organizations interested in implementing virtual world (and other) social media applications We also identify four key findings and insights that are particularly noteworshythy in light of our research questions and we elucidate the highly interrelated nature of such findings and insights suggesting the likelihood of strong interaction effects in addition to likely main effects individually Identifying and linking these findings and insights helps us to make a new contribution to the large literature on technological intermediation of knowlshyedge work and to add new knowledge specific to leveraging social media applications to extend knowledge reach and pursue competitive advantage

We would feel uncomfortable speculating on a specific theoretical model (eg which factors may influence mediate moderate or otherwise affect other factors in addition to task performance) but such model would appear to be far from simple and univarishyate Much additional research is called for theoretical and empirical alike Likewise we would feel uncomfortable attempting to outline a single implementation strategy for all organizations but the kinds of considerations identified through this study would appear to be important for any such organization to consider This applies in particular to expeshyrience with virtual worlds in general time criticality of the task environment teamwork (esp within a particular virtual world but also via a F2F team interaction) and fidelity of the virtual world user interface We can envision a very productive mutually supportshyive program of interaction between research and practice toward such end and as our work along these lines continues we invite other scholars and managers alike to engage with us

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ACKNOWLEDGEMENTS

We wish to thank Bryan Hudgens and other Information Science PhD students for their assistance with the experimentation and analysis reported in this article

REFERENCES

Aldrich C 2005 Learning by doing A comprehensive guide to simulations computer games and pedagogy in e-learning and other educational experiences New York Wiley

Allen M E Mabry M Mattrey J Bourhis S Titsworth and N Burrell 2004 ldquoEvaluating the effectiveness of distance learning A comparison using meta-analysisrdquo Journal of Communication 54(3)402ndash420

Bates A W and G Poole 2003 Effective teaching with technology in higher education Foundations for success San Francisco CA Jossey-Bass

Baym N K 2000 Tune in log on Thousand Oaks CA Sage Becerra-Fernandez I and R Sabherwal 2001 ldquoOrganizational knowledge management A continshy

gency perspectiverdquo Journal of Management Information Systems 18 (1)23 Beersma B J R Hollenbeck S E Humphrey H Moon D E Conlon and D R Ilgen 2003

ldquoCooperation competition and team performance Toward a contingency approachrdquo Academy of Management Journal 46(5)572ndash590

Bergin R D A A Adams R Andraus B J Hudgens J G Lee and Mark E Nissen 2010 ldquoCommand and control in virtual environments Laboratory experimentation to compare virtual with physicalrdquo Santa Monica CA CCRP June 2010

Bernard H R 1998 Handbook of methods in cultural anthropology Walnut Creek CA Altamira Press

Brown J S and R P Adler 2008 ldquoMinds on fire Open education the long trail and learning 20rdquo Educause 4317ndash32

Cacas M 2012 ldquoSharing Homeland Security information The Agencyrsquos intelligence office builds bridges with the law enforcement communityrdquo Signal February 2012 31ndash32

Castronova E 2005 Synthetic worlds The business and culture of online games Chicago University of Chicago Press

Chidambaram L and I Zigurs eds 2001 Our virtual world The transformation of work play and life via technology Hershey PA Idea Group

Choi S Y H Lee and Y Yoo 2010 ldquoThe impact of information technology and transactive memory systems on knowledge sharing application and team performance A field studyrdquo MIS Q 34(4)855ndash870

Clark R 1983 ldquoReconsidering research on learning from mediardquo Review of Educational Research 53445ndash459

Cole R E 1998 ldquoIntroductionrdquo California Management Review 40(3)15ndash21 Daft R L and R H Lengel 1986 ldquoOrganizational information requirements media richness and

structural designrdquo Management Science 32(5 Organization Design)554ndash571 Dennis A R R M Fuller and J S Valacich 2008 ldquoMedia tasks and communication processes

A theory of media synchronicityrdquo MIS Q 32(3)575ndash600 Dennis A R J S Valacich C Speier and M G Morris 1998 ldquoBeyond media richness An empirshy

ical test of media synchronicity theoryrdquo Proceedings of the Thirty-First Hawaii International Conference on System Sciences Vol 1 48ndash57

Ducheneaut N and R J Moore 2005 ldquoMore than just lsquoXPrsquo Learning social skills in massively multiplayer online gamesrdquo Interactive Education amp Smart Technology 289ndash100

Ellaway R H and D Topps 2010 ldquoPreparing for practice Issues in virtual medical educationrdquo In Emerging ethical issues of life in virtual worlds (pp 101ndash118) Charlotte NC Information Age Publishing

Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


Elliott D W F Helsen and R Chua 2001 ldquoA century later Woodworthrsquos (1899) two-component model of goal directed aimingrdquo Psychological Bulletin 127(3)342ndash357

Fine G A 1983 Shared fantasy Role-playing games as social worlds Chicago University of Chicago Press

Giddens A 1984 The constitution of society Outline of the theory of structuration Berkeley University of California Press

Goel L I Junglas and B Ives 2009 ldquoVirtual worlds as platforms for communities of practicerdquo In Knowledge management and organizational learning edited by R Sharda S Voszlig and W R King (Vol 4) New York Springer US

Goffman E 1974 Frame analysis Cambridge MA Harvard University Press Grant R M Winter Special Issue 1996 ldquoToward a knowledge-based theory of the firmrdquo Strategic

Management Journal 17109 Guzzo R A and M W Dickson 1996 ldquoTeams in organizations Recent research on performance

and effectivenessrdquo Annual Review of Psychology 47(1)307 Homer 2008 The Odyssey Translated by S Butler edited by D Stevenson Samuel Butler

Translation ed Cambridge MA Internet Classics Archive Hudson K and M E Nissen 2011 Understanding the potential of virtual environments for

improving C2 performance US Department of Defense Command amp Control Research Program

Kaplan A M and M Haenlein 2010 ldquoUsers of the world unite The challenges and opportunities of social mediardquo Business Horizons 53(1)59ndash68

Keane B T and R M Mason 2006 On the nature of knowledge Rethinking popular assumptions Koloa Kauai HI IEEE

Laurel B 1991 Computers as theatre Reading MA Addison-Wesley Lehdonvirta V 2010 ldquoVirtual worlds donrsquot exist Questioning the dichotomous approach in MMO

studiesrdquo Game Studies 10(1) Linden Labs 2010 ldquoSecond Liferdquo Retrieved from httpsecondlifecom (accessed August 20

2010) Malhotra A amp A Majchrzak 2009 ldquoCommunication context-dependent technology use in virtual

teamsrdquo ICIS 2009 Proceedings Paper 181 Malhotra A and A Majchrzak 2012 ldquoHow virtual teams use their virtual workspace to coordinate

knowledgerdquo ACM TransManageInfSyst 3(1) 61ndash614 Martiacutenez-Moreno E P Gonzaacutelez-Navarro A Zornoza and P Ripoll 2009 ldquoRelationship task

and process conflicts on team performance The moderating role of communication mediardquo International Journal of Conflict Management 20(3)251ndash268

Maznevski M L and K M Chudoba 2000 ldquoBridging space over time Global virtual team dynamics and effectivenessrdquo Organization Science 11(5) 473ndash492

McLennan K J 2008 The virtual world of work How to gain competitive advantage through the virtual workplace Charlotte NC Information Age Pub

Meyer D E D E Irwin A M Osman and J Kounios 1998 ldquoThe dynamics of cognition and action Mental processes inferred from time-accuracy decompositionrdquo Psychological Review 95183ndash237

Montoya M M A P Massey and N S Lockwood 2011 ldquo3D collaborative virtual environments Exploring the link between collaborative behaviors and team performancerdquo Decision Sciences 42(2)451ndash476

Morgan G 1997 Images of organization 2nd ed Thousand Oaks CA Sage Publications Nahapiet J and S Ghoshal 1998 ldquoSocial capital intellectual capital and the organizational

advantagerdquo The Academy of Management Review 23(2)242ndash266 National Commission on Terrorist Attacks upon the United ed 2004 The 911 commission

report Final report of the National Commission on Terrorist Attacks upon the United States Washington DC US Government Printing Office

Nissen M E 2006 Harnessing knowledge dynamics Principled organizational knowing amp learning Hershey PA IRM Press

Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


Nissen M E 2010 Command and control in virtual environments Using contingency theory to understand organization in virtual worlds Monterey CA Naval Postgraduate School

Nissen M E and T A Leweling 2010 ldquoKnowledge sharing as a contingency in the design of counterterrorism organizationsrdquo International C2 Journal 4(2)1ndash30

Nonaka I 1994 ldquoA dynamic theory of organizational knowledge creationrdquo Organization Science 5(1)14

Oravec J A 2001 ldquoOnline recreation and play in organizational life The Internet as virtual contested terrainrdquo In Our virtual world The transformation of work play and life via technology edited by L Chidambaram and I Zigurs (pp 124ndash140) Hershey PA Idea Group

Palen L S R Hiltz and S B Liu 2007 ldquoOnline forums supporting grassroots participation in emergency preparedness and responserdquo Communications of the ACM 50(3)54ndash58

Parity Communications Inc 2006 Experiments in command and control within edge organizations Final report Boston Author

Plamondon R and A M Alimi 1997 ldquoTimeaccuracy trade-offs in target-directed movementsrdquo Behavioral and Brain Sciences 20279ndash303

Porter D 1997 Internet culture New York Routledge Rogers R D and S Monsell 1995 ldquoCosts of a predictable switch between simple cognitive tasksrdquo

Journal of Experimental Psychology 124207ndash231 Russell T L 1999 The no significant difference phenomenon Raleigh NC North Carolina State

University Office of Instructional Telecommunication Samarah I S Paul and S Tadisina 2007 ldquoCollaboration technology support for knowledge convershy

sion in virtual teams A theoretical perspectiverdquo Hawaii International Conference on System Sciences 137

Sandelowski M 2000 ldquoCombining qualitative and quantitative sampling data collection and analysis techniques in mixed-method studiesrdquo Research in Nursing amp Health 23(3)246ndash255

Sarcevic A L Palen J White K Starbird M Bagdouri and K Anderson 2012 Beacons of hope in decentralized coordination Learning from on-the-ground medical twitterers during the 2010 Haiti earthquake Seattle WA ACM

Schijns Janet 2007 ldquoThe fine line between reality virtualityrdquo CRN (1242)20 Schutz A 1971 Collected papers I The problem of social reality The Hague Martinus Nijhoff Scott W R 1995 Institutions and organizations Foundations for organizational science Thousand

Oaks CA SAGE Short J E Williams and B Christie 1976 The social psychology of telecommunications London

John Wiley Sicart M 2010 ldquoThis war is a lie Ethical implication of massively multiplayer online game designrdquo

In Emerging ethical issues of life in virtual worlds (pp 177ndash196) Charlotte NC Information Age Publishing

Spender J-C 1996 ldquoMaking knowledge the basis of a dynamic theory of the firmrdquo Strategic Management Journal 1745

Spradley J 1980 Participant observation New York Holt Rinehart and Winston Teigland R 2010 ldquoWhat benefits do virtual worlds provide charitable organizations A case study

of peace trainmdashA charitable organization in Second Liferdquo In Emerging ethical issues of life in virtual worlds (pp 119ndash136) Charlotte NC Information Age Publishing

Wankel C and S Malleck eds 2010 Emerging ethical issues of life in virtual worlds Charlotte NC Information Age Publishing

Witmer B G 1998 ldquoMeasuring presence in virtual environments A presence questionnairerdquo Presence 7(3)225

Yee N 2006a ldquoThe demographics motivations and derived experiences of users of massively multishyuser online graphical environmentsrdquo Presence 15(3)309

Yee N 2006b ldquoThe labor of fun How video games blur the boundaries of work and playrdquo Games and Culture 1 68

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2013


AUTHOR BIOS

Mark E Nissen is Professor of Information Science and Management and Director of the Edge Center at the US Naval Postgraduate School His research focuses on dynamic knowing and organizing He views work technology organization and people as an integrated design problem and he has concentrated for some time on the dynamshyics of knowledge flows culminating in his second book entitled Harnessing Knowledge Dynamics Principled Organizational Knowing amp Learning (IRM Press 2006) Currently he is pursuing a multidisciplinary initiative to understand the potential and limitations of organization and management through virtual environments Cyberspace and coopershyative autonomous systems Markrsquos 150+ publications span information systems project management organization studies knowledge management and related fields In 2000 he received the Menneken Faculty Award for Excellence in Scientific Research the top research award available to faculty at the Naval Postgraduate School In 2001 he received a prestigious Young Investigator Grant Award from the Office of Naval Research for work on Knowledge Flow Theory In 2002ndash2003 he was Visiting Professor at Stanford integrating Knowledge Flow Theory into agent-based tools for computational modeling In 2004 he established the Edge Center for multi-university multidisciplinary research on what the Military terms command amp control (esp organizing managing and knowing) Before his doctoral work at the University of Southern California he acquired over a dozen yearsrsquo management experience in the aerospace and electronics industries

Richard D Bergin is an Adjunct Assistant Professor of Information Sciences at the Naval Postgraduate School Over the past seven years he has been teaching for the Center for Homeland Defense and Security (CHDS) During this time he has advised more than 70 CHDS Masterrsquos thesis projects and is currently serving on the editorial board for the Homeland Security Affairs Journal Mr Bergin earned his Bachelor of Science degree in Business Administration with a dual emphasis in Information and Operations Management He holds two Master of Science degrees in Information Sciences and Management Information Systems from USC and Claremont Colleges respectively He is currently a PhD student at the Naval Postgraduate School in the Information Sciences department His area of research focuses on virtual copresence and Knowledge Flow Theory Mr Bergin has taught courses at both the graduate and undergraduate level Courses taught at the Marshall School of Business at the University of Southern California (USC) include Information Technology Strategy E-Business Business Systems Networking and Information Security Coursers taught for the Information Sciences and for the Homeland Security departments at the Naval Postgraduate School include Management Information Systems Information Technology Strategy and Technology for Homeland Security Prior to his academic assignments he founded and acted as CEO of Internet Productionsmdasha preshymier software development company that specialized in offering innovative e-commerce applications for the World Wide Web Mr Bergin has an extensive background in operashytions and production management He worked in the Aerospace and Networking industry and the Communication Devices industry as a production manager systems analyst and senior planner

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2013

Journal of Organizational Computing and Electronic Commerce 23 84ndash109 2013 ISSN 1091-9392 print 1532-7744 online DOI 101080109193922013748612

KNOWLEDGE WORK THROUGH SOCIAL MEDIA APPLICATIONS TEAM PERFORMANCE IMPLICATIONS OF IMMERSIVE VIRTUAL WORLDS

Mark E Nissen and Richard D Bergin

Naval Postgraduate School Monterey California USA

Knowledge is key to competitive advantage and the social reach of knowledge can amplify its power in terms of organizational performance Leveraging such amplification social media applications are expected broadly to improve the performance of organizational work which highlights teams groups and organizations as particularly appropriate units of analshyysis The technological intermediation of knowledge work within and among such social aggregations of people remains a complex endeavor however This pertains in particular to immersive 3D environments supporting virtual social and game worlds As with other social media applications such virtual worlds can amplify knowledge broadly but they also offer potential to approach the sensations of face-to-face (F2F) interaction in terms of presshyence copresence psychological engrossment and affective experience In this exploratory study we employ both quantitative and qualitative research techniques to investigate how teamwork performed through virtual worlds can approach that accomplished via F2F intershyactions This leads to important findings insights guidelines and suggestions for expanded research as well as enhanced practice

Keywords competitive advantage immersiveness information systems knowledge exchange presence social media technological intermediation virtual world

1 INTRODUCTION

A great many scholars have long viewed knowledge as key to competitive advantage (Cole 1998 Grant 1996 Spender 1996) Nissen (2006) explained how knowledge enables effective action effective action drives superior performance and superior performance supports competitive advantage Both tacit and explicit knowledge are considered important widely whether together as separate dimensions or apart as complementary types (Keane and Mason 2006) and the social reach of knowledge is deemed by many to amplify its power in terms of organizational performance (Nonaka 1994)

Leveraging such amplification the comparatively recent advent and continued proshyliferation of Web 20 social media applications many of which extend social reach (Nahapiet and Ghoshal 1998) and facilitate knowledge exchange (Kaplan and Haenlein

This article not subject to US copyright law Address correspondence to Mark E Nissen US Naval Postgraduate School Graduate School of

Operational amp Information Sciences 1411 Cunningham Road Room GW2006 Monterey CA 93943 USA E-mail MNissennpsedu

84

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2 BACKGROUND


21 Virtual Worlds



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3 RESEARCH DESIGN


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33 Task Environment



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34 Participants



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2013









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15

09 2

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15

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36 Measurements




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4 RESULTS








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3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






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rsity

] at

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4 Se

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2013









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15

09 2

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2013








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5 CONCLUSION


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2013


ACKNOWLEDGEMENTS


REFERENCES





















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4 Se

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Dow

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09 2

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AUTHOR BIOS



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2013







Dow

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09 2

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2 BACKGROUND


21 Virtual Worlds



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rsity

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15

09 2

4 Se

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Dow

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15

09 2

4 Se

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2013









Dow

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15

09 2

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Dow

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15

09 2

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3 RESEARCH DESIGN


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15

09 2

4 Se

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ber

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33 Task Environment



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15

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34 Participants



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15

09 2

4 Se

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15

09 2

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2013









Dow

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by [

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15

09 2

4 Se

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2013







Dow

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by [

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15

09 2

4 Se

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36 Measurements




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15

09 2

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4 RESULTS








Dow

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by [

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15

09 2

4 Se

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3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

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by [

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rsity

] at

15

09 2

4 Se

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2013









Dow

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15

09 2

4 Se

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2013








Dow

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15

09 2

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5 CONCLUSION


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ACKNOWLEDGEMENTS


REFERENCES





















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Dow

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15

09 2

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2013


























Dow

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AUTHOR BIOS



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2 BACKGROUND


21 Virtual Worlds



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15

09 2

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09 2

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2013









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15

09 2

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15

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3 RESEARCH DESIGN


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15

09 2

4 Se

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2013









33 Task Environment



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15

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34 Participants



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15

09 2

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2013









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2013









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36 Measurements




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4 RESULTS








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3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






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09 2

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5 CONCLUSION


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ACKNOWLEDGEMENTS


REFERENCES





















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AUTHOR BIOS



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3 RESEARCH DESIGN


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33 Task Environment



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34 Participants



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nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3 RESEARCH DESIGN


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









33 Task Environment



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






34 Participants



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3 RESEARCH DESIGN


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









33 Task Environment



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






34 Participants



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3 RESEARCH DESIGN


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









33 Task Environment



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






34 Participants



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









33 Task Environment



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






34 Participants



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






34 Participants



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013





36 Measurements




Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



4 RESULTS








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






3953 minus949


25 25 25 225 25 25

001

352

035

000

308

007






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013









Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013








Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






5 CONCLUSION


Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013







Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013






Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


ACKNOWLEDGEMENTS


REFERENCES





















Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013



























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


























Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013


AUTHOR BIOS



Dow

nloa

ded

by [

RM

IT U

nive

rsity

] at

15

09 2

4 Se

ptem

ber

2013

Documents

Knowledge Work Through Social Media Applications: Team Performance Implications of Immersive Virtual Worlds