User goals in social virtual worlds: A means-end ... - Semantic Scholar

Computers in Human Behavior 26 (2010) 218–225

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User goals in social virtual worlds: A means-end chain approach Yoonhyuk Jung a,*, Hyunmee Kang b,1 a b

Department of Information Systems and Decision Sciences, Louisiana State University, Baton Rouge, LA 70803, USA The Manship School of Mass Communications, Louisiana State University, Baton Rouge, LA 70803, USA

a r t i c l e

i n f o

Article history: Available online 1 November 2009 Keywords: Virtual worlds Social virtual worlds User goal Goal structure Means-end chain analysis

a b s t r a c t The purpose of this study is twofold: first, to investigate user goals in social virtual worlds; second, to introduce a methodological alternative (i.e., a means-end chain approach) for analyzing user goals in cyberspaces. The data were acquired from a web survey, and were analyzed by means-end chain analysis (MECA), which produces users’ goal structure in reference to a hierarchical system of interrelated goals (Olson & Reynolds, 1983). The results show that people come to social virtual worlds to satisfy their social and hedonic needs, and to escape from real world constraints, as do virtual community members and virtual gamers; they also pursue unique activities, such as creating virtual objects and selling them. On the other hand, by clarifying relations among users’ goals, MECA provides a richer explanation for user goals than prior research which only offers separate user goals for cyberspace users without explanation of relationship among goals. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Virtual worlds (VWs) refer to computer-simulated, graphicbased, virtual environments that are accessed by multiple users. They have grown dramatically over the last decade as supporting technologies (e.g., virtual reality, broadband) have been advanced. World of Warcraft, which is the largest gaming virtual world (GVW), had over 10 million subscribers as of the end of 2008 (MMOGchart.com, 2008). Virtual gaming even became a professional sport with leagues, sponsorships, and spectators in South Korea (Castronova, 2005). Recently a new type of VWs that stress social interaction and user empowerment has appeared, namely social virtual worlds (SVWs) (e.g., Second Life, There.com). GVWs are characterized by a pre-defined structure and quest-driven behaviors, whereas SVWs have emergent structures that are created by users under minimum constraints (Juul, 2005). SVWs offer their users an opportunity to determine their experiences in the virtual worlds for themselves (Dreyfus, 2008). This autonomy makes the worlds places that are filled with diverse activities such as socializing, learning, sprouting virtual business, entertainment, and so on. The increased number of users reflects SVWs’ popularity. For instance, Second Life, a popular SVW, announced that its subscribers exceeded 12 million as of February 2008 (SLOG, 2008). The amount of real-money trading in SVWs has also been increasing exponentially. User-to-user trading of vir-

* Corresponding author. Tel.: +1 225 578 2126; fax: +1 225 578 2511. E-mail addresses: [email protected] (Y. Jung), [email protected] (H. Kang). 1 Tel.: +1 225 578 2336; fax: +1 225 578 2125. 0747-5632/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2009.10.002

tual goods in Second Life was estimated to be 360 million dollar in 2008 (Linden Lab, 2009). In addition to SVWs’ quantitative growth, their potential as a marketing channel and a collaboration tool has attracted both managers and researchers’ attention. Several corporations, such as Nike, Adidas, Levi’s, GM, and Toyota, create virtual products, which simulate real products, and enable SVW users to have virtual use experiences. Also, IBM trains new employees in organizational culture and work processes, holds distant meetings, and operates customer service centers in SVWs (Hobson, 2007). Regarding SVWs as a future platform for e-learning, many educational organizations including Harvard University have been increasingly using SVWs. Despite renowned interest in SVWs, there is currently little empirical research on SVWs, particularly on users’ perceptions and behaviors in SVWs. SVWs’ distinctive characteristics may cause users to access and behave in such environments differently than in other cyberspaces, such as text-based virtual communities and even GVWs. The initial step to study SVW users may be a question of ‘why do people come to SVWs?’; that is, user goals for accessing SVWs. Knowledge of these goals would benefit businesses that are considering the strategic use of SVWs, as well as SVW operators in terms of attracting and retaining more users, and also the researchers that are begging to investigate the nature of SVW users. In order to comprehend user goals for accessing SVWs, this study employed means-end chain analysis (MECA), which is considered an effective method for eliciting people’s goal structure (i.e., goals and their relations) when they approach an object or an event (Olson & Reynolds, 1983). Prior studies that investigated users’ goals for participating in virtual communities (Ridings & Ge-

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Y. Jung, H. Kang / Computers in Human Behavior 26 (2010) 218–225

fen, 2004) and gaming virtual worlds (Bartle, 2003; Yee, 2006), produced sets of separate individual user goals but offer little explanation about the relations among goals. Studies of relations among goals, or a goal structure, can provide more information about users’ goal-oriented behavior than those of isolated goals (Pieters, Baumgartner, & Allen, 1995). Because it can clarify goal structure, a means-end analysis is expected to offer richer information about user goals for participating in SVWs. This study thus contributes to research on cyberspace users, in terms of offering a methodological novelty, and demonstrates an empirical study of an emerging cyberspace. 2. Theoretical background 2.1. Social virtual worlds Despite its diverse definitions, the term virtual world has been commonly used to indicate a computer-simulated persistent spatial environment that supports synchronous communication among }m & multiple users who are represented by avatars (Holmstro Jakobsson, 2001; Jakobsson, 2006). This concept of VWs is similar with the notion of MMOGs or massive multiplayer online games, such as World of Warcraft. However, MMOGs have been mainly used to specify one type of VW that has a pre-defined theme and plot and clarifies users’ performances (e.g., level-ups). Although they (or GVWs) still occupy the majority of VWs, the other distinctive VWs, where users create their experiences for themselves and have diverse social interaction, have dramatically increased. In the present study, we call these types of VWs social virtual worlds (SVWs) and differentiate them from gaming virtual worlds (GVWs). In GVWs, user activities are based on pre-defined themes and plots which the game designers imagined and produced. Their activities usually aim at a quest or level-ups rather than social interactions. On the other hand, the lack of predetermined storyline of SVWs makes them distinct from GVWs, and this openness has attracted various kinds of users (Warburton, 2009). SVWs endow users with the ability to personalize their virtual experiences, which induce various social interactions under minimum constraints. As a result, SVWs’ nature of minimum rules leads to diversity in members’ behavior (Juul, 2005). Furthermore, as some SVWs (e.g., Second Life, There), supports transactional systems (e.g., virtual currency, sanctioned virtual market), and allow users

to create virtual objects, users’ activities are extended to economic activities, that is, production and real-money trade of virtual objects. This autonomy distinguishes SVWs’ characteristics from GVWs’. 2.2. User goals in social cyberspaces User goals for accessing SVWs may be analogous to motivations for joining virtual communities (VCs) and playing GVWs. VCs are cyberspaces in which people communicate and form networks of personal relationships (Rheingold, 1993). SVWs that support 3D interfaces and real-time avatar interactions can be differentiated from conventional VCs that depend on asynchronous text-based interactions. Nevertheless, because of the social nature, SVWs are usually considered an extension of VCs. Prior studies note that information exchange, social relations, psychological support, and entertainment are common goals for joining VCs (Bressler & Grantham, 2000; Hagel & Armstrong, 1997; Wellman, 1996). Information exchange indicates that the members share information generated by other members rather than merely accessing information that the website operator provides (Hagel & Armstrong, 1997). VC members also interact with other members and are embedded in web of personal relationships in VCs; that is, social relations (Rheingold, 1993). Additionally, people satisfy psychological needs, such as emotional support, a sense of belonging, and encouragement in VCs, and also come to VCs for entertainment. Ridings and Gefen (2004) empirically examined users’ motivation to join VCs and confirm that those four goals are the main motivational forces. Bartle (2003) classifies GVW users into four types according to their goals: socializers, explorers, achievers, and controllers. Socializers attempt to form groups and complete shared objectives; explorers seek new places in a GVW; achievers pursue the gradual accumulation of wealth and reputation in GVWs; and controllers want to compete with and defeat others. Yee (2006) empirically examined Bartle’s GVW user typology through an exploratory factor analysis, and proposes a new framework that consists of three overarching goals: achievement, socializing, and immersion. Each overarching purpose is composed of sub-motivations. Achievement includes advancement, mechanics, and competition; socializing includes relationships and teamwork; and immersion includes discovery, role-playing, customization, and escapism (see Table 1).

Table 1 User goals in cyberspaces. Type of cyberspaces

Purposes

Description

References

Virtual communities

Information exchange Social relations Psychological support Entertainment

Attain and transfer information about a topic, learn new things

Ridings and Gefen (2004)

Get involved with other members Attain and offer emotional support

Gaming virtual worlds

Socializers Explorers Achievers Controllers

Form groups and complete shared objectives Seek new places Pursue the gradual accumulation of wealth and reputation Compete with and defeat others

Bartle (2003)

Gaming virtual worlds

Achievement

Gain power and accumulate wealth or status Analyze the underlying rules and system to optimize character performance Challenge and compete with others Help and chat with other players Form long-term meaningful relationships with others Be part of a group effort Find things that most other players do not know about Create a persona with a background story and interact with others Customize the appearance of their character Avoid thinking about real life problems

Yee (2006)

Social

Immersion

Attain fun

Advancement Mechanics Competition Socializing Relationships Teamwork Discovery Role-playing Customization Escapism

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All prior studies that investigate user goals for inhabiting cyberspaces provide sets of isolated goals without addressing the relations among those goals. Although the prior studies recognize that goals do not suppress each other (i.e., a user may have multiple goals) (Bartle, 2003; Yee, 2006), they overlook how they are connected. As a result, the prior studies provide just a set of separate goals for social cyberspaces. This limit may preclude drawing a more comprehensive picture of user goals because goals need to be explained within a goal structure that includes a hierarchical system of interrelated goals (Pervin, 1989). Accordingly, this study attempts to clarify a goal structure beyond finding isolated goals by employing a means-end chain analysis. 2.3. User goals and a means-end chain analysis A goal is a desired outcome of an action (Locke & Latham, 1990). Many researchers argue that goals exist within a hierarchical system where a goal is located between its superordinate and subordinate goals, and furthermore, each goal is a means to achieve its superordinate goal (Kruglanski et al., 2002; Newell & Simon, 1972; Pervin, 1989). Means-end chain analysis (MECA) stems from the idea of a hierarchical goal system. The analysis posits that product or service attributes represent the means by which consumers achieve benefits and important personal values (i.e., ends) (Gutman, 1982; Olson & Reynolds, 1983). In other words, MECA is an approach for discovering the important meanings that consumers ascribe to a product or service’s attributes (Voss, Gruberb, & Szmigin, 2007). The analysis assumes that consumer knowledge is hierarchically organized by levels of abstraction (Reynolds & Whitlark, 1995), and focuses on a product or service’s meanings at three levels of abstraction: attributions, consequences, and values. Attributes refer to a product or service’s physical or observable properties: consequences are the benefits attained by the attributes; and values imply highly abstract motivation that guides usage behavior (Klenosky, 2002). An attribute–consequence–value chain is usually expressed by a hierarchical map, which consists of nodes (i.e., attributes, consequences, and values) and relationships among them. A means-end chain analysis typically depends on a laddering interview technique, which has been comprehensively used in consumer research that tries to understand consumers’ preferences toward products or services (e.g., Klenosky, 2002; Reynolds & Rochon, 2001), and organization research, which elucidates an organization’s strategic values and decision-making structures (e.g., Peffers, Gengler, & Tuunanen, 2003; van Rekom, van Riel, & Wierenga, 2006). For helping respondents elicit lower or higher levels of abstractions for the concepts, the technique aims to understand the way in which the respondent sees the world (Reynolds & Gutman, 1988). A laddering procedure typically includes three questions: the attribute question (What attribute makes the product (or service) attractive to you?), the consequence question (Why is the attribute important or desirable to you?), and the value question (Why is your response important?). In the first phase, the respondent is asked to supply the attributes of a product that affect his or her consumption decision. The respondent is then asked to explain what benefits he or she attains owing to the attributes. Finally, the respondent is asked to offer the reason why those benefits are important to him or her, namely, a justification stage. All responses are coded, and then a hierarchical map is finally produced by the level of abstraction, that is, attributes ? consequences ? values. Even though means-end chain studies that employ a laddering interview technique usually use the traditional way as stated above, some studies employ a modified MECA. The main modification appears to be a way to decide the level of abstraction and a technique for data collection. Employing network theory (see Scott,

1991), some studies calculate the abstractness of each element (concept) and use it to determine the position of the element in a hierarchical map instead of a strict specification of three levels of abstraction (i.e., attributes, consequences, and values) (Bagozzi & Dabholkar, 1994; Capozza, Falvo, Robusto, & Orlando, 2003; Pieters et al., 1995). This revised method allows researchers to know the relationships of elements without having to conduct additional work to classify elements into three levels. One critique of the laddering technique is that an answer frequently does not correspond to the question (e.g., a consequence or value answer to the attribute question). Studies that employ network theory reduce this limitation because each element has a level according to its abstractness without any label (e.g., attribute) in the revised method. On the other hand, instead of in-depth interviews, some studies use questionnaires: a technique developed by Walker and Olson (1991) (e.g., Botschen & Hemetsberger, 1998; Pieters et al., 1995; Voss et al., 2007). The advantage of a questionnaire version is that respondents themselves decide when they finish the laddering process, which may make respondents feel pressure (Botschen & Hemetsberger, 1998). In addition, compared to the in-depth interviews, a questionnaire version is a cost-effective method for data collection (Botschen, Thelen, & Pieters, 1999).

3. Methodology 3.1. Data gathering The target SVW for this study was There (www.there.com), which is a SVW equipped with a 3D environment. The reason why we chose There was that it is closer to our definition of SVWs in terms of users’ autonomy (i.e., diverse user activities including creating and selling virtual objects), and it has recently grown rapidly in terms of membership. The number of its registered members is over one million as of early 2008. In There, a member has a personal avatar that represents oneself. A member can manipulate his or her personal avatar’s face, hair, and body and put it into clothes. Also, a member can create 3D objects (e.g., chair, building, waterfall) using developer program, and perform virtual tasks with them or sell them to other members. Controlling their personal avatars, members enjoy synchronous chatting at the park or on the beach, dancing at night club, or taking a buggy in There. During two weeks, we solicited Thereians, which There users call themselves, to participate in the web-based survey. Two approaches were used to recruit participants. First, we sent group leaders of a wide diversity of groups messages which requested them to distribute our solicitation message which included the web-based survey link. Also, we directly recruited participants in various places (e.g., beaches, parks, sandboxes) of There. We first sent longed-in users a message that introduced our survey, and if they wanted to participate in the web-based survey, then we sent the link. Fifty-four Thereians, which There users call themselves, responded to our web-based survey or a questionnaire-based laddering technique.2 Participation in this study was voluntary. The participants were heterogeneous in age, gender, and educational background as seen in Table 2, indicating that There is used by varied sorts of people. The majority of the participants can be regarded as highly-attached users in that over 60% of the participants logged in There daily or several times a week and over half of the participants had used There over one year. The questionnaire consisted of three questions for a laddering analysis and five questions about demographic information. Based 2 Prior research employing a laddering technique usually has from 40 to 140 samples (51 samples in Pieters et al. (1995), 53 samples in Klenosky (2002), 82 in Voss et al. (2007), 133 samples in Bagozzi and Dabholkar (1994)). Thus, based on prior research, our number of samples is acceptable.


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4.1. Coding

Table 2 Demographics of participants. Frequency

Percent

Age

18–24 25–34 35–44 45–54 55 or older No answer

10 6 13 11 3 11

18.6 11.1 24.1 20.4 5.5 20.4

Gender

Male Female No answer

21 22 11

38.9 40.7 20.4

Education

High school Community college Undergraduate Graduate No answer

11 9 7 15 12

20.4 16.7 13.0 27.8 22.2

Logging frequency

Once a month Once a week Several times a week Daily No answer

3 4 12 24 11

5.6 7.4 22.2 44.4 20.4

Tenure

Less than 6 months 6 months to 1 year 1 year to 2 years Longer than 2 years No answer

7 6 11 19 11

13.0 11.1 20.4 35.2 20.4

on prior laddering research, we made three open questions to probe members’ goals for using There: (1) we first placed the overarching question (What are your main three purposes for using There?); (2) we then asked the downward question (What characteristics of There help you achieve each purpose?); (3) and we finally asked the upward question (Why is each purpose important to you?). In our procedure, the overarching question corresponds to the consequence question of a conventional laddering technique; the downward question corresponds to the attribute question; and the upward question corresponds to the value question (see Fig. 1). As previously stated, because our focus is on the relationships of elements rather than of their classification into attributes, consequences, and values, we used our own terms rather than those three labels. Also, based on our pilot study, we ordered the three questions. In the pilot study, most of our respondents answered their consequences or values instead of attributes to the attribute question (the downward question in our study). In this situation, we had difficulty continuing to probe because they had already answered their highly abstractive purposes. For example, when we asked the attribute question (i.e., What characteristic makes There attractive to you?), many respondents answered just fun or meeting new people which corresponded to consequences or values rather than attributes. Thus, we had to ask the attribute question again (i.e., What characteristic of There help you achieve this purpose?). In order to conduct more effective web surveybased laddering procedure, we needed to modify a laddering procedure, and placed the overarching question first. Our procedure, in which an intermediate question (consequence question) is placed first, is also consistent with thought: humans set a goal at an intermediate level rather than explicitly pursuing a highly abstract goal, and then decide operational strategies to attain it (Rifkin, 1985).

4. Results and analysis The analysis consisted of two stages: (1) coding and (2) generating a goal structure (a hierarchical goal map).

For analysis, the responses from three probing questions were coded. One of the authors coded the data using an open coding procedure in which codes were not predetermined but rather emerged from the data. This resulted in 45 detailed codes present in the data. In cases where the data contained more than one topic, multiple codes were assigned. For example, ‘‘Thereians want to be sociable and have fun” was assigned two codes – Social relations and Fun. A second coder, the other author, independently re-coded the data using the set of codes identified by the first coder. The two raters were in agreement on 292 of the 363 codes assigned (Cohen’s Kappa = 0.78), indicating an acceptable level of inter-rater reliability (Fleiss, 1981). Inter-rater disagreements were then reconciled through discussion. Finally, associated codes were grouped into 11 topics, as shown in Table 3. This categorization was also verified by the same procedure as the detailed coding procedure (Cohen’s Kappa = 0.91). 4.2. Generating a goal structure Responses to the three questions (the overarching question, the downward question, and the upward question) generated a means-end chain, or a ladder of meanings; that is, answers to the downward question pertain to a means for answers to the overarching question, and likewise answers to the overarching question correspond to a means for answers to the upward question. For instance, if a subject responds Technical features to the downward question; Social relations to the overarching question; and Amusement to the upward question, two direct linkages are created: Technical features ? Social relations, and Social relations ? Amusement. We can also consider an indirect linkage; for instance, eliciting the linkage of Technical features ? Amusement from the linkage of Technical features ? Social relations ? Amusement. Ultimately all linkages were summarized in an implication matrix which depicts the number of times each topic (code) leads to each other topic in responses (Klenosky, 2002). As can be seen in Table 4, each topic in the row leads to the other topics in the column. For instance, T5 (Social relations) led to T8 (Escapism) 2 times; T11 (Technical features) led to T10 (Amusement) 5 times. Typical means-end chain studies classify responses into attributes, consequences, and values and then produce a hierarchical structure of attributes ? purposes ? values. In order to mitigate classification errors, the current study employed an alternative method proposed by Bagozzi and Dabholkar (1994) and Pieters et al. (1995). Instead of classifying responses into three labels, this approach, which is based on network analysis (Scott, 1991), produces a hierarchical structure by comparing the number of times each element is mentioned as the means versus the end. The approach uses out-degrees and in-degrees in order to estimate abstractness of each element. Out-degrees of a particular element refer to the number of times the element serves as the source or origin (means) of linkages with other elements (i.e., the row sum of the element in an implication matrix), whereas in-degrees of the element indicate the number of times the element serves as the object or end of linkages with others (i.e., the column sum of the element in an implication matrix) (Pieters et al., 1995). Abstractness of an element is the ratio of in-degrees over in-degrees plus out-degrees of the element, and ranges from 0 to l (Pieters et al., 1995). Elements with high abstractness scores are regarded mainly as ends, while ones with low abstractness scores are thought of primarily as means. Based on the alternative approach, we created an implication matrix (see Table 4). Additionally, in order for informative analysis, this study calculated centrality of each element, which represents the degree to which the element has a central role in the structure (Knoke & Burt, 1982). Centrality is cal-

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Fig. 1. Laddering procedure.

Table 3 Topics (super-codes) and sub-codes. Topics

Codes

T1. T2. T3. T4. T5.

Addiction/hooked Creating virtual objects/decorating own avatar or property/creativity Educational tool Escaping from reality/free from disability Social interaction/meeting people/chatting/social events/dating Finding friendly users Having time with family or offline friends Helping people Worldwide availability/interacting with diverse people Exploring/traveling/adventuring/walking around Lots of places/large scale of the virtual world Economic freedom/auction system Financial/running a business/making money Acquiring new ideas Doing research Improving practical skills (programming) Free membership Good administration of There.com Tutorial & class for training how to develop virtual objects Fun/enjoyment Gaming (paintball, spades) Movies/music Relaxing/reducing stress Something to do/goofing off/killing time Buggies 3D environment/virtual reality/graphic Human-like avatars’ behavior Simple operation/easiness of navigation Teleport The map Voice/IM/Email (communication tools in There)

Addiction (removed) Creating Educational tool (removed) Escapism Social relations

T6. Exploring T7. Financial T8. Knowledge acquisition

T9. Positive administration (removed)

T10. Amusement

T11. Technical features

culated by dividing the ratio of in-degree plus out-degree of a particular element by the sum of all active cells in the implication matrix (the sum = 167 in the current study). The next step was to generate a hierarchical map according to the information in the implication matrix. In this stage, the important point was determining what linkages were included in a hierarchical goal map. Because inclusion of all linkages could decrease a map’s usefulness and informativeness, we did not embrace all linkages and decided to employ a cutoff level (Reynolds & Gutman, 1988). Following Bagozzi and Dabholkar’s (1994) method, we built Table 5 to choose a cutoff level and finally selected a cutoff of four, indicating that the included relations are counted at least four times. This cutoff level represented 30.0% of the active cells and 64.1% of the active linkages, which corresponds to a measure of variance (Gengler & Reynolds, 1995). According to the cutoff, T1 (Addiction), T3 (Positive administration), and T9 (Educational tool)

were excluded because they had no linkage to satisfy the cutoff criterion. The hierarchical goal map in Fig. 2 offers a graphical summary of the means-end structure pertinent to using a SVW. In the map, the topics are placed relative to their abstractness scores. Accordingly, the more abstract a topic, the higher it is located in the map.

5. Discussion A means-end chain analysis played an effective role in clarifying SVW users’ goal structure. The analysis is summarized in the hierarchical goal map, which provides a quick and rich understanding of SVW users’ goal structure. The findings show that SVW user goals are usually overlapped with VC members’ and GVW players’

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Y. Jung, H. Kang / Computers in Human Behavior 26 (2010) 218–225 Table 4 Implication matrix. Abstractness

Centrality

Topics

T1

0.000 0.442 0.800 0.731 0.532 0.158 0.250 0.722 0.167 0.725 0.195 In-degrees

0.018 0.257 0.060 0.156 0.461 0.114 0.096 0.108 0.072 0.413 0.246

T1. Addiction T2. Creating T3. Educational tool T4. Escapism T5. Social relations T6. Exploring T7. Financial T8. Knowledge acquisition T9. Positive administration T10. Amusement T11. Technical features

T2

1 2 2 7

0

3 1 3 19

T3

T4

3

4

1

7

T5

T6

T7

T8

T9

T10

T11

4

5 2

1

3 5

2

2

1

2 1 4 1 2 1 8

2 1 2 3 19

1 2 3 12 18 41

1 1 3

4

1 2 13

2

4 21 8 2 1 1 5 50

1 2

1 2 8

Out-degrees 3 24 2 7 36 16 12 5 10 19 33 167

Out-degree: the number of times the element serves as the source or origin (means) of linkages with other elements. In-degree: the number of times the element serves as the object or end of linkages with others. Abstractness: (in-degrees)/(in-degrees + out-degrees). Centrality: (in-degree + out-degree)/the sum of all active cells.

Table 5 Statistics for determining a cutoff level. Cutoff level

Number of active cells in the implication matrix

Percentage of active cells at or above the cutoff level (%)

Number of active linkages in the implication matrix

Percentage of active linkages at or above the cutoff level (%)

1 2 3 4 5 6

50 33 19 15 11 8

100.0 66.0 38.0 30.0 22.0 16.0

167 150 122 107 91 76

100.0 89.8 73.1 64.1 54.5 45.5

goals; yet they also have unique goals. Compared to VC members, SVW user goals embrace VC members’ goals and further include

distinctive goals, such as Creation, Financial, and Exploring. On the other hand, SVW users have differential goals, or Creation, Financial, and Knowledge acquisition, from those of GVW players, and do not mention achievement, which is one of GVW players’ goals. Accordingly, a SVW can be regarded as a novel cyberspace that allows users to engage in activities that are different from traditional VCs and GVWs. The specific findings are summarized below: In terms of the topics’ centrality, Social relations (0.413) and Amusement (0.416) are predominant, and Creating (0.257) and Technical features (0.246) follow. Amusement, Escapism, and Knowledge acquisition are the most abstract topics (i.e., high-level purposes), whereas Financial, Technical feature, and Exploring are the least abstract ones. Creating and Social relations play an intermediary role in the hierarchical structure (i.e., intermediary purposes).

Fig. 2. Hierarchical goal map for a social virtual worlds.

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In examining linkages, we have seven findings. First, the main chain is Technical feature ? Social relations ? Amusement. This chain is 23.3% of all relations in the implication matrix. Second, Exploring is a source for Social relations and Amusement. Third, Creating supports the highest abstraction out of three purposes including Amusement, Escapism, and Knowledge acquisition. Fourth, Social relations functions as a means for Escapism. Fifth, Escapism leads Amusement. Sixth, Amusement has a strong reciprocal feedback loop with Social relations. Both can be a means or an end for each other; in other words, the both are deeply interwoven. Finally, Creating has a reciprocal feedback loop with Financial. The results show that Social relations and Amusement are main goals for using SVWs from the viewpoint of abstractness and centrality, indicating that SVW users have goals that are similar to VC members and GVW players. Social relations correspond to VC members’ major goal, and thus we can conclude that a SVW is another channel for expanding social relations, as are other social cyberspaces. Amusement is also mentioned as one of VC members’ main goals. In the context of GVWs, Amusement is not explicitly stated as a goal (Yee, 2006); it can be considered an intrinsic goal in playing GVWs. The most predominant relation is the link of Technical feature ? Social relations ? Amusement. This result indicates that various technical features (e.g., voice chatting, avatar interaction) help users expend social relations, which subsequently lead to amusement. Ultimately, considering the strong reciprocal feedback relation of Social relations and Amusement, users tend to aim at enjoyable social relations supported by technical features in SVWs. Both Social relations and Amusement are also supported by another topic of Exploring, which represents users’ traveling to 3D spatial places where users come together for dancing or social events. Thus, Exploring is directly relevant to social communications. In addition, traveling to fantastic places (e.g., exotic heaven) or reallike places (e.g., virtual New Orleans) may provide visitors with an experience that produce pleasure. Escapism, which implies that users try to get out of their routine or constrained real-life environments (Hirschman, 1983), is one of the most abstract goals and corresponds to one of GVW players’ goals. Escapism has a much higher position in the hierarchical goal map as the end of two intermediary goals (Social relations and Creating). Some users achieve the Escapism goal by making social relations in SVWs. To users with limited social activities (e.g., a disabled person, a housewife caring for four children), social interaction in SVWs can be a way to overcome their constraints in the real world, which eventually leads to positive feelings or amusement. Creating is a goal that distinguishes VC members’ goals from GVW players’ goals. Despite the fact that not all SVWs enable their users to create virtual objects and even sell them, many SVWs do so. This behavioral autonomy is a unique characteristic of SVWs and is regarded as a central goal (3rd highest centrality). In particular, Creating plays an important role of an intermediary goal that supports all three highest purposes in the hierarchical structure. By creating virtual objects, users have fun and improve their practical skills, such as computer programming. Also, by designing their avatars’ appearances, which may be significantly different from their real appearances, or by creating imaginary virtual objects, they escape from constraints in real life. Another point is that Creating has a reciprocal feedback loop with the Financial goal. Besides making virtual objects to satisfy their creative needs and imagination, users produce them partially with the intent to sell them in SVWs. Whether their intention is to gain self-contentment or do business, their creatures become fundamental contents of SVWs. VC research has stressed the impor-

tance of member-generated contents and further regarded them as a vital factor in VCs’ success (Filipczak, 1998). However, according to prior studies, only 10% of members of one popular peer-topeer sharing VC produced 87% of all contents (Adar & Huberman, 2000), and 4% of members in an open-source development VC produced 88% of new codes and 66% of code fixes (Mockus, Fielding, & Andersen, 2002). Minor devotees produce almost all contents in pre-existing VCs, whereas SVW users consider the behavior for producing contents, or Creating, a central goal. Therefore, member-generated contents are more prominent in SVWs than in traditional VCs. Furthermore, transactional systems (e.g., virtual currency, virtual market for trading virtual objects) play some sort of incentive for users’ creating behavior. SVWs’ transactional systems can encourage users to create contents spontaneously without incentive systems supported by SVW providers. Thus, the combination of Creating and Financial has a strong self-sustaining function for SVWs. This finding provides current and future SVW providers with managerial insights: advancing a creating tool, reinforcing transactional systems, and guaranteeing secure transactions. Knowledge acquisition is one of four of VC members’ main goals (i.e., information exchange). Knowledge acquisition is, however, not a central goal to SVW users (the second lowest centrality), though it is one of the most abstract goals. Compared to Social relations and Amusement, Knowledge acquisition seems to be considered a minor goal in SVWs.

6. Contributions and limitations This study’s contributions are threefold: conceptualizing SVWs, empirical investigation of a timely topic, and finding a new approach towards analyzing user goals for social cyberspaces. First, although SVWs have their own distinctive characteristics that are different from traditional MMOGs (or GVWs), many studies pertinent to SVWs have considered them to belong to one broad single category together with GVWs, or VWs. This study clarifies the notion of SVWs through differentiating them from GVWs, and we hope that our initial conceptualization of SVWs stimulates discussion about what SVWs are, and how different these new cyberspaces are from others. Second, while SVWs have dramatically attracted the attention of business mangers, educational practitioners, and researchers, there is little empirical research on SVW users. The study offers preliminary knowledge about SVW users by empirical investigation of user goals. Lastly, the study introduces a means-end chain analysis which provides a richer understanding of SVW user goals. In prior studies that examined individuals’ goals for using social cyberspaces, those goals are separately listed without any explanation of relations among them. A means-end chain analysis used in this study presented the hierarchical goal map, which consists of separate goals and their relations, and thus offers a better explanation of SVW user goals. This study has the following three limitations. First, the study’s samples may be biased in that the study surveyed users during a short term, and chose a convenient sampling method. The other potential bias with our data is that because the samples voluntarily responded the survey our results may be based on highly-motivated users’ responses. Accordingly, the study has a limitation in fully generalizing the findings. Second, the study deals with only one type of SVW which supports 3D interfaces and endows users to create virtual object and sell them. Thus, the findings of the study should be re-examined on other SVWs, which have different environments, in future research. Furthermore, in order for further understanding of SVWs user goals, future research needs to compare them to user goals for other various social cyberspaces, such as conventional text-based VCs, social networking services, or


weblogs. Finally, this study did not control variance caused by culture, which may function as a crucial variable in fully understanding SVW users’ goals, in a sense that There is a worldwide cyberspace and the users come from various countries. Thus, cultural consideration can be a prominent aspect in research on SVW users’ behaviors.

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