Energizing business transactions in virtual worlds: anempirical study of consumers’ purchasing behaviors

Eunyoung Cheon

Published online: 31 August 2013

� Springer Science+Business Media New York 2013

Abstract Virtual marketplaces for products and services

have become major profit sources in virtual worlds (VWs).

The large quantity and growth of virtual product transac-

tions and their platform providers’ profits have made it

critical to understand consumer purchasing behavior in

VWs. However, as open-ended VWs such as Second Life

have environments that differ from those of other online

communities, the underlying mechanisms of consumers’

e-commerce behavior may not explain their VW behavior.

Therefore, this study examines consumers’ VW behavior

by considering three categories of factors influencing their

purchasing behavior: the platform context (i.e., technical

characteristics such as interactivity and vividness and

social characteristics such as involvement), product context

(i.e., product value), and virtual experience (i.e., flow and

satisfaction). This study examines how these factors affect

consumers when they purchase virtual products. Its results

highlight the importance of flow experience in consumers’

VW behavior. Interactivity, vividness, and involvement are

found to affect consumers’ virtual experience—flow, and

involvement exhibits a significantly stronger influence on

flow. Flow and involvement are found to affect product

value, and flow exerts a stronger influence than involve-

ment on product value. Flow and product value directly

impact consumers’ willingness to purchase, whereas sat-

isfaction with the virtual world experience, which is sig-

nificantly affected by flow, is not associated with

willingness to purchase. The results further indicate that

product value is more influential on willingness to purchase

than is flow. After describing the study’s contributions to

both research and practice, I conclude the paper by pre-

senting avenues for future research.

Keywords Virtual worlds � Consumer behaviors �Willingness to purchase virtual products � Platform �Virtual experience � Second Life

1 Introduction

The emergence of the Internet and advances in technology

have narrowed the gap among people around the globe and

created virtual communities (VCs) such as virtual worlds

(VWs). VCs are networks of individuals or business part-

ners that share common interests; they fulfill participants’

needs through digitized communication on a technical

platform [10] through which computer-mediated systems

(CMS) are deployed. As VCs become more prevalent and

sophisticated, VWs equipped with advanced interfaces and

features emerge. Generally, VWs are computer-based

digital worlds that rely on streaming technology and grid

computing to simulate the real world; they can broadly be

categorized into game-oriented VWs, such as World of

Warcraft and EverQuest, and open-ended VWs, such as

Second Life and There. Users of game-oriented VWs, the

most well-known VW type, are focused on playing games

through their avatars, which involves competition, winning

or losing, levels, prizes, and a mission. Open-ended VWs,

our research interest, offer open-ended experiences that

mimic real life, unlike game-oriented VWs. Open-ended

VW users are focused on activities such as socializing with

community members, exploring new activities and places,

sharing information, and attending classes. Countless vir-

tual experience possibilities can be offered in open-ended

VWs.

E. Cheon (&)

College of Business Administration, Seoul National University,

Seoul 151-916, Republic of Korea

e-mail: echeon@snu.ac.kr

123

Inf Technol Manag (2013) 14:315–330

DOI 10.1007/s10799-013-0169-6

The dynamics and features of VWs have engendered

commercial activities. Users create a variety of interesting

digital contents, share these contents with others, or sell

them for a profit [10]. Thus, VWs allow people to not only

form social relationships but also buy and sell virtual

products such as clothes, accessories, automobiles, and real

estate, and virtual services (e.g., consulting and entertain-

ment). As a tremendous number of transactions involving

virtual product markets have occurred in multiplayer online

games, VWs are also creating huge business opportunities

for product and service sales in the community. Many

companies, even huge corporations such as IBM, Intel,

NIKE, BMW, and Gucci, are investing large sums to reach

customers and market their products in VWs such as

Second Life. The revenue from the sale of virtual goods

increased by 245% between 2007 and 2010 (from USD 2.1

billion to USD 7.3 billion) and will more than double by

2014 [23]. Consumer behavior is one of the most important

factors in vitalizing marketplaces, and invigorated market

activity can increase currency flow. Shopping has become a

main VW activity; it can increase currency flow, and thus,

generate profits for platform providers such as Linden Lab

of Second Life. For example, the ‘‘Linden dollar’’ is used

as currency in Second Life. Similar to a bank that deals in

foreign exchange, Linden Lab profits by selling and buying

Linden dollars to users. The more Linden dollars are nee-

ded in Second Life, the more profits Linden Lab can

generate. Virtual products can be traded for real money in

most VWs. Given the large quantity and growth of the

virtual product transactions that are generating profits for

platform providers, understanding the process that leads to

consumers’ VW purchasing behavior is important. There-

fore, it is crucial to understand consumers’ purchasing

behavior and to configure the new VW market environment

to suit this behavior and energize transaction activities.

Both academic researchers and practitioners have long

examined consumer behaviors related to buying products

in offline and online markets, such as through e-commerce

(e.g., [15, 80, 89]). Many organizations are searching for

new ways to be globally competitive, and recent attempts

have taken a more outward customer orientation, with a

focus on how customers see value [89]. Research on why

consumers choose a specific product or choose one product

type or brand over another seek to determine consumers’

consumption values [80]. Researchers regard consumer

choice as a function of multiple consumption value

dimensions such as functional, social, emotional, episte-

mic, and conditional dimensions [80] In addition, the

concept of customer value has a strong relationship to

customer satisfaction [15].

The importance of understanding consumer behaviors in

virtual environments has been recognized by both academic

researchers and practitioners. Extensive literature exists on

consumer behavior in online markets, such as e-commerce

environments. With the growth of online shopping, knowing

how to use the Web effectively to gain more customers is

becoming increasingly important. Researchers have sought

to identify the factors that affect consumers’ attitudes and

behaviors in e-commerce environments. The characteristics

of a website, such as its interactivity and vividness, affect

attitudes to and behaviors concerning it [18, 58].

Researchers have suggested that flow construct is important

for understanding consumer behaviors on the Web [39, 45]

and that individual involvement in certain activities can

influence the flow stage [62]. In addition, the perceived risks

related to products or online transactions that can emerge

during online shopping also influence consumers’ attitudes

and behaviors [4].

Although the impact of VWs on commercial transac-

tions has grown over time, most VW research remains

conceptual. The factors that explain consumer behaviors in

conventional e-commerce media such as Amazon may not

completely explain behaviors in VWs such as Second Life,

as VWs represent different market environments. For

example, virtual products purchased in a virtual community

can be consumed only in that community; they cannot be

consumed in the real world or in another virtual commu-

nity. Furthermore, people in a VW participate in a virtual

community first, and their need to purchase a product is

usually aroused later. Moreover, the risk of fraud in online

transactions is often lower in a VW because exchanges of

virtual currency and virtual products happen almost

simultaneously there. Because VWs have environments

different from those of other online communities, the

underlying mechanisms explaining consumer behavior

have not yet been adequately explained. In other words,

VWs are different from conventional e-commerce sites,

and consumer behavior applicable to them may or may not

be applicable to VWs. Therefore, consumers’ VW pur-

chasing behavior must be investigated.

This study attempts to complement the VW knowledge

base in the Information System (IS) field and assist various

parties involved in VWs to achieve their optimal goals.

This study’s particular aim is to examine the process

leading to consumers’ VW purchasing behavior and to

identify the factors that affect consumers when purchasing

virtual products such as clothes, accessories, automobiles,

real estate, and virtual services (e.g., consulting and

entertainment) in VWs. I focus on VW environments such

as Second Life in order to identify the factors that drive

individuals to purchase virtual products in VWs.

This study explores several perspectives on consumers’

VW purchasing behavior. First, while most research on

purchasing behaviors in VCs has focused on the technical

and social factors in platform contexts, this research con-

siders both the platform and product contexts. The

316 Inf Technol Manag (2013) 14:315–330

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‘‘platform’’ is the virtual place where a product is purchased

and consumed, and the ‘‘platform context’’ of a VW market

refers to the characteristics of a shopping environment such

as a shopping mall in the real world. The ‘‘product context’’

is related to the virtual objects that users in VWs purchase

and the product values flowing from users’ overall assess-

ment of those products based on their perceptions of the

values obtainable through purchase. Second, because VW

customers are exposed to their communities before being

exposed to the products and as the need to buy a product is

aroused later, I considered the factors relating to platforms as

preceding indicators of the factors relating to products. This

study examines how platform-related factors affect con-

sumers’ perceived assessments of product value. Third, most

studies assume that consumers’ perceived product value

directly affects purchase intention, while some studies have

shown that a consumer’s positive online experience may

also serve as an important factor in that consumer’s shop-

ping satisfaction [57] Therefore, this study investigates how

willingness to purchase is affected by product value and

virtual experience. Fourth, numerous empirical tests have

shown that intention significantly determines actual behav-

ior [21, 25, 87]; intention indicates readiness to perform a

given behavior and is considered a direct antecedent of

behavior [2]. This study thus considers an individual’s

willingness to purchase a virtual product rather than actual

purchase behavior.

This research is organized into seven sections (including

this introduction). The next section reviews the literature to

identify relevant constructs for our research model. I apply

the reviewed theories in order to present a model enabling

the examination of how individuals are convinced to pur-

chase virtual products; I then develop the research hypoth-

eses. The research methods are presented in the fourth

section, and the results of this study are described in the fifth

section. Finally, I discuss the study’s results, implications,

and suggestions for future research and conclude the paper.

2 Theoretical background and research model

2.1 Overview

Researchers examining consumer behaviors in computer-

mediated environments have attempted to identify the

factors influencing consumer experiences, attitudes, and

behaviors. Flow has been examined in the computer-

mediated environment field and is considered to influence

the online consumer experience (e.g., [31, 39, 62, 88]).

Csikszentmihalyi defined flow as a ‘‘state in which people

are so involved in an activity that nothing else seems to

matter’’ [19]. Flow experience in a computer-mediated

environment is similar to real-world flow experience [14].

Some researchers have studied flow in Web environments

narrowly, such as by examining VW environments [24,

65]. These researches assert that participating in VW

environments provides users an enjoyable experience.

Flow is affected by the characteristics of the communica-

tion medium [39]. The media characteristics of VWs can

be considered important factors in their users’ flow expe-

rience. Flow has also been considered a critical factor in

the online consumer experience [31, 39, 62, 88] and in

consumers’ attitudes and behavior [39]. Flow seems to be

an important factor in understanding consumers’ purchas-

ing behavior. Flow is the central variable of this study’s

research model. This research focuses on the flow phe-

nomenon within a computer-mediated environment that

can lead to participants’ positive purchasing behavior.

Technologies are primary components of VC platforms,

which is related to the characteristics of the VWs’ com-

munication medium. Researchers in IS have acknowledged

the need to study consumer behaviors in virtual environ-

ments, and most have attempted to identify the impacts of

technical factors on consumer behaviors. Technical factors

related to platforms’ design and implementation are

assumed to be important to VC members. Vividness and

interactivity influence Web environment contents [62, 84].

Some researchers have investigated online consumer atti-

tudes to the Web-based interfaces, products, and adver-

tisements that influence consumer decisions to purchase a

product, join a community, or revisit a website [28, 44].

They suggest that technical characteristics such as inter-

activity and vividness are key to the novel media that

influence consumers’ opinions and attitudes. Interactivity is

the degree to which participants feel that they have control

over the communication medium and that the communi-

cation medium allows them to communicate both recip-

rocally and synchronously [58]. Vividness is the

representational richness of a mediated environment [84];

vividness is the intensity to which information in an

environment is represented to users. Both interactivity [38,

62] and vividness [43] affect virtual experiences such as

flow.

Furthermore, I also contend that social factors signifi-

cantly affect consumer experience in the virtual environ-

ment. The construct of involvement, which is related to

intrinsic motivation, is considered an important commu-

nication variable [62]. While interactivity and vividness

relate to technical characteristics, involvement encom-

passes social characteristics in human–computer interac-

tions. Involvement is an individual’s internal or

motivational state toward an object, where that internal

state is activated by the relevance or importance of the

object in question [91]. I classify interactivity, vividness,

and involvement as elements comprising the platform

context that characterizes a place; they relate to the

Inf Technol Manag (2013) 14:315–330 317

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interactions among consumers. Meanwhile, platform is

characterized by technological factors (i.e., vividness and

interactivity) and social factors (i.e., involvement).

No matter how much the world around us and we evolve

or how far technology advances, humans subscribe to a

number of notions. One is that a value obtained through a

choice often becomes the foundation for subsequent action.

The notion that ‘‘customers are value-driven’’ is put forward

by many commentators and is widely discussed in the

practitioner literature. The importance of customer percep-

tion of value is continuously emphasized to producers and

retailers [89]. Perceived value is seen as the consumer’s

overall assessment of the utility of a product [92].

Researchers regard consumer choice as a function of mul-

tiple consumption value dimensions such as functional,

emotional, and social dimensions [80, 85]. Researchers of

VC purchasing behavior have focused on technical and

social factors. To the best of my knowledge, however, very

few studies have investigated the consumption values that

explain why consumers decide to purchase a specific prod-

uct over another product in virtual communities. In line with

Sweeney and Soutar [85], this study conceptualizes value as

a multidimensional construct encompassing both functional

and emotional values. Functional value involves the cost,

performance, or quality of the product. Emotional value

relates to the feelings or affective states associated with a

product. I classify functional and emotional product values

as comprising the product context. A product is then influ-

enced by its emotional and functional values.

Satisfaction is a widely studied construct used to

investigate the factors influencing consumers as they per-

form certain tasks. Concerning consumer behavior in

computer-mediated environments, ‘‘satisfaction’’ can be

defined as the degree to which consumers’ perceptions of

their online experience confirm their expectations.

Researchers often describe flow and satisfaction as medi-

ators of other variables (such as usage and purchasing) of

consumer behavior [39, 45, 62]. Therefore, I take these two

constructs as the variables affecting willingness to pur-

chase, which in turn affects purchasing behavior in virtual

communities. Furthermore, as flow and satisfaction repre-

sent the consumer’s experience in a virtual world, I classify

them as virtual experience.

This study’s research model (see Fig. 1) delineates the

positive relationships among the factors of a platform (i.e.,

interactivity, vividness, and involvement), product (i.e.,

product values), virtual experience (i.e., flow and satis-

faction), and consumer behavior (i.e., willingness to pur-

chase). The model shows the process by which factors

drive consumers’ willingness to purchase virtual products

by building flow, satisfaction, and product values. In this

model, interactivity, vividness, and involvement are major

factors influencing virtual experience and behavior. I

measured the intention to purchase virtual products

because actual purchasing behavior is mediated by the

behavioral intention to purchase. Many researchers have

shown that specific behavior is determined by a behavioral

intention to perform that behavior [21, 25, 87].

2.2 Platform context

2.2.1 Interactivity

The term ‘‘interactivity’’ generally means ‘‘reciprocal

activity,’’ ‘‘mutual influence,’’ or ‘‘interplay.’’ Similar to

‘‘information,’’ the term ‘‘interactivity’’ is widely used in

various disciplines and it means many things to many peo-

ple. Nevertheless, the term can be defined according to its

context and has an extensive body of literature devoted to it.

For example, in the computer-related context, interactivity

describes the sequences occurring between a human and a

computer program. In the field of communication, interac-

tivity involves the dialog exchanged between humans. In

sociology, interactivity refers to the relationships between

people influencing and being influenced by each other.

Within the communication and media contexts of this

study, ‘‘interactivity’’ can be understood using three con-

ceptualizations. One involves human communication, and

much of the relevant literature grows out of the sociolog-

ical perspective. For example, the definition of DeFleur

et al. [22] addresses real-time, interpersonal exchanges

between individuals. Ghose and Dou [32] suggested 23

functions of interactivity based on a communication-

focused conceptualization. Scholars working from the

sociological perspective have suggested that interactivity is

the relationship between two or more people who mutually

adapt their behavior and actions to each other [42].

Another conceptualization of interactivity involves

humans’ interactions with computers. This literature,

growing out of the computer science perspective, considers

interactivity as a capability of the medium. For example,

Rogers [73] viewed interactivity as the capability of new

communication systems such as computers to offer role

interchangeability between a sender and a receiver, almost

H1

H2

H3

H4

H6

H7

H8 H9

H5Involvement

Interactivity

Willingness to Purchase

Vividness

Satisfaction

Product Value

Flow

Fig. 1 Research model

318 Inf Technol Manag (2013) 14:315–330

123

as in a conversation. Similarly, Rafaeli [69] examined

interactivity in computer-mediated environments. Rafaeli

and Sudweeks [70] defined interactivity as the ‘‘extent to

which messages in a sequence relate to each other and

especially the extent to which later messages recount the

relatedness of earlier messages.’’ Steuer [84] defined

interactivity as the extent to which users can modify the

form and content of a mediated environment in real time.

The literature on interactivity used by this research

includes multiple-dimensional concepts based on human

perceptions in the computer-mediated communication

context. Many scholars have identified the key dimensions

of interactivity that help to explain how individuals per-

ceive interactivity during computer-mediated communica-

tion (e.g., [35, 38, 49, 58]). For example, Ha and James

[35] defined interactivity as having five dimensions: play-

fulness, choice, connectedness, information collection, and

reciprocal communication. Liu and Shrum [49] proposed

three dimensions of interactivity: active control, two-way

communication, and synchronicity. McMillan and Dowens

[58] identified two dominant dimensions: direction of

communication and control of communication experience.

The present study indicates that the level of interactivity

differs depending on the capability of the communication

media and the individual’s perception of interactivity. On

the basis of multiple-dimensional concepts in the com-

puter-mediated communication context, I define ‘‘interac-

tivity’’ as the degree to which two or more communication

parties feel that they have control over a virtual environ-

ment and can communicate mutually.

This proposed definition of interactivity implies that

different communication media display different levels of

interactivity depending on how the individuals involved

perceive that interactivity. If we apply the multi-dimen-

sional concepts of interactivity based on individuals’ per-

ceptions, a website regarded as interactive allows users to

have more control over their communication experience.

Because interactivity is considered to contribute to users’

perception of being physically present in a natural envi-

ronment [35], high levels of interactivity may influence

consumers to feel a strong sense of telepresence, the

experience of being in a remote environment by means of a

communication medium [84]. When consumers interact

more playfully and enjoyably with a website, they are more

absorbed and interested in the interaction on the website

[59]. Consumers exert more effort when they participate in

more interactive communication media [38], and the

characteristics of the communication medium affect flow

[39]. High levels of interactivity create flow experience

[39, 62]. Therefore, I posit the following:

H1 Perceived interactivity is positively associated with

flow.

2.2.2 Vividness

A number of behavioral science studies have explored the

vividness effects in human judgments affecting decision

making. Vividly presented information is more likely than

information offered in a pallid format to engage people in

cognitive elaboration [61]. Thus, vividness is said to be a

stimulus-driven variable because a high level of vividness

gives users more stimulation. Vivid information is likely to

produce arousal attraction and grab attention by being

emotionally interesting, concrete, and imagery provoking,

while being proximate in a sensory, temporal, or spatial

way [61]. Thus, vividly presented information exposes

consumers to more information cues and stimulates more

senses than a pallid presentation.

Vividness is generally understood as the representa-

tional quality of information delivery to consumers. Viv-

idness is the ‘‘representational richness of a mediated

environment as defined by its formal features; that is, the

way in which an environment presents information to the

senses’’ [84]. The breadth and depth of the information

being delivered are two of the important variables in viv-

idness [84]. The breadth of information refers to the

number of sensory dimensions, cues, and senses being

presented, and depth is the resolution and quality of the

presentation. The wider the range of sensations and the

higher the level of image quality a medium delivers, the

greater the degree of vividness.

Vividness is often applied to technical medium charac-

teristics describing the medium’s capability to convey

information. As in media richness theory [20], vividness

involves the ability of a communication medium to

reproduce the information received. Rich media tools

offering a greater number of channels while reproducing

information and enhancing the richness of the communi-

cation experience may be considered to increase vividness.

Many factors can affect the degree of vividness, and

information replicated with rich media tools such as video

or animation can be said to be more vivid than information

presented in print or on the radio. Applying the mechanical

perspective of vividness to a virtual environment suggests

that a virtual environment described as ‘‘vivid’’ exposes

consumers to more information cues and stimulates more

senses than a pallid environment. In this study, I define

‘‘vividness’’ as the representational quality or richness

featured while demonstrating a mediated environment.

Similar to interactivity, vividness is considered a

determinant in the mediated perception of environment

[84]. A virtual environment considered to be vivid offers

rich content that appeals to multiple senses and induces a

strong sense of telepresence in consumers [18]. Participants

in mediated environments offering a high level of vividness

may feel that they are in a real environment and may

Inf Technol Manag (2013) 14:315–330 319

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therefore exert more effort and enjoy their time in the

environment more thoroughly. As a result, high levels of

vividness inspire an optimal state of communication

experience in consumers, leading to a sense of flow. It has

also been demonstrated that the characteristics of a com-

munication medium affect flow [39]. Thus, vividness

positively affects the sense of flow in a Web context [43]. I

therefore hypothesize the following:

H2 Perceived vividness is positively associated with

flow.

2.2.3 Involvement

In this study, involvement is distinct from interactivity.

While interactivity, similarly as vividness, is a variable

related to human–computer interaction, involvement is of

concern mainly to researchers in communication. People

can be involved with many things such as activities,

objects, or social issues. Thus, involvement has long been

an important concept for researchers and is used widely in

different disciplines. In psychology, involvement is the

state of an organism when it is presented with any stimulus

related to the ego [78] or an individual’s psychological

state when activated by personal relevance, significance, or

importance [79].

Consumer behavior researchers have examined con-

sumers’ involvements with phenomena such as adver-

tisements, products, and purchasing decisions. Their use

of ‘‘involvement’’ is consistent with the definition found

in psychology. For example, researchers describe

involvement as an internal state [7] or a motivational state

[71] induced by personal relevance or importance [33, 91]

or by an object being used to accomplish a specific goal

[67], or as an indicator of the amount of arousal, interest,

or drive evoked by a stimulus or situation [7]. Organi-

zational behavior research considers involvement as the

degree to which individuals associate psychologically

with their jobs and work [47, 50]. Involvement is gener-

ally used to describe one’s psychological state regarding

the importance and personal relevance of one’s job or

work.

Throughout the many variations on the definition of

‘‘involvement,’’ the term is generally used to describe an

individual’s psychological state regarding personal impor-

tance and relevance of, for example, an issue, object, job,

or work. Involvement is an individual’s internal or moti-

vational state regarding an object as activated by the

object’s relevance or importance [91]. In this study, I adopt

the view of involvement focusing on consumers’ internal

states regarding an object reflecting personal relevance or

importance: involvement reflects the personal relevance or

importance of events and activities in VWs.

High involvement indicates high personal relevance or

importance [33]. When people are deeply involved in some

event, object, or activity, they are completely immersed in

it, and thus, experience a sense of flow [51], the complete

involvement in an activity [54]. An involvement derived

from the Web’s importance to customers strongly affects

the primary antecedents of flow [62]. Consumers’

involvement in a VW focuses their attention, leading to a

sense of flow. In addition, when consumers are completely

involved with events and activities in the VC, they are

more likely to feel the need for products enabling them to

continue their existence in the community. I expect that

customers with higher involvement with events and activ-

ities in a virtual community have a more positive product

value owing to their increased interest in the products in the

virtual community. Therefore, I hypothesize the following:

H3 Involvement is positively associated with flow.

H4 Involvement is positively associated with product

value.

2.3 Product context

Consumers tend to be value driven [48]. Perceived con-

sumer value has been identified as one of the most

important marketing concepts for purchase intention [42].

Delivering value has become a key to success for producers

and retailers. Thus, managers need to understand what their

customers value in products, focus on that value, and be

good at delivering it to their customers [89]. Value can be

seen as a tradeoff between perceived benefits and perceived

sacrifice. Many researchers who have examined consumer-

perceived value suggest that delivering this value is a key

to success. For example, Zeithaml [92] defines perceived

value as the ‘‘consumer’s overall assessment of the utility

of a product based on perceptions of what is received and

what is given.’’ Product value is created when the benefits

of a product are greater than its overall costs [81].

From these definitions, we can see that the utility theory

is grounded in the value concept: a consumer has a motive

to buy a product or service when he or she is satisfied with

the tradeoff between its perceived utility (benefits) and its

corresponding sacrifices. This ‘‘get’’ and ‘‘give’’ are often

described as the benefits and sacrifices that might be pro-

vided by a product or service. Most researchers describe

perceived value as the ratio or tradeoff between all relevant

get and give components [92]. Researchers have taken

quality and price to be major components in the tradeoff

between benefit and sacrifice [17] or the relationship

between desired value and satisfaction [89].

However, some researchers have adopted more sophis-

ticated measures to understand how consumers value

products and services. Sheth et al. [80] identified five

320 Inf Technol Manag (2013) 14:315–330

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consumer values influencing consumer choice behavior:

functional, social, emotional, epistemic, and conditional

values. Barbin et al. [5] considered two types of shopping

value: utilitarian and hedonic. Holbrook [40] provided two

classifications of customer value: either intrinsic or

extrinsic to the product and either self-oriented or other-

oriented. Burns [8] distinguished between four categories

of value: product value, value in use, possession value, and

overall value in a consumer’s evaluation process.

As viewing consumer value as having multiple dimen-

sions tends to allow a satisfactory explanation of consumer

outcomes such as purchasing intention [85], this study

assumes that the focus of consumer value is the consumer’s

perception of the value derived from a product or service,

while acknowledging the complexity of the intrinsic value

scale in the consumer’s mind. A perception of value can

occur before a product or service is purchased [89]; thus, a

consumer’s perception of value can be generated without

the experience of using or purchasing the product or ser-

vice. Therefore, I define product value as consumers’

overall assessment of a product based on their perceptions

of what values the product provides. In this study, I adopt

the components of Sheth et al.’s [80] value scale and focus

on functional and emotional value. Functional value refers

to the perceived utility derived from the product owing to

the cost reduction or perceived and expected performance

of the product. Emotional value is the enjoyment or plea-

sure derived from the product.

Studies suggest that consumer values influence con-

sumers’ purchase behavior. For example, consumer choice

behavior is a function of multiple consumption values, and

consumer values influence consumer choice behavior [80].

Perceived consumer value is considered an important

concept for consumer purchasing intention [42]. Con-

sumption values can drive consumers’ purchase intentions

and behaviors. Therefore, I posit the following:

H5 Perceived product value is positively associated with

willingness to purchase.

2.4 Virtual experience and behavior

Many researchers in disciplines, such as psychology, con-

sumer behavior, communications, and information tech-

nologies, have extensively discussed flow theory. Flow is

applied to computer-mediated environments to describe

human–computer interactions and consumption experience

(e.g., [40]). In the context of such environments, flow is

considered an important construct for the online consumer

experience; flow is understood to drive exploratory

behavior [39, 88], positive experience [9, 39, 86], shopping

behavior [82], satisfaction [39], learning [30, 39], and

technology acceptance and use [31, 88].

Flow is defined as a multidimensional construct repre-

senting users’ perception of an interaction as enjoyable and

playful. Studies of flow during human-computer interaction

have measured flow as a two-dimensional construct com-

prising enjoyment and concentration [30] or as a four-

dimensional construct comprising control, attention focus,

curiosity, and intrinsic interest [86]. Flow is the process of

optimal experience and holistic sensation people feel when

acting with total attachment [19]. These individuals have clear

goals, concentrate fully on their tasks, see little distinction

between self and environment, and experience a distortion of

time [19]. Hoffmann and Novack [39] examined flow in Web

environments and defined it as the ‘‘state occurring during

network navigation, which is (1) characterized by a seamless

sequence of responses facilitated by machine interactivity, (2)

intrinsically enjoyable, (3) accompanied by a loss of self-

consciousness, and (4) self-reinforcing.’’

When people are in a flow state, they are so immersed in

an activity that nothing else seems to matter. The experi-

ence itself is so pleasurable and so conducive to repetition

that attachment in a playful and exploratory experience is

self-motivating [60]. Thus, people who interact more

playfully with a virtual world are more motivated to engage

in the interaction. In this study, flow refers to the state of

mental arousal that occurs when an individual participates

in an activity with total attachment.

Flow has been widely used to describe the interaction

between humans and computer-mediated technologies [19,

86]. Since VWs involve a computer-mediated environment

and context, flow theory is relevant and adaptable to this

study. When consumers are in a flow state, they are so entirely

focused on their activities and on interacting with those joy-

fully that irrelevant thoughts and perceptions are screened out

and nothing else matters to them. Consumers who highly

enjoy engaging on a website have a more positive sensation

about the activity than those who enjoy it less and be more

motivated to engage in further activities in the future [88].

In addition, when consumers are in a flow state, product

information through advertisements or placement marketing

tends to be highly effective and easily accepted by con-

sumers. Hence, their perception of a product is affected

easily, and these consumers are likely to have higher product

value. Furthermore, consumers who experience more

enjoyment when using a website perceive their interaction

more positively and be more motivated to revisit the site [88].

When consumers experience the flow state, they have

more positive experiences and a higher level of satisfaction

and loyalty [39]. A VW in which consumers experience

considerable enjoyment and easily enter into the flow state

encourages consumers to return often. Thus, consumers

completely engaged in the interaction are more likely to

purchase products enabling them to continue their experi-

ence on the site. Therefore, I hypothesize the following:

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H6 Flow is positively associated with product value.

H7 Flow is positively associated with satisfaction.

H8 Flow is positively associated with willingness to

purchase.

Another virtual experience factor, consumer satisfaction,

is the perception of the pleasurable fulfillment of a service

[76]. Most researchers describe satisfaction as a positive

feeling. Satisfaction is often viewed as consumers’ percep-

tion that the online experience conforms to their expectations.

Consumer expectation is a subjective comparison between

expected and perceived attribute levels [16]; expectations can

be about the price, product, service, vendor, or quality of a

website. Researchers generally agree that consumer satis-

faction results from expectations: when consumers’ expec-

tations are met, they have a high degree of satisfaction.

Satisfaction is one of the most important concepts in

online shopping, and willingness to purchase is considered

to be strongly related to consumer satisfaction [1]. Cus-

tomers’ satisfaction influences their attitudes about and

intentions toward online shopping. For example, when

customers are satisfied with an online service, they have a

positive attitudinal disposition that creates loyalty to the

service provider [76] and behavioral intentions such as

willingness to continuously purchasing from the same pro-

vider [90]. Satisfaction can be considered a reaction to the

attributes and processes taking place during service delivery

[83]. Therefore, satisfaction can be defined as a consumer’s

overall evaluation of an experience with a VW. In this study,

‘‘satisfaction’’ refers to consumers’ overall evaluation of

their online experience, and ‘‘willingness to purchase’’

refers to their intention to make purchases in a virtual world.

In online shopping, satisfaction is strongly related to

willingness to purchase [1]. Similarly, satisfaction with a

website increases the likelihood of purchasing intention

[71]: a high degree of satisfaction positively influences

willingness to purchase. It is therefore likely that con-

sumers’ satisfaction with their experience of a virtual world

impacts their willingness to purchase, leading to purchas-

ing activities. Therefore, I hypothesize the following:

H9 Satisfaction is positively associated with willingness

to purchase.

3 Research methodology

3.1 Data collection

I used the survey method for data collection to test the

proposed research model. The targeted VC for this study is

Second Life, one of the most popular VWs featuring a 3D

virtual environment. Its number of signups is growing

continuously and currently totals over 31 million [77]. In

Second Life, users are represented by avatars. Each user

can change the face and hairstyle of his or her avatar and

personalize it using clothes and accessories. The avatar can

ride in a car, live in a house, and interact with others

synchronously in an environment that simulates the real

world. Users can create various contents such as clothes,

buildings, vehicles, and furniture and then use these con-

tents or sell them to others. As of May 2010, Second Life

had more than 16 million users from more than 150

countries, 2 billon square meters of virtual land occupied

by residents, 2 billion virtual products created by the users,

and 1 million user-to-user transactions per day [74].

I performed a Web-based survey in various places on

Second Life. The users were invited to participate in the

survey and were rewarded with Linden Dollars (Second

Life money) upon completion of the survey. Our avatars

introduced the survey, and users answered the questions by

clicking boxes. To ensure only one response per user, I

checked user IDs and IP addresses and allowed each user to

participate in the survey only once. To minimize missing

data, I set up a control by which users had to complete each

question before moving on to the next.

I received 397 surveys. Of these, 54 responses were

incomplete or answered in inadequate detail. Therefore, I

used only 343 responses. To attain the desired research

power, researchers must satisfy a required sample size.

Cohen’s recommended values, a = .05 and power = .80,

have been widely accepted among researchers [56, 72]. A

power level of .80 or higher is typically treated as high.

According to Cohen [12, 13], to have a power value of .80

with a medium effect size (.15), the required sample size is

76 under a = .05 and 108 under a = .01. This study’s

sample size (343) exceeds the required sample size even at

a = .01. Furthermore, according to Gefen et al. [29], the

minimum required sample size in PLS is ten times the

number of items in the most complex construct. Therefore,

following Cohen [12, 13] and Gefen et al. [29], the sample

size of 343 is statically valid. With its 343 responses, my

sample size fulfills the required sample size and this

number of data points is sufficient for a meaningful con-

clusion. Table 1 presents the respondents’ profiles.

3.2 Instrument construction

Seven constructs were measured in this study: interactivity,

vividness, involvement, flow, product value, satisfaction,

and willingness to purchase. I developed the items in the

questionnaire by either adapting measures that were pre-

viously validated by other researchers (appropriately

reworded to fit the context of this study) or converting the

definitions of constructs into question format. The items

were designed on a seven-point Likert scale (1 = ‘‘strongly

322 Inf Technol Manag (2013) 14:315–330

123

disagree’’ to 7 = ‘‘strongly agree’’). All constructs were

measured using multiple items for adequate reliability [63].

The willingness to purchase scale was adapted from the

items used by Heijden and Verhagen [37]; the scale in this

study consists of four items. Intention to return to the

website was not considered, because VWs are used for

activities other than online shopping, while the first con-

sideration when visiting online shopping sites is purchasing

a product. Therefore, revisiting a virtual world may not

include an intention to purchase a product. The satisfaction

scale was adapted from Oliver [64] and Bhattacherjee [3]

and was reworded to fit the VW context.

The flow scale was adapted from Novak et al. [62] and

Koufaris [45]. On the basis of a scale used by Novak et al.

[62] to measure intrinsically enjoyable experiences on the

Web, these scale items assess the degree of intrinsic

enjoyment gained through activities in VW [45]. The

product value scale was adapted from Sheth et al. [80]; its

items include three dimensions—performance, price, and

emotional value—that present product values.

The interactivity scale was adapted from the items used

by Steuer [84] and McMillan [58]. These scale items assess

the degree of users’ ability to modify the form and content of

the virtual environment and two-way communication while

interacting with the VW. The scale measuring vividness was

adapted from Marks [55] and Steuer [84]. On the basis of the

two sensory breadth and depth dimensions defined by Steuer

[84], I measured the vividness of the visual and auditory

systems in the VW. The scale items were taken from the

scale developed by Marks [55] and were appropriately

reworded to fit the VW context. Involvement scale items

were taken from Zaichkowsky [91].

The initial version of the operationalized constructs was

pretested for content validity by five experts specializing in

the IS or business field. These experts examined the

questionnaire and offered comments on wording, format,

and item length according to which ambiguous items were

revised and refined.

4 Results

The research model was tested through partial least square

(PLS), often referred to as ‘‘component-based’’ or ‘‘vari-

ance-based’’ structural equation modeling (SEM). Using

SEM allows the testing of complex relationship patterns as

a whole because it can explore several indicator variables

per construct simultaneously rather than only one at a time.

Moreover, SEM can take measurement error variables for

observed variables [11], which leads to more reliable

conclusions about the relationships between constructs.

The PLS has less effect on small sample sizes than does

covariance-based SEM models such as LISREL and

AMOS [29], and data are not assumed to be multivariate

normal in PLS, making it powerful in terms of multivariate

normality deviation [26, 29]. Therefore, I adopted the PLS

method as the main data analysis method for this study. I

also used a two-step approach [75], assessing the mea-

surement model to validate the quality of the measures and

evaluating the structural model to test the hypotheses.

4.1 Measurement model results

Validity and reliability assessments were conducted to val-

idate the measurement model. For reliability, composite

reliability scores must be above .7 [63]. As shown in

Table 2, the study’s composite reliability scores are above

.7, demonstrating high construct reliability. Individual item

loadings must be above .6, or ideally .7, to satisfy internal

Table 1 Descriptive statistics of survey respondents (N = 343)

Gender

Male 158 (46.1 %)

Female 185 (53.9 %)

Age

\17 0 (0 %)

18–25 119 (34.7 %)

26–35 92 (26.4 %)

36–45 75 (21.9 %)

46–59 59 (17.2 %)

[60 0 (0 %)

Occupation

Student 50 (14.6 %)

Working 215 (62.7 %)

Unemployed 32 (9.3 %)

Retired 14 (4.1 %)

Others 32 (93 %)

Logging time (h/week)

\1 3 (0.9 %)

1–5 63 (18.4 %)

5–10 75 (21.9 %)

10–20 120 (35.0 %)

[20 82 (23.9 %)

Education

High school 71 (20.7 %)

Undergraduate 14 (42.9 %)

Masters 67 (19.5 %)

Ph.D. 16 (4.7 %)

Others 42 (12.2 %)

Residence

America 195 (56.9 %)

Europe 98 (28.6 %)

Asia 22 (6.4 %)

Others 289 (8.2 %)

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consistency criteria [11]. Table 3 shows that the study’s

measurement item loadings are above .7, demonstrating

adequate reliability. The average variance extracted (AVE)

is examined to assess the convergent validity of the con-

structs. The AVE is an index showing the degree of variance

that a latent variable factor captures from its indicators in

relation to the amount of variance owing to measurement

error. The AVE should be greater than .50 [27, 36]. Table 2

shows that the AVE values are above .50, strongly sup-

porting the presence of convergent validity.

Discriminant validity was assessed by testing whether

the square roots of the AVE for each construct were greater

than any other corresponding row or column entries [6, 27]:

all the diagonal elements should be greater than the cor-

responding off-diagonal ones. The data shown in Table 4

satisfy this requirement.

Another way to assess discriminant validity is examin-

ing the factor loadings of each factor. Each factor should

have higher loadings on the construct of interest than on

any other factor [11]. As seen in Table 5, each factor

loading is higher on its assigned construct than it is on

other constructs. The results of the factor loadings and

cross-loadings indicate that the measurement model dis-

plays sufficient discriminant and convergent validity.

I used self-report measures as the type of data gathered.

As with all self-reported data, there is a possible threat of

common method bias [68]. Therefore, I tested the data for

the common method bias using Harman’s single-factor test.

The factor analysis revealed six factors with eigenvalues

greater than 1.00, with the first factor explaining 37.576 %.

These results indicate that our data do not suffer from the

common method bias.

4.2 Structural model results

A PLS analysis was performed on the structural model to

test the study’s hypotheses. Visual PLS and PLS Graph 3.0

were used in this study and showed no significant differ-

ence. Figure 2 presents the path coefficients, t values, and

explanatory power value (R2) based on the PLS analysis.

Overall, all coefficients on the path are positive, and all

relationships proposed by the theoretical model are sig-

nificant, except for the path from satisfaction to willingness

to purchase. Both flow (path coefficient: .300, p\ .01) and

product value (path coefficient: .516, p \ .001) are sig-

nificantly associated with willingness to purchase. The

relatively larger path coefficient for product value implies

that product value has a larger influence on willingness to

purchase. However, contrary to my prediction, the rela-

tionship between satisfaction and willingness to purchase is

not supported. Involvement (coefficient: .274, p\ .01) and

flow (coefficient: .320, p\ .001) have a significant impact

on product values. Flow is significantly affected by inter-

activity (coefficient: .222, p \ .05), vividness (coefficient:

.195, p \ .05), and involvement (coefficient: .429, p \

.001). In terms of their relative power, involvement exerts a

stronger influence on flow than do the others. Flow sig-

nificantly affects satisfaction (coefficient: .615 p \ .001).

Overall, the model explains approximately 54% of the

variance in willingness to purchase. The PLS analyses

confirm that interactivity, vividness, and involvement are

the driving forces of consumers’ willingness to purchase.

Furthermore, both flow and product value enhance con-

sumers’ willingness to purchase. Flow experience mediates

the impact of technical and social VW characteristics such

Table 2 Results of measurement model

Interactivity Vividness Involvement Flow Product value Satisfaction Willingness

to purchase

Composite reliability 0.857111 0.909619 0.915159 0.892108 0.930827 0.913782 0.828069

AVE 0.600124 0.668338 0.729716 0.579619 0.627428 0.679622 0.708697

Table 3 Measurement model statistics

Items Loading Items Loading Items Loading Items Loading

INT1 0.787100 INV1 0.871200 FLO6 0.795700 SAT1 0.792400

INT2 0.782900 INV2 0.868600 PV1 0.781600 SAT2 0.810800

INT3 0.790200 INV3 0.871700 PV2 0.802500 SAT3 0.831500

INT4 0.737300 INV4 0.803400 PV3 0.821400 SAT4 0.824700

VIV1 0.798400 FLO1 0.750700 PV4 0.767000 SAT5 0.861000

VIV2 0.805900 FLO2 0.761000 PV5 0.846200 WIL1 0.922400

VIV3 0.871400 FLO3 0.757800 PV6 0.775900 WIL2 0.752700

VIV4 0.805600 FLO4 0.739500 PV7 0.770100

VIV5 0.804000 FLO5 0.762100 PV8 0.768300

324 Inf Technol Manag (2013) 14:315–330

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Table 4 Discriminant validity

Interactivity Vividness Involvement Flow Product value Satisfaction Willingness to purchase

Interactivity 0.775

Vividness 0.625 0.818

Involvement 0.418 0.504 0.854

Flow 0.523 0.550 0.620 0.761

Product value 0.506 0.467 0.473 0.490 0.792

Satisfaction 0.390 0.492 0.554 0.615 0.357 0.824

Willingness to purchase 0.421 0.417 0.412 0.577 0.677 0.408 0.842

The diagonal elements in bold are the square roots of the AVE

Table 5 Factor structure matrix of loadings and cross-loadings

Items Interactivity Vividness Involvement Flow Product value Satisfaction Willingness to purchase

INT1 0.7871 0.4869 0.3533 0.4397 0.3731 0.3334 0.3040

INT2 0.7829 0.4655 0.2857 0.3385 0.4048 0.2807 0.2957

INT3 0.7902 0.4947 0.3590 0.4467 0.4105 0.3277 0.3567

INT4 0.7373 0.4873 0.2844 0.3777 0.3825 0.2547 0.3448

VIV1 0.5141 0.7984 0.3734 0.4097 0.4264 0.4294 0.3951

VIV2 0.4901 0.8059 0.4105 0.4219 0.3721 0.3963 0.2988

VIV3 0.5535 0.8714 0.4323 0.4866 0.3719 0.4253 0.3502

VIV4 0.5147 0.8055 0.4417 0.4538 0.3814 0.4171 0.3299

VIV5 0.4818 0.8040 0.3996 0.4692 0.3643 0.3480 0.3358

INV1 0.3888 0.4483 0.8712 0.5267 0.4310 0.4442 0.3523

INV2 0.3620 0.4347 0.8686 0.5772 0.4258 0.5273 0.4078

INV3 0.3656 0.4520 0.8717 0.5338 0.3958 0.5050 0.3358

INV4 0.3088 0.3842 0.8034 0.4755 0.3575 0.4089 0.3039

FLO1 0.4184 0.4373 0.4696 0.7507 0.4580 0.5361 0.4649

FLO2 0.4210 0.4289 0.4717 0.7610 0.3789 0.4673 0.4629

FLO3 0.3767 0.4621 0.5031 0.7578 0.3026 0.4709 0.4126

FLO4 0.3474 0.4051 0.4485 0.7395 0.3127 0.4477 0.4061

FLO5 0.3754 0.4025 0.4362 0.7621 0.3035 0.4223 0.4111

FLO6 0.4402 0.3765 0.4999 0.7957 0.4565 0.4548 0.4704

PV1 0.3650 0.3401 0.4161 0.4263 0.7816 0.2960 0.5525

PV2 0.4135 0.3514 0.3288 0.4109 0.8025 0.2942 0.5145

PV3 0.4508 0.3947 0.3870 0.4277 0.8214 0.2704 0.5506

PV4 0.3450 0.3737 0.3463 0.3796 0.7670 0.2301 0.5254

PV5 0.4864 0.4174 0.4058 0.4580 0.8462 0.3326 0.5667

PV6 0.4208 0.4388 0.3793 0.3552 0.7759 0.2894 0.5080

PV7 0.3535 0.3268 0.3423 0.3082 0.7701 0.2556 0.5392

PV8 0.3630 0.3143 0.3826 0.3226 0.7683 0.2859 0.5302

SAT1 0.3499 0.3954 0.4361 0.5210 0.3269 0.7924 0.3439

SAT2 0.2589 0.3866 0.4101 0.4558 0.2451 0.8108 0.3155

SAT3 0.3337 0.4172 0.4578 0.4683 0.2854 0.8315 0.3088

SAT4 0.3350 0.3958 0.4902 0.5173 0.2987 0.8247 0.3377

SAT5 0.3236 0.4316 0.4836 0.5606 0.3069 0.8610 0.3702

WIL1 0.3359 0.3167 0.2944 0.4398 0.6837 0.3109 0.9224

WIL2 0.4067 0.4307 0.4557 0.5931 0.4088 0.4194 0.7527

Bold numbers are the results of each factor loading on its assigned construct

Inf Technol Manag (2013) 14:315–330 325

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as interactivity, vividness, and involvement on willingness

to purchase. In fact, the results suggest that flow plays an

important role in affecting consumers’ VW behavior.

5 Discussion

5.1 Implications for research

This study contributes to IS research by introducing

appropriate concepts such as ‘‘involvement’’ and ‘‘product

value’’ in order to further our understanding of users’ vir-

tual experiences and behavior with a new technology, VW.

Drawing on new theories from other streams of research

and applying them to the IS field is a promising way to

understand complex phenomena that cannot be explained

solely through the IS knowledge pool. This study examines

the impact of the technical characteristics (i.e., interactivity

and vividness), social characteristics (i.e., involvement),

and economic characteristics (i.e., product value) of VWs

on consumers’ willingness to purchase virtual products.

Combining technical and social characteristics helps to

explain the virtual experience better. In fact, many

researchers advocate the need to investigate not only the

technical factors but also the social factors in VWs [41,

46]. Furthermore, economic characteristics such as the

principles of product value help us to understand con-

sumers’ willingness to purchase virtual products in

emerging VW markets. This study directs IS researchers’

attention to a broader view while integrating many con-

cepts from several fields to prove how multiple character-

istics affect consumers’ VW perceptions, experience, and

behavior.

This study contributes to the emerging literature on VW

consumer behavior, which remains at an early stage;

few studies have considered consumer behavior in VW

settings. In addition, most researchers have explored VW

behavior only conceptually. This study conceptualizes,

operationalizes, and validates the factors that affect con-

sumers when they purchase virtual products in VWs. The

results show that interactivity, vividness, and involvement

are the major factors influencing virtual experience and

behavior and that involvement and flow enhance product

value, which in turn impacts virtual behavior. Furthermore,

flow increases satisfaction within theVW, although this

satisfaction is not associated with a willingness to pur-

chase. Our empirically proven research model contributes

to building a founding theory by which to understand the

behaviors of individuals in VWs.

While most IS studies focus on technologies while

examining consumer VW behavior, this study investigates

both the technical and social characteristics of a VW. The

results show that the technical characteristics (i.e., inter-

activity and vividness) of the virtual world influence virtual

experience, which in turn affects consumers’ purchase

behavior. This is consistent with the view that technical

features influence consumers’ virtual experiences. These

results suggest that high levels of interactivity and a vivid

representation in the VW increase flow. Moreover, a higher

level of involvement with the VW improves consumers’

virtual experience. In fact, the path coefficient in the PLS

model reveals that social characteristics (i.e., involvement)

exhibit a significantly stronger influence on virtual expe-

rience than do technical characteristics (i.e., interactivity

and vividness), which in turn affects purchase behavior. On

the basis of these findings, I suggest that VW platform

providers should develop sociability features (such as

public places) to enhance consumers’ interactions with

their fellow VW members. For example, providers can

hold events or meetings in public places that any consumer

can attend, allowing them to interact with the VW.

The results of this study provide insight into a complex

process influencing consumers’ intention to purchase vir-

tual products. All relationships examined in the model

(except satisfaction with willingness to purchase) are sup-

ported. The results show that flow and product value are

directly related to consumers’ purchasing intention. Con-

sumers’ beliefs about product value are more strongly

related to purchasing intention than is flow. However, flow

exerts a stronger influence on product value than does

involvement. The three flow-related factors are interactiv-

ity, vividness, and involvement; involvement is more than

twice as effective as is interactivity or vividness in

improving consumers’ flow experience.

5.2 Implications for practice

As VWs emerged and became sophisticated very quickly,

most VW businesses are still in their infancy; many are still

exploring and understanding community activities and

their business potential. The results of this study provide

0.274**

0.0400.300**

0.615***

0.195*

0.429***

0.222*

0.320***

* Significant at p<0.05 level** Significant at p<0.01 level*** Significant at p<0.001 level

0.516***

Involvement

Interactivity

Willingness to Purchase

Vividness

Satisfaction

Product Value

Flow

R2=0.489

R2=0.286 R2=0.539

R2=0.378

Fig. 2 Results of structural model

326 Inf Technol Manag (2013) 14:315–330

123

significant implications for those seeking to design a VW

that allows user immersion. The PLS analysis shows that

involvement is more effective than either interactivity or

vividness in allowing users to be immersed in their VWs,

suggesting that platform and Web designers should focus

more on social characteristics that improve involvement

levels than on technical characteristics that enable inter-

activity and vividness. This finding confirms the view that

the design of a virtual world is not only about the design of

the technology but also about the means of and tools for

social behavior [41]. Designers are encouraged to look

beyond the technical characteristics of their VWs and focus

on improving the social characteristics within them. I

recommend that designers offer social features allowing

consumers to interact and be involved with VW activities.

Managers who operate communities in VWs are also

encouraged to follow the guidelines suggested by this study

in designing and managing their communities. This study’s

results suggest that they should focus not only on imple-

menting technical features but also on developing social

features. Both technical and social features have a significant

impact on aspects of the virtual experience, including flow

and satisfaction; thus, managers are encouraged to enhance

all three driving factors, which may give them more users

who are loyal to their communities and help them achieve an

optimal virtual experience. However, a comparison of the

path coefficients reveals that the link between flow and

involvement is stronger than the other links. I therefore

propose that managers constrained by limited resources

should focus on enhancing the social features of the VW.

The study’s results can also help sellers understand better

the factors that drive consumers to buy virtual products.

Consumers who have a higher level of flow and product

value perception have a greater intention to purchase virtual

products, and product value has a stronger influence than

flow on consumers’ willingness to purchase. These findings

suggest that consumers tend to be value-driven and that

perceived product values are very important for purchasing

intention. This study proves that, even in VWs, delivering

product value to consumers is a key to success for sellers, a

conclusion consistent with the research finding that sellers

should identify what their potential customers value when

purchasing a product, focus on that product value, and be

good at delivering that value to customers [89]. Therefore,

while improving flow states, sellers are also encouraged to

enhance product value for consumers.

The results of this study, which show that involvement

and flow influence product value, indicate that sellers can

provide products targeted to people with a higher level of

VW flow and involvement. For example, sellers can focus

on the communities of a virtual world with many immersed

members who are highly involved in their VW activities.

A VW that provides better technical and social features

exerts a favorable influence on consumers’ purchasing

behavior. Therefore, sellers can advertise their products in

these VWs and expect better marketing results.

5.3 Suggestions for future research

As with most empirical studies, this study has generaliz-

ability limitations owing to its sample size and respondent

spectrum. Because this study focused on a specific VW,

Second Life, rather than VWs in general, the scope of the

sample is limited. Although the sample size is substantial

and I strove to include a range of individuals representing

many demographic VW consumer groups, the sample data

may not truly represent all VW consumers. A larger sample

size with data collected from other VWs, such as Habbo

Hotel, Cyworld, and There, would have increased the

generalizability of this study. It would also be interesting to

investigate consumers in different VWs and compare their

perceptions, virtual experiences, and behaviors.

This study examined consumers’ willingness to pur-

chase virtual products instead of their actual purchasing

behavior. Many researchers have shown that specified

behavior is determined by an individual’s behavioral

intention to perform the behavior [21, 25, 87]. Thus, these

findings are useful for practitioners. However, behavioral

intention may be a weak proxy for actual purchasing.

Future research that measures and investigates consumers’

actual VW purchasing behavior will be more practical.

Future research can also examine the purchasing behavior

differences revealed before and after virtual experiences or

product purchases, which could identify whether a con-

sumer’s previous virtual experience or purchasing experi-

ence affects actual purchasing behavior.

Interactivity, vividness, and involvement are the three

VW platform criteria, and flow and satisfaction represent

virtual experience. However, these factors do not represent

comprehensive characterizations of VW platforms or virtual

experiences. Identifying new constructs relevant to the VW

context and examining their impact on consumer behavior is

worthy of researchers’ and practitioners’ attention. Drawing

on new theories from other disciplines and applying them

appropriately to the IS field would be a promising advance

toward understanding complex phenomena that cannot be

explained by the IS literature alone. It is thus anticipated that

future research will introduce other VW aspects related to

consumers’ VW purchasing behavior.

6 Conclusion

In VWs, virtual marketplaces offering various business

opportunities are becoming increasingly important, sophis-

ticated, and competitive. The VW ecosystem is expanding

Inf Technol Manag (2013) 14:315–330 327

123

socially and economically as parties such as retailers, ga-

mers, advertisers, and social agents become more heavily

engaged. The emergence of VWs has been so rapid that solid

theories attempting to explain emerging VW dynamics and

issues are few. This study attempts to complement the VW

knowledge base in the IS field and help parties involved in

VWs achieve their optimal goals by presenting useful

values.

This study illustrates a complex process influencing

consumers’ VW purchasing behavior. Its results show that

platform context (i.e., interactivity, vividness, and

involvement) affects the virtual experience (i.e., flow), in

turn leading to consumer purchasing behavior. Addition-

ally, product value, which is affected by involvement and

flow, impacts consumers’ willingness to purchase virtual

products. This study’s results highlight the importance of

consumers’ VW flow experience by confirming that flow

experience mediates the impact of VWs’ technical and

social characteristics on purchasing behavior. Another

interesting finding is that flow is positively associated with

satisfaction that can lead to subsequent behaviors such as

revisiting and continuous purchasing in the VW.

I hope that the theories proposed in this study will help

both researchers and practitioners review consumers’ VW

behaviors and offer useful contributions to the IS literature.

I expect that VWs will continuously evolve under the

power of new technologies and a changing environment.

As VWs evolve, it will be important to develop a better

understanding of their phenomena. I therefore hope that

future research will continue to explore VWs and develop

new theories enabling a better understanding of them.

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