Classifying, Measuring, and Predicting
Users’ Overall Active Behavior on Social
Networking Sites
AIHUI CHEN, YAOBIN LU, PATRICK Y.K. CHAU, AND
SUMEET GUPTA
AIHUI CHEN is an assistant professor in the College of Management and Economics at
Tianjin University, China. He obtained his Ph.D. at Huazhong University of Science
and Technology in China in 2014. His research focuses on social commerce, social
networks, and electronic and mobile business. His research has been published in
Journal of Information Technology, Journal of Business Research, and other
journals.
YAOBIN LU is a specially appointed professor in information systems and the
associate dean of the School of Management at Huazhong University of Science
and Technology in China. He obtained his Ph.D at Huazhong University of Science
and Technology in 1997. His research interests include social commerce, mobile
commerce, customer trust, electronic commerce, and related topics. He is the author
of more than forty publications in various international journals, such as Decision
Support Systems, Information Systems Journal, Information & Management,
International Journal of Electronic Commerce, Journal of Information Technology,
and International Journal of Information Management. Yaobin Lu is the corresponding author of this paper.
PATRICK Y.K. CHAU is the Padma and Hari Harilela Professor in Strategic Information
Management at the Faculty of Business and Economics of the University of Hong
Kong. He obtained his Ph.D. in business administration from the Richard Ivey
School of Business at the University of Western Ontario, Canada. His teaching
and research interests are in management of information systems, electronic commerce, and research methodology. His work has appeared in3 leading information
systems journals such as Journal of Management Information Systems, MIS
Quarterly, Journal of the Association for Information Systems, Decision Sciences,
Decision Support Systems, Information & Management, and International Journal
of Electronic Commerce.
SUMEET GUPTA is chair of the Research Division at the Indian Institute of
Management Raipur, India. He received his M.B.A. and Ph.D. from the National
University of Singapore. He was associated with the Logistic Institute-Asia Pacific,
Singapore as a research fellow and worked on several projects with the ASEAN
Secretariat, DFS Gallerias, and SAP A.G., Walldorf, Germany. He is the author of
more than fifty publications in leading international journals (Decision Support
Systems, International Journal of Electronic Commerce, and others) and conference
proceedings (ICIS, AMCIS, ECIS, PACIS, and others). He has also published many
Journal of Management Information Systems / Winter 2014, Vol. 31, No. 3, pp. 213–253.
Copyright © Taylor & Francis Group, LLC
ISSN 0742–1222 (print) / ISSN 1557–928X (online)
DOI: 10.1080/07421222.2014.995557
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CHEN, LU, CHAU, AND GUPTA
book chapters. His research interests include supply chain management and business
analytics.
ABSTRACT: Although understanding the role of users’ overall active behavior on a
social networking site (SNS) is of significant importance for both theory and
practice, the complexity and difficulty involved in measuring such behavior has
inhibited research attention. To understand users’ active behaviors on an SNS, it is
important that we identify and classify various types of online behaviors before
measuring them. In this paper we holistically examine users’ active behaviors on an
SNS. Toward this end, we conduct three studies. First, we classify active behaviors
on an SNS into four categories using the Delphi method. Then, we develop a
measurement model and validate it using the data collected from an online survey
of 477 SNS users. The measures of the developed instrument exhibit satisfactory
reliability and validity and are used as indicators of the latent constructs. This
instrument is then used in a predictive model based on commitment theory and
tested using data from 1,242 responses. The results of data analysis suggest that
affective commitment and continuance commitment are good predictors of overall
active behavior on an SNS. This study complements the existing research on social
media, cocreation, and social commerce. Most important, this study provides a
theoretically sound measurement instrument that addresses the complex characteristic of overall active behavior on an SNS and which should be useful for future
research. The findings of this study have important implications for practice as they
highlight managing and stimulating users’ active behaviors on an SNS.
KEY WORDS AND PHRASES: Delphi study, measurement model, social media, social
networking site, user commitment, users’ active behavior.
A SOCIAL NETWORKING SITE (SNS) IS A FORM OF ONLINE COMMUNITY through which users
can connect with the people within and beyond their social circle. The pervasiveness
of services offered by SNSs and their increasing and widespread influence as a
source of information, as a marketplace, and as a setting for social contact has
sparked research interest about the behavior of people on an SNS [14, 90]. Recent
findings [92, 98] about the significant importance of users’ active involvement and
participation has further catalyzed this research interest.
Active behavior and lurking (passive) behavior are two common types of behaviors on an SNS [81, 95]. Active behavior refers to activities such as content
creation, information sharing, meeting new people online and chatting with them,
joining groups, talking about hobbies and personal interests, and posting or uploading videos or photos [9, 82], which require use of active functions provided by the
SNS. Lurking behavior refers to passive browsing of social network content [9, 82].
Depending on a user’s normal and regular behavior (active or passive), the user is
termed an active user or a passive user [11, 76]. When individuals choose to actively
participate in the communication, they are explicitly deciding to contribute their
time, energy, attention, knowledge, and relationships [15]. An active user actively
contributes to the content and relationships (e.g., posting comments or commenting
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on a comment) on an SNS. A passive user, also known as a lurker, browses the
content on the SNS but rarely contributes. According to Nonnecke and Preece [75],
such lurkers constitute around 90 percent of total members in online communities.
Lurkers add hardly any value to the growth of an online community [76] except for a
meager contribution to its branding and image building [92]. Some studies have
reported active behavior of members as fundamental to the growth of an SNS. For
example, Carroll [16] found that active users positively influence the sales revenue
of an SNS. Similarly, Lu and Hsiao [63] concluded that greater activity on an SNS
has the potential to increase revenues earned through advertising and subscription.
Trusov et al. [105] also reported the positive influence of users’ activity level on the
success of an SNS.
A successful SNS is characterized by a growing network of social relationships,
accumulation of user-generated content, and increasing volume of site visits and
traffic [21], all of which are necessary for market capitalization and revenue
generation [16]. Active behavior on an SNS enhances social activity, such as
browsing profiles, exchanging comments, and reciprocating favors, which ultimately leads to growth and expansion of social relationships on the SNS [21,
51]. Moreover, active users of an SNS contribute to the user-generated content,
such as photos, videos, and blogs. User-generated conrent contributes to the wealth
of an online community and increase its network externalities, which in turn
further promotes and propagates the acceptance of the SNS. Furthermore, active
behavior boosts the volume of site visits and traffic, which attracts advertisers and
helps in generating extra revenues [21].
Active Behavior Research
Several researchers have examined the active behaviors of users of SNSs. We can
categorize the research into three areas. First, researchers employing the social
identity perspective have analyzed the process of self-conception on an SNS [26],
which comprises a set of compatible and interrelated components, such as social
comparison, intergroup relations, self-enhancement, and social categorization. All
of these constitute active behavior on an SNS [45]. However, these studies have
reported mixed findings. Although some scholars have argued that active behavior on an SNS increases users’ social capital [18] and perceived social support
[61], reduces users’ perceived stress and loneliness, and ultimately leads to
higher psychological well-being and even physical health [7, 26, 41, 72], others
have indicated that active behavior on an SNS contributes little to users’ social
capital and social support. Such scholars have argued that active behavior
increases loneliness [6, 106], as the user may ignore the real world. Thus, we
can construe that the role of active behaviors on an SNS depends upon the type
of active behavior [7]. It is imperative, therefore, to accurately understand the
behavior of active users on SNSs.
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In a second area of research into the active behavior of SNS users, investigators
have examined such behavior as a contextualized form of cocreation. Cocreation is
an evolutionary process that occurs between the firm and users as well as within the
community of users [91]. Active behavior on an SNS can contribute content and
relationship resources to the community [21, 61]. These resources become valuable
through mutual interaction among SNS users. Thus, SNS users often act as “partial
employees” of SNSs [111], because they are the primary human resources who
coproduce and generate the excitement and services that determine the level of
traffic in the community. As partial employees of SNSs, they can contribute by
engaging in various active behaviors that improve the performance of SNSs. Zwass
[114, p. 31] emphasized the need for much contextualized research to explore how
various cocreation patterns are performed.
A third line of investigation into the active behavior of SNS users has employed
the social commerce perspective in emphasizing the fundamental role of active
behavior on an SNS. Social commerce involves using Web 2.0 technologies and
infrastructure to support online interactions and user contributions to assist in online
buying and selling of products and services [81]. Researchers have indicated that
customers rely on social behavior to access others’ knowledge and experiences so as
to make better-informed and accurate purchase decisions [48, 56]. Thus, the fundamental social behavior can facilitate commerce by reducing uncertainty and aiding
customers in making rational decisions [66, 85]. Researchers have also noted that
active behavior on SNSs is a major force in the marketplace [44]. SNSs provide
companies with tools to launch targeted campaigns based on their community users’
profiles [102]. Users can participate in these campaigns by posting comments and
interacting with community members.
Another key characteristic of social networks is the speed with which information
is dissemminated. The information in social networks spreads like an epidemic,
reaching large audiences through users’ sharing behavior [102]. Thus, different types
of active behaviors have different effects on a firm’s marketing function. Heinonen
[44] indicated that research is needed to understand the implications of each activity
in order to identify the types of activities that are needed or desirable. Some
activities may influence the company image and brand positively, but others may
not be so desirable.
The foregoing discussion underlines the importance of classifying various types of
behavior performed by active users on an SNS. A user’s overall active behavior on
an SNS incorporating different types of active behaviors has not been examined yet,
perhaps because of the inherent difficulty underlying measuring the large and
complex activities performed on an SNS. Thus, although SNSs have been studied
widely, the endeavor to understand users’ overall active behavior on them is still in
its nascent stage.
This study first develops a complete measurement instrument for assessing users’
overall active behavior on an SNS and then develops a model to validate it
empirically. The methodology is based on the measurement development paradigm
proposed by Churchill [27], which has been adopted in IS studies [10, 29]. This
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217
paradigm includes four phases: (1) construct domain specification, (2) construction
of items, (3) data collection, and (4) measures purification. Each phase focuses on
satisfying the validity and reliability concerns about the construct through iterative
development and testing. To achieve this goal, this study is divided into three parts.
The first part aims at developing appropriate categories of behaviors that would
reveal the essence and contribution usually made by an active user of an SNS. This
helps generate a pool of items required for the development of indicators. The
second part aims at developing a measurement model to operationalize these behaviors and testing them in an empirical setting. In the third part, nomological validity
[10] is tested. Nomological validity is the ability of a new measure to perform as
expected in a network of well-established measures [10]. We place the empirically
validated measurement model in a research study that investigates the mind-sets
driving the overall active behavior on an SNS. The research model in this study is
based on commitment theory and is empirically examined using data collected from
a survey of 1,242 actual users of Renren.com, the biggest SNS and the equivalent of
Facebook in China. This process further validates the construct, as well as evaluates
the nomological validity of the proposed measurement instrument.
Users’ Active Behavior on Online Communities and SNSs
AN ONLINE COMMUNITY IS GENERALLY REGARDED AS a cyberspace domain supported
by computer-based information technology and centered upon communication
and interaction of participants to generate member-driven content that results in
a relationship [57, 64]. Traditional online communities are of four types: (1)
interest communities (such as the open source software community), in which
people who share an interest or expertise on a specific topic gather to communicate with one another [33]; (2) relationship communities (such as AIDS and
Hepatitis B bulletin board system communities), in which people with similar
experiences gather and form meaningful relationships [65]; (3) fantasy communities (such as Second Life and World of Warcraft players), which usually refer
to online game player sites where people gather to fulfill their fantasies [113];
and (4) transaction communities (such as the community embedded in Amazon.
com), which focus on transaction needs and provide people with trading information [64]. Active behavior in these communities has been conceptualized as
posting behavior [75, 96].
Nonnecke et al. [76] noted that whereas posters frequently display extrovert
behavior, lurkers exhibit introvert behavior limited to observing and browsing
postings. This poster-lurker dichotomy, however, describes the two end points of
the SNS behavior spectrum and does not consider the activity level anywhere
between the end points (such as for users who are somewhat active or mildly
active). Kim [52] proposed a more thorough framework that models users’
behavior specifically in an online community. This framework differentiates
among several active behavioral roles: (1) the visitor, who exhibits unstructured
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CHEN, LU, CHAU, AND GUPTA
participation; (2) the novice, who invests time and effort in order to become an
active member; (3) a regular member, who exhibits full commitment; and (4) the
leader, who sustains membership participation and guides interactions of others
[79]. Preece and Shneiderman [89] proposed a reader-to-leader framework that
emphasizes different needs and values at different levels of active behavior: (1)
reader, who only consumes articles/content; (2) contributor, who contributes
some content to the community; (3) collaborator, who participates in group
projects; and (4) leader, who leads the community and moderates discussions.
Active behavior has been conceptualized as high intensity of SNS use [62, 87]. A
few studies have applied these conceptualizations of active behavior to the SNS
context. Rau et al. [92], for example, classified the respondents into posters and
lurkers based on their posting frequency. This conceptualization asserts that
active behavior is content contribution behavior, and lurking behavior is content
consumption behavior [82, 92]. However, although these frameworks describe
the spectrum of active behavior to some extent, they lose sight of the collaboration and socialization roles of active behavior.
Online Communities Versus Social Networking Sites
SNSs are different from traditional online communities in organization structure
and function, and therefore the users’ behavior pattern on SNSs is different from
that of traditional online communities. SNSs are gnerally defined as web-based
services that allow individuals to (1) construct a public or semipublic profile
within a bounded system, (2) articulate a list of other users with whom they share
a connection, and (3) view and traverse their list of connections and those of
others within the system [12]. People go to SNSs mainly to satisfy their socioemotional needs rather than their informational needs [92, 102]. Although there
are relationship communities that are designed to exchange emotional support
similar to that offered by SNSs, such exchange occurs primarily among strangers,
rather than among offline acquaintances as on an SNS [92]. Consequently, the
users’ motivation for action and their behavioral pattern on an SNS are quite
different from that of other types of online communities. Moreover, SNSs
comprise both content and social linkages [39], which means the active behavior
on an SNS should include both cotent-related behavior and relationship-related
behavior.
Shao [97] proposed that people perform a variety of activities on an SNS: (1)
consumption of information and entertainment, (2) participation in social interaction
and community development, and (3) production of self-expression and self-actualization. Heinonen [44] divided social media activities into three categories depending on
the motivation behind the activity: (1) information-processing activities, (2) entertainment activities, and (3) social connection activities. These researchers conceptualize
active behavior as the various activities users perform using the active functionalities of
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219
SNSs to satisfy their various needs [44, 97]. However, these frameworks are limited in
that they do not discuss the cocreation pattern and the resources that active behaviors
contribute to the SNS. Therefore, this study develops a framework describing users’
specific active behaviors on an SNS, depicting users’ contribution of content and
relationship, and reveals the cocreation pattern in the context of an SNS.
Users' Active Behavior on SNSs
A few studies [12, 61, 92] on users’ active behavior on SNSs have examined various
kinds of active behaviors. Broadly speaking, these active behaviors can be grouped
into four categories, namely, content creation, content transmission, relationship
building, and relationship maintenance. This categorization conforms with Boyd
and Ellision’s [12] assertion that content and relationships are two main elements of
social network services used primarily by users. These two elements, that is, content
generation and social interaction, can also be understood as relationship contribution
activities in the social media [81]. Researchers posit that individuals use social media
to participate in social networks, which enables them to create and share content,
communicate with one another, and build relationships [2].
Studies on content creation have focused mainly on factors that motivate a user to
create or generate content in an online service. For example, Zhang and Zhu [112]
explored the intrinsic motivation of open content contributors in an open source
community. Daugherty et al. [30] explored consumer motivations for creating usergenerated content in the social media. Nov et al. [78] explored the factors that affect
individuals’ metaknowledge contribution in a popular photo-sharing site, and Nosko
et al. [77] examined the relationship between users’ personal information and their
information disclosure behavior in online social networking profiles. A few other
researchers have also examined the value of user-generated content. For example,
Park et al. [83] concluded that user-generated reviews can influence consumers’
purchase intention, and Ghose and Ipeirotis [38] demonstrated the economic value of
user-generated content. In summary, prior literature suggests that content creation
behavior is an important indicator of users’ participation in SNSs and that usergenerated content is an essential contribution to SNSs [19, 78].
Studies on content transmission focus on why an SNS user engages in sharing
behavior, such as sharing blogs, photos, and videos [12]. Previous studies have
investigated this issue from various perspectives. Chiu et al. [25] integrated social
cognitive theory and social capital theory to construct a model for investigating the
factors motivating people’s knowledge sharing in virtual communities. Acquisti and
Gross [1] investigated the motivation and privacy issues associated with information-sharing behavior on Facebook among college students and faculty members.
And Garg et al. [37] classified the content diffusion in social networks into two
categories, namely, influence (by a system or a peer) and discovery (by active
search), and then developed an empirical method to measure content discovery
and content diffusion in online social networks. Users’ content transmission
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CHEN, LU, CHAU, AND GUPTA
behavior is vital for online marketing (such as online word of mouth) performance
[37] and extracting economic value from user-generated content [38].
Studies on relationship building are based on the premise that SNSs can help strangers
connect with each other based on their shared interests, political views, or activities [12].
After joining an SNS, users are prompted by the system to identify others with whom
they have a relationship within the system. For example, LinkedIn has a referral system
so that these users can be introduced, through a chain of friends of a friend, to the person
they intend to meet. Building relationships on an SNS can help users enhance their
online social capital [106], as well as accelerate the transmission of information and thus
generate social and economic value [100]. For example, Vergeer and Pelzer [106]
examined the influence of online relationship building behavior on offline and online
network capital and well-being.. Similarly, Antheunis et al. [6] investigated strategies to
reduce uncertainty in relationship building activity through SNSs. A few other studies in
this category have focused on relationship building between customers and vendors
through social media. For example, Wang and Head [109] analyzed the influence of
Webcharacteristics in building consumer relationship mechanisms in online services.
Social relationships are dynamic and require active maintenance for their survival.
If no effort is made by the relationship partners, the relationships tend to decay over
time [94]. Roberts and Dunbar [94] examined the effects of social network size,
emotional closeness, and type of relationship (kinship vs. friendship) on users’
relationship maintenance behavior in the social networks. Similarly, Park et al.
[84] also examined the influence of the need for affiliation on the motivations for
relationship maintenance in the context of Facebook.
Thus, active behavior on an SNS involves content creation and content transmission, relationship building, and relationship maintenance. In this study, we conceptualize the overall active behavior on an SNS as users’ specific activities using the
active functionalities in the SNS, which includesusers’ contributions to content and
relationship and reveals the cocreation pattern on an SNS. Active user in this study is
defined as an individual who participates in these four kinds of active behaviors—
content creation, content transmission, relationship building, and relationship maintenance—normally and regularly. Although the four proposed categories are based
on a literature review, they have not been empirically investigated. We now examine
these categories empirically using a Delphi study.
The Delphi Study
Although we could have conducted a traditional survey or literature review [10] to
gather users’ various active behaviors on SNSs and then classified them into
categories through a content analysis or card-sorting approach [70], the Delphi
method is a superior method because it involves rigorous queries from experts and
stakeholders [80]. A Delphi study attempts to obtain consensus from a group of
experts using repeated responses on questionnaires and controlled feedback [74]. A
key advantage of this approach is that it avoids direct confrontation among experts.
USERS’ ACTIVE BEHAVIOR ON SNSS
221
The process used in this study involved two panels consisting of nineteen participants.
The first panel comprised ten active users of SNSs who logged in to their SNS daily and
frequently participated in various contribution activities on the SNS. We selected them as
the most active group (called “popular stars” in online social networks), and all of them
claimed to be active users of an SNS. Although not all the behaviors of active users on an
SNS can be regarded as active behavior, active users carry out active behavior normally
and regularly. Thus, they can provide a relatively complete item pool of active behavior.
The second panel comprised nine academic scholars who had research experience and
publications in online human behavior and were familiar with the concept of users’ overall
active behavior on an SNS. They can guard the completeness and conciseness of the item
pool. As advised by Okoli and Pawlowski [80], a panel size of nine or ten is considered
appropriate in a Delphi study of this nature. We chose to have two panels of almost equal
size so as to maintain a balance between the two groups of panelists and so that the opinions
and comments obtained from the study would remain unbiased toward either side.
Following standard Delphi procedures [80], we carried out the Delphi study in
three phases: a brainstorming phase to generate a list of users’ active behaviors on
SNSs, a narrowing-down phase, and finally a classification phase to classify users’
active behaviors identified in the previous phases. Figure 1 outlines the process of
administering the study.
In the first phase, ten panelists were asked to consider the functionalityand list all
possible (at least ten) actions/behaviors carried out by a user on an SNS. A total of
thirty-four active behaviors were identified at the end of this phase (see Table 1). In
the second phase, each panelist was asked to select—not rank—at least ten active
behaviors that they considered important in contributing to either content or relationships [61]. This process reduced the list of active user behaviors to twenty-seven.
(The seven behaviors that were discarded are shown in italics in Table 1.) The last
phase in the Delphi study was classification of behaviors. For the purpose of
classification, the panelists were asked to ponder two questions: (1) Does the
behavior contribute either to content or relationship in an SNS? and (2) What is
Phase 1
Panelists: Ten active users of an SNS
Brainstorming
Step 1: Panelists were asked to list at least ten user behaviors in an SNS
Step 2: Panelists were asked to verify the behavior list and suggest additional items if they saw the need
Panelists: All nineteen experts
Phase 2
Step 1: Panelists were asked to select (not rank) at least ten active behavioral items that they considered
Narrowing down
important for contributing either content or relationships
Step 2: Items selected by over 50 per cent of panelists were retained
Panelist: All nineteen experts
Phase 3
Step 1: Panelists were asked to classify the retained items into four categories (content creation, content
Classifying
transmission, relationship building, and relationship maintenance)
Step 2: Aggregated the responses statistically, and iterated the process until responses attained
Figure 1. The Administration Process in the Delphi Study
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Table 1. Users’ Active Behaviors in SNSs (Thirty-Four Items)
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Item
Posting blogs/articles
Changing/posting current status
Posting photos/videos
Updating/editing profile
Tagging photos
Sharing resources from other sites on SNSs
Sharing friends’ sharing
Sharing friends’ blogs
Sharing friends’ photos
Sharing or transmitting friends’ statuses
Creating groups or public profiles
Visiting public profiles or discussion board
Joining groups or public profiles
Creating events and sending invitations
Searching friends and sending application for adding friends
Accepting application for adding friends
Sending private messages to nonfriends
Responding to invitations to participate in events from groups
Interacting with groups
Participating in the topic of friends
Commenting on photos
Chatting with friends through SNSs
Looking at personal information of friends
Visiting friends’ profiles
Looking at news about friends
Looking at photos of friends
Reading my news feed
Reading posts on my wall
Reading posts on friends’ walls
Posting on friends’ walls
Reading private messages from friends
Sending private messages to friends
Playing social games in SNSs
Using social applications in SNSs
Note: Items 28–34 were discarded in the second phase.
the specific result essentially generated by that specific behavior? Panelists were also
asked to provide a brief explanation or justification for their classification. The
iterations were stopped at the fourth round, as all panelists declared that they had
sufficiently deliberated on the issue and were satisfied with the final classification.
After getting stable responses from all panelists, we examined the level of consensus
statistically, both through the level of agreement and the coefficient of variation among
panelists. Consensus, which can be either agreement or disagreement with a statement,
USERS’ ACTIVE BEHAVIOR ON SNSS
223
can be defined as a percentage higher than the average percentage of majority opinion, and
majority is usually defined as a percentage above 50 percent [107]. As shown in Table 2,
the panelists’ agreement reached an acceptable level of consensus for all the items except
one. The agreement level of one item (tagging photos) fell below the lowest acceptable
level of 50 percent, suggesting that tagging photos might be a complex behavior
contributing to both content and relationship and should be investigated separately [78].
Table 2. Classification Results of the Delphi Study
Dimension
Content Creation
(CC)
Content
Transmission
(CT)
Relationship
Building (RB)
Relationship
Maintenance
(RM)
Item
Posting blogs/articles
Changing/posting current status
Posting photos/videos
Updating/editing profile
Tagging photos (dropped)
Sharing resources from other sites
to SNSs
Sharing friends’ sharing
Sharing friends’ blogs
Sharing friends’ photos
Sharing or transmitting friends’
statuses
Creating groups or public profiles
Visiting public profiles or discussion
board
Joining groups or public profiles
Creating events and sending
invitations
Searching friends and sending
application for adding friends
Accepting application for adding
friends
Sending private messages to nonfriends
Responding to invitations to
participate in events from groups
Interacting with groups
Participating in the topic of friends
Commenting on photos
Chatting with friends through SNSs
Looking at personal information of
friends
Visiting friends’ profiles
Looking at news about friends
Looking at photos of friends
Reading my news feed (dropped)
Agreement
level (percent)
Coefficient
of variation
94.7
94.7
94.7
94.7
47.4
100
.242
.242
.242
.242
1.08
0
100
100
100
94.7
0
0
0
.242
94.7
78.9
.242
.531
89.5
84.2
.352
.445
89.5
.353
100
0
94.7
100
78.9
100
94.7
94.7
100
100
100
100
57.9
.242
0
.531
0
.242
.242
0
0
0
0
.876
224
CHEN, LU, CHAU, AND GUPTA
We calculated the coefficient of variation for each item, and the results show a
very high level of consensus for all the items except two (see Table 2). Tagging
photos and reading my news feed showed poor consensus (V > 0.8) [32]. Following
von der Gracht [107], we dropped these two items from further analysis. Thus, the
final list contains twenty-five user active behaviors.
It is important to note that some SNSs also allow commercial applications within the
SNS, and hence there may be some active behaviors on the SNS that are related to
“social commerce” [60]. However, none of the items describing users’ commercial
behavior were incorporated in our Delphi results. Nevertheless, the items identified in
this study can represent the primary active behvior on an SNS, for three reasons. First,
most SNSs are mainly designed to satisfy users’ social needs [12], rather than commercial needs. Commercial activity is not the main function, but rather an extension of SNSs
[108]. Second, although a few social commerce sites have emerged in recent years
through a combination of the social function and the commercial function, users can
only realize the commercial behaviors by carrying out the basic social activities. For
example, Liang et al. [61] designed items to assess a user’s intention to participate in
social commerce by referring to recommend shopping information and products and
receive shopping information and products on SNSs. These activities could only be
realized through “sharing friends’ blogs,” “sharing friends’ photos,” “visiting friends’
profiles,” or “looking at news about friends.” Third, although the student sample may
bias the results to some extent, the mainstream SNSs (e.g., Facebook) were designed
first for students in universities and later were extended to other populations in the
society. College students are a significant segment of SNS users. Eighty-five percent of
U.S. college students use Facebook, and 95 percent of Chinese students use Renren [21].
Also, student samples are widely used in SNS studies [21–23]. Hence, we consider
using the student sample as appropriate for the current research.
Measurement Model Development
WE USED THE RESULTS OBTAINED FROM THE DELPHI STUDY to develop a measurement
model that can be used in further research related to active behavior on an SNS.
After categorizing twenty-five user active behaviors in four groups, we conducted a
second study to test the validity of this classification.
Method
Data for this study were collected using an online survey. The survey instrument
comprises twenty-five behavior items obtained from the Delphi study.
Respondents were asked to rate the frequency of these behaviors on SNSs on a
seven-point scale ranging from 1 (never) to 7 (very frequently). A pilot test of
twenty subjects suggested that at least five minutes were needed to complete the
questionnaire.
USERS’ ACTIVE BEHAVIOR ON SNSS
225
Our sampling frame was users of Renren (www.renren.com), the Chinese equivalent of Facebook. A survey hyperlink was placed on the forums of Renren to solicit
participants. This hyperlink could be shared by users and thus could be spread
virally throughout the SNS. To attract more participants, each respondent got ¥1
as a reward for participating. We paid the respondents through a third-party payment
platform called Alipay. To ensure that each respondent responded only once, we
noted each participant’s IP (Internet protocol) address and demographic information.
Five hundred forty-seven Renren users completed the online survey in twenty days.
After deleting responses with missing values and those completed in less than five
minutes, we retained 477 valid questionnaires. The demographics of these respondents are summarized in Table 3. On comparing the demographic features of the
current study with that of the large-scale national survey about SNS users in China
[28], we found that only the identity feature showed significant difference. A
plausible explanation is that our survey was aimed at active users on SNSs, rather
than general users [28], and students generally exhibit more active behavior [21].
Thus, our sample is representative of the entire active population of Renren users.
Results
Exploratory Factor Analysis
After obtaining the survey results, we then performed exploratory factor analysis using
principal component analysis with Varimax rotation on the collected data. Both the
Table 3. Descriptive Statistics of Survey Respondents
Category
Gender
Age
Education
Identity
Relationship
status
Items
Male
Female
≤ 19
20–29
≥ 30
High school or
below
Junior college
Undergraduate
Graduate or
above
Students
Nonstudents
Single
Just in love
Married
Sample (N = 477)
CNNIC Sample
Frequency Percentage
Percentage
220
257
36
419
22
31
46.1
53.9
7.5
87.8
4.6
6.5
53.7
46.3
29.0
56.8
14.2
7.2
135
281
30
28.3
58.9
6.3
37.4
42.7
12.6
263
214
264
156
57
55.1
44.9
55.3
32.7
11.9
Chi-square
differences
χ2(1) = 1.28
(p = .258)
χ2(2) = 2.674
(p = .102)
χ2(3) = 6.335
(p = .096)
26
χ2(1) = 17.450
(p = .000)
74
There are no data about
participants’ relationship status
in the CNNIC’s survey in 2012.
226
CHEN, LU, CHAU, AND GUPTA
Bartlett’s test of sphericity (770.47 at p = 0.000) and the Kaiser–Meyer–Olkin measure of
sampling adequacy (KMO = 0.922) indicated that there were sufficient interitem correlations within the data for conducting factor analysis. The results of factor analysis show that
five items were dropped for weak loadings and twenty items are clearly loaded on four
constructs (see Table 4). The four factors together account for 72.2 percent of the total
variance.
The items for the two content constructs show clear and clean loadings, with three and
five items loaded on the two constructs, content creation and content transmission,
respectively. We therefore keep the original labels, CC (content creation) and CT (content
transmission), for them. The items for the two relationship constructs, however, did not
load on the two constructs as we expected. Some of the items for relationship building
loaded on relationship maintenance, and vice versa. On closer examination, we found that
the six items that loaded on the first factor were all related to relationships among
Table 4. Results of the Exploratory Factor Analysis (Twenty Items)
Component
Item
1
2
3
4
Posting blogs/articles
Changing/posting current status
Posting photos/videos
Sharing resources from other sites on SNSs
Sharing friends’ sharing
Sharing friends’ blogs
Sharing friends’ photos
Sharing or transmitting friends’ statuses
Creating groups or public profiles
Visiting public profiles or discussion boards
Joining groups or public profiles
Creating events and sending invitations
Responding to invitations to participate in events from
groups
Interacting with groups
Searching friends and sending application for adding
friends
Accepting application for adding friends
Looking at personal information of friends
Visiting friends’ profiles
Looking at news about friends
Looking at photos of friends
.069
.284
.195
.240
.292
.195
.142
.208
.047
.271
.194
.159
.246
.364
.221
.201
.188
.128
.222
.254
.233
.798
.708
.783
.752
.654
.179
.257
.286
.754
.818
.825
.834
.797
.153
.151
.147
.224
.230
.771
.762
.764
.183
.222
.184
.172
.112
.230
.119
.101
.141
.262
.180
.719
.802
.187
.187
.209
.143
.074
.679
.817
.864
.851
.827
.181
.198
.182
.156
.125
.258
.159
.143
.131
.203
.085
.104
.135
.156
.136
Eigenvalue
Variance %
Cumulative %
4.400 4.010 3.850 2.187
22.000 20.049 19.248 10.935
22.000 42.049 61.297 72.232
Notes: All the boldface factor loadings are significant at the 0.001 level.
USERS’ ACTIVE BEHAVIOR ON SNSS
227
individuals (such as “searching friends and sending application for adding friends,”
“accepting application for adding friends,” and “looking at personal information of
friends”), which indicates that users were devoting their efforts to personal relationship
building or maintenance. We therefore label this construct individual relationship contribution (IRC). Similarly, the other six items that load on the second factor indicate that
they are all related to relationships between individuals and groups. These items reflect
users’ efforts to build or maintain group relationships. Accordingly, we label this construct
group relationship contribution.
Confirmatory Factor Analysis
To confirm the factor structure obtained in the exploratory factor analysis, we
conducted a confirmatory factor analysis using AMOS (version 7.0). Each item
was restricted so as to load on its prespecified factor, and the factors themselves
were allowed to correlate freely (see Figure 2).
The results of the initial estimation of the first-order factor model were not satisfactory,
as the chi-square value of 780.623 (df = 164, p < 0.05) was significant. This is quite
obvious, given the large sample size. Other fit indices revealed a moderate fit (comparative fit Index (CFI) = 0.911; goodness of fit index (GFI) = 0.850; adjusted goodness of fit
index (AGFI) = 0.809; non-normed fit index (NFI) = 0.891; tucker-lewis index (TLI) =
0.897; Root Mean Square Error of Approximation (RMSEA) = 0.089). We then
optimized the measurement model based on modification indices and dropped “accepting application for adding friends,” “sharing resources from other sites to SNSs,”
“visiting public profiles or discussion board,” and “sharing friends’ sharing” step by
step. At each step we examined the fit indices. After these deletions, the fit indices
demonstrated a good fit between the model and the data (χ2/df = 2.856; CFI = 0.965;
GFI = 0.932; AGFI = 0.906; NFI = 0.947; TLI = 0.957; RMSEA = 0.062). Thus, we
retained sixteen items loaded on four factors. Next, we tested for reliability and validity.
Construct Reliability
The four factors have Cronbach’s alphas higher than 0.8, indicating adequate construct reliability. The composite reliabilities of each construct are all above 0.9,
indicating that the constructs used in this study have acceptable levels of individual
item reliability (see Table 5).
Convergent Validity and Discriminant Validity
We confirmed convergent validity by examining both the average variance extracted
(AVE) and indicator loadings. As shown in Table 5, all AVE values were higher than
the recommended level of 0.5 [34]. The standardized path loadings of all items in the
confirmatory factor analysis ranged from 0.661 to 0.925, which are all significant at
the level of 0.001 (Figure 2) [24]. This shows a good convergent validity [24].
228
CHEN, LU, CHAU, AND GUPTA
CC1
.726
.827
CC2
CC3
Content Creation
.785
.593
CT1
.867
.925
CT2
CT3
Content Transmission
.491
.829
.457
IRC1
IRC2
.661
.844
.643
.909
Individual Relationship
.883
Contribution
IRC3
.568
IRC4
.856
IRC5
.474
GRC1
GRC2
.777
.725
.771
Group Relationship
.774
Contribution
GRC3
GRC4
.843
GRC5
Figure 2. Confirmatory Factor Analysis Results of First-Order Factor Measurement Model
Table 5. Descriptive Statistics, Reliability, and Average Variance Extracted
Latent variable
Mean
SD
Cronbach’s
alpha
Composite
reliability
AVE
Content creation
Content transmission
Group relationship contribution
Individual relationship contribution
3.468
3.726
3.123
4.790
1.307
1.435
1.387
1.343
.833
.904
.884
.917
.900
.940
.915
.938
.750
.840
.684
.753
We evaluated discriminant validity of the factors using various approaches. For
each pair of factors, we compared the χ2 value for the baseline model with the
constraint model (by constraining their correlation as 1). The χ2 difference test was
USERS’ ACTIVE BEHAVIOR ON SNSS
229
Table 6. Pairwise Discriminant Validity Analyses
Two-factor combinations for constrained
measurement models
Content creation & Content transmission
Content creation & Individual relationship contribution
Content creation & Group relationship contribution
Content transmission & Individual relationship contribution
Content transmission & Group relationship contribution
Individual relationship contribution & Group relationship
contribution
χ2
χ2 difference
286.782
331.508
294.372
309.928
285.984
328.210
6.908**
51.634***
14.498***
30.054***
6.11*
48.336***
Notes: The unconstrained measurement model: χ2 (98) = 279.874.
***p ≤ .001; **p ≤ 0.01; *p ≤ 0.05.
performed for each pair of factors (a total of six tests). The results of these tests
showed significant differences in each case, suggesting that none of the six pairs of
constructs could be merged as a single construct, thus demonstrating discriminant
validity. The results of χ2 difference tests are presented in Table 6.
We also assessed discriminant validity was by comparing the square root of the
AVE for each construct with the correlations between the construct and other
constructs [8]. As shown in Table 7, the square root of the AVE (diagonal elements)
was found to be larger than the correlations (off-diagonal elements) between constructs, suggesting discriminant validity.
We again performed a principal component analysis on the final list of sixteen items left
after the confirmatory factor analysis. The results of the analysis, presented in Table 8,
indicate that each item loaded onto only one factor, and no single factor accounted for the
majority of the variance. After rotation, each factor accounted for less than 25 percent of
the total variance, and all four factors accounted for 75.3 percent of the total variance.
Together with all the foregoing tests, the measures developed in this study
demonstrate satisfactory construct reliability and validity and thus serve as good
Table 7. Construct Correlation Matrix and Square Root of
Average Variance Extracted
Correlations
CC
CT
GRC
IRC
CC
CT
GRC
IRC
.866
.521
.564
.432
.917
.514
.447
.827
.442
.868
Notes: Diagonal elements in boldface display the square root of AVE.
CC = content creation, CT = content transmission, GRC = group relationship
contribution, IRC = individual relationship contribution.
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CHEN, LU, CHAU, AND GUPTA
Table 8. Results of the Principal Component Analysis (Sixteen Final Items)
Component
Final items
Posting blogs/articles
Changing/posting current status
Posting photos/videos
Sharing friends’ blogs
Sharing friends’ photos
Sharing or transmitting friends’ statuses
Creating groups or public profiles
Joining groups or public profiles
Creating events and sending invitations
Responding to invitations to participate in
events from friends
Interacting with groups
Searching friends and sending application
for adding friends
Looking at personal information of friends
Visiting friends’ profiles
Looking at news about friends
Looking at photos of friends
Eigenvalue
Variance %
Cumulative %
Labels
1
2
3
4
CC1
CC2
CC3
CT1
CT2
CT3
GR1
GR2
GR3
GR4
.069
.283
.196
.219
.168
.229
.061
.196
.160
.262
.367
.210
.191
.230
.241
.212
.793
.756
.779
.683
.143
.185
.260
.823
.868
.830
.162
.107
.189
.221
.774
.792
.780
.219
.210
.157
.232
.146
.156
.247
GR5
IR1
.196
.680
.821
.206
.180
.190
.138
.101
IR2
IR3
IR4
IR5
.838
.883
.873
.852
.191
.171
.148
.104
.150
.123
.103
.177
.105
.139
.159
.148
3.858
24.116
24.116
3.459 2.513 2.218
21.621 15.707 13.862
45.737 61.444 75.360
Notes: All the boldface factor loadings are significant at the 0.001 level.
indicators of the four latent constructs. The final survey instrument is presented
in Table 8.
Second-Order Factor Model
Because the four identified factors demonstrated good reliability and validity, we
tested a second-order factor model in which these four factors are viewed as
indicators of the second-order construct “overall active behavior in an SNS”
(Figure 3). We consider the overall active behavior in an SNS as a reflective
construct for three reasons. First, the theoretical direction of causality is from the
second-order construct to the first-order constructs, which is the characteristic of a
reflective construct [86]. If a user is an active user in an SNS, the user would post
content, transmit content, and contribute to the relationship. Each of the active
behaviors is a reflection of users’ overall activity. Second, all the correlations
between the four first-order factors are around 0.5 (as shown in Table 7), which is
relatively high. Internal consistency or reliability is important in reflective measures,
USERS’ ACTIVE BEHAVIOR ON SNSS
CC1
CC2
CC3
CT1
.761
.828
Content Creation
.786
CT2
IRC1
IRC2
IRC4
Content Transmission
Overall Active
Behavior on an SNS
.661
.610
.844
.883
Individual Relationship
Contribution
.856
.783
IRC5
GRC1
GRC2
GRC3
GRC4
.730
.829
.909
IRC3
.815
.867
.925
CT3
231
.776
.725
.771
.775
Group Relationship
Contribution
.843
GRC5
Figure 3. Second-Order Factor Measurement Model
implying that all first-order factors are measuring the same phenomenon, and if the
value for one of the measures changes, then the other values should also move in the
same direction [86]. This demonstrates that the four factors are reflecting the same
thing to a large extent [13, 86]. With formative constructs, measures need not covary.
In fact, strong intercorrelations among formative measures is an indication of multicollinearity. Third, to further test whether the second-order construct can be treated
as a formative construct, we calculated the level of collinearity of the four first-order
factors. As suggested by Cenfetelli and Bassellier [17], eigenvalues of the correlation matrix of predictors that significantly depart from 1.00 indicate mulitcollinearity. The results (eigenvalues are .048, .047, .040, and .025) demonstrate that
multicollinearity exists among these factors, which is a characteristic of a reflective
construct. Thus, the overall active behavior in an SNS can be considered to be a
reflective construct.
We used two different criteria to compare the second-order factor model with the
first-order factor model. First, we compared the statistics of the two models [99].
The fit indices of the second-order model (χ2/df = 2.802; CFI = .965; GFI = .932;
AGFI = .908; NFI = .947; TLI = .958; RMSEA = .062) are similar to that of the
first-order model. For this criterion, the second-order factor model is preferred over
the first-order factor model as it explains the covariance among the first-order factors
more parsimoniously [99]. Second, we computed the target coefficient to compare
the two models. Marsh and Hocevar [67] suggested that the efficacy of second-order
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CHEN, LU, CHAU, AND GUPTA
models should be assessed by examining the target (T) coefficient [T = χ2 (baseline
model)/χ2 (alternative model)]. When the target coefficient is close to its upper
bound of 1.0, the second-order factor model is preferred over the first-order factor
model [101]. The value of the target coefficient value in our case is 0.999, which is
very close to the upper limit of 1.0. This suggests that the second-order factor model
is superior to the first-order factor model. From these two tests, we conclude that the
second-order factor model is a better measurement model in operationalizing users’
overall active behavior on an SNS.
An Empirical Use of the Measurement Model
AFTER TESTING THE VALIDITY OF OUR MEASUREMENT MODEL, we conducted empirical tests
using commitment theory to further examine the model to understand overall active
behavior in an SNS.
Commitment Theory and Hypotheses Development
Commitment theory was developed by Allen and Meyer [3] in the domain of
organizational behavior. It explains employees’ psychological binding to the workplace or specific activities that benefit the organization [69]. According to Meyer and
Herscovitch, commitment is a stabilizing or obliging force that gives direction to
behavior (e.g., restricts freedom, binds a person to a course of action) [69].
Commitment can influence behavior independently, even in the absence of
exchange-based forms of motivation and target-relevant positive attitudes, and may
lead to persistence in a course of action, even in the face of conflicting motives or
attitudes [58, 69].
The notion of commitment has been used to examine users’ adoption and postadoption behavior in the context of online services. Li et al. [58], for example,
identified affective commitment and calculative commitment to explain the continuance intention of online services. Li et al. [58] further suggested that trust and the
quality of alternatives are antecedents to these two types of commitment in the web
service context. Kim and Son [54] explained the postadoption behavior of online
portal services by proposing a dedication-constraint dual model. In particular, the
model predicts that two contrasting mechanisms of commitment—dedication and
constraint—are the main drivers of postadoption phenomena (i.e., consumers’ postadoption reactions to online services, such as their beliefs, attitudes, intentions, and
behaviors). Building on this dedication-constraint framework [54], Zhou et al. [113]
integrated the concepts of affective and calculative commitment to represent two
distinctive commitment mechanisms and identified an encompassing set of antecedents of commitment specific to the social virtual world service context. Although
these studies are useful for predicting online services continuance in general, they do
not provide adequate insights into users’ overall active behavior in SNSs.
USERS’ ACTIVE BEHAVIOR ON SNSS
233
Allen and Meyer [3] identified three components of commitment that reflect three
distinguishable themes, namely, affective attachment to the target (affective commitment), perceived cost of leaving (continuance commitment), and obligation to act
(normative commitment). Affective commitment refers to the psychological state of a
user demonstrating an affective and emotional attachment to the service. Continuance
commitment (also known as calculative commitment) occurs when the user determines
that the costs associated with terminating use of a service or switching to another one
are too high [58]. Normative commitment, understood as a sense of moral obligation to
pursue a course of action [69], alludes to an obligation in terms of justice and duty.
In this study, we define affective commitment as “users’ affective and emotional
attachment to their active behaviors on an SNS.” The mind-set characterizing
affective commitment is desire: individuals with a strong affective commitment
want to pursue a course of action that is relevant to their target [69]. Aristotle
explained the reasons or aims for specific human actions, arguing that the concept of
the good is understood as the goal, or the end, of the action [40]. Although there are
many goods that we human beings pursue in our actions—such as friendship,
pleasure, virtue, honor, and wealth—these goods can be hierarchically ordered
[40]. The affective dimension of commitment moves into the arena of enjoyment,
of feelings and desires, which is the same as the attractiveness of pleasant goods
described by Aristotle. Pleasant goods can be identified with the affective dimension
of commitment, in which the world of emotions explains the attachment to courses
of action such as higher priority to community goals, means, or people. Affective
commitment also suggests that the user has positive attitudes toward the user’s past
active behaviors in the SNS, which is generally associated with affection, happiness,
and pleasure [49]. In the context of SNSs, desire or the attractiveness of pleasant
goods can be induced in a user by the user’s (1) becoming involved (intrinsically
motivated, absorbed) in a course of action, (2) recognizing the value-relevance of the
pursuit of the course of action, or (3) deriving his or her identity from association
with a community [69]. In addition, human beings need social interactions to satisfy
their social needs of belonging and support [68]. An SNS provides a platform
whereby users can exchange support with one another. Active behavior in an SNS
can help a person receive support from friends, which brings the person warmth.
Such good experiences satisfy users in their social interactions with their friends and
help fulfill the users’ social needs and produce affective attachment to active
behavior in the SNS [58]. Thus, such psychological desire could explain users’
overall active behavior in an SNS, which leads to the following hypothesis:
Hypothesis 1: Affective commitment is positively associated with the overall
active behavior in an SNS.
We define continuance commitment as “users’ recognition of the rewards and benefits
associated with being active on an SNS.” It is characterized by the perception that it would
be costly to discontinue a course of action. Such commitment occurs when a user
determines that the cost associated with just lurking in an SNS or switching to another
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CHEN, LU, CHAU, AND GUPTA
site is too high, considering the user’s investments in the current site [58, 69]. Continuance
commitment is, in fact, a kind of human action guided by the objective of external or
useful goods, as described by Aristotle [as discussed in 40]. In the context of SNSs, users
invest considerable time and effort in developing close interpersonal relationships with
other members of the network, thus building their reputation. By posting contents or other
acitve behaviors, they develop goodwill, trust, a sense of identity, and a reputation among
their friends, which enhances the value that they derive from using the network [93, 104].
Relationships and individual user reputation are valuable and durable in social networks,
and they are neither transferable nor easily replicable to other networks [104]. These
investments constitute sunk costs, which are diminished or lost if the user does not behave
actively in the specific SNS. Thus, such commitment may explain users’ overall active
behavior on an SNS, leading to the following hypothesis:
Hypothesis 2: Continuance commitment is positively associated with the overall
active behavior in an SNS.
We define normative commitment as “the obligation of a user to pursue active
behavior in an SNS.” Normative commitment develops when an individual (1) has
internalized a set of norms considered appropriate through socialization, or (2) is a
recipient of benefits and experiences the need to reciprocate [69]. Normative commitment is undoubtedly connected with the moral dimension, or moral good, as
described by Aristotle. This kind of good refers to desirable traits of character, to
human excellence or moral virtues. One of the moral virtues is rational evaluation of
the conscience about what is right or wrong, just or unjust, fair or unfair, and not
only a feeling [40]. Another moral virtue is sense of duty, or responsibility, or moral
accountability—the willingness to account for one’s actions [40]. In an SNS context,
a user may feel an obligation to behave actively when the user benefits from the
online actions of friends, or when individuals around the user are very active. To do
this, the user can accept feedback from others and avoid being isolated from friends.
And a user should feel guilty when the user fails to conduct active behavior regularly
as expected by the user’s circle [40, 113]. Thus, such psychological commitment is
likely to drive users’ active behavior on an SNS, leading to our third hypothesis:
Hypothesis 3: Normative commitment is positively associated with the overall
active behavior in an SNS.
To develop a rigorous model, we specified various population characteristics as control
variables: (1) age, (2) gender, (3) identity (student or nonstudent), and (4) relationship
status (single, in love, or married). Nosko et al. [77] indicated that users’ age and
relationship status significantly influence their disclosure behavior on Facebook.
Disclosure behavior decreases with age. Older individuals may be more cautious when
behaving online. Individuals who are searching for romantic relationships may use the
online medium as a way to self-present or to advertise themselves to potential dating
partners [77]. Using a combination of data mining and content analysis, Thelwall et al.
[103] found that males and females behave differently in posting comments on MySpace.
USERS’ ACTIVE BEHAVIOR ON SNSS
235
As noted previously, Renren is an SNS that was originally designed mainly for students
in universities, and then expanded to other populations in the society. However, students
who have leisure time may behave more actively than individuals at the workplace.
Therefore, to examine the relationship between the three components of commitment and
overall active behavior of a user on an SNS, we control for these four factors.
Methodology
Survey Administration
We tested our model using data collected from a large sample survey. Our sampling
frame was the users of Renren (http://www.renren.com). Overall, 1,457 participants
completed the questionnaire. A pilot test of thirty subjects suggested that the questionnaire required at least ten minutes for completion. After deleting those questionnaires
with missing values and those completed in less than ten minutes, we were left with
1,242 valid questionnaires. The demographics of the respondents are summarized in
Table 9. We also conducted a difference test between the demographic features of the
current study and the large-scale national survey about SNS users in China [28]. Our
results indicate that the two populations were similar in terms of demographic characteristics. Similar to the results of difference test in the measurement model development study, only the identity feature showed significant difference.
Table 9. Descriptive Statistics of Survey Respondents
Category
Gender
Age
Education
Identity
Relationship
status
Items
Male
Female
≤ 19
20–29
≥ 30
High school or
below
Junior college
Undergraduate
Graduate or
above
Students
Nonstudents
Single
Just in love
Married
Sample (N = 1,242)
CNNIC sample
Frequency Percentage
Percentage
598
644
92
1,072
78
123
48.1
51.9
7.4
86.3
6.3
9.9
53.7
46.3
29.0
56.8
14.2
7.2
362
687
70
29.1
55.3
5.6
37.4
42.7
12.6
586
656
607
440
195
47.2
52.8
48.9
35.4
15.7
Chi-square
differences
χ2(1) = .720
(p = .396)
χ2(2) = 3.524
(p = .061)
χ2(3) = 5.547
(p = .136)
26
χ2(1) = 9.514
(p = .002)
74
There are no data about
participants’ relationship
status in the CNNIC’s survey
in 2012.
236
CHEN, LU, CHAU, AND GUPTA
Measurement
The overall active behavior of a user on an SNS was operationalized as a secondorder construct with four reflective first-order factors (creation, content transmission,
group relationship contribution, individual relationship contribution). All four of
these factors were measured using multiple-item scales obtained from the study of
measurement model development reported in this paper. A seven-point scale ranging
from 1 (never) to 7 (very frequently) was used to rate participants’ frequency of
performing these behaviors. Affective commitment, continuance commitment, and
normative commitment were measured by adapting scales from Li et al. [58] and
Fullerton [36] to fit our research context. The measures used a seven-point scale
ranging from 1 (strongly disagree) to 7 (strongly agree). The items correlated with
these three components of commitment are presented in Table 10.
Results
We adopted structural equation modeling (SEM) to perform data analysis. We used
the two-step approach proposed by Anderson and Gerbing [4], in which first a valid
and reliable measurement is established, followed by testing of the structural model.
Measurement Model
We examined the measurement model for validity and reliability. Reliability was
assessed based on Cronbach’s alpha and composite reliability scores. As shown in
Table 11, Cronbach’s alpha and composite reliability estimates of all constructs
exceeded the recommended threshold value of 0.7, indicating adequate construct
reliability. Convergent validity was confirmed by examining the AVE and the factor
loading of the indicators. A confirmatory factor analysis was employed to compute
the factor loadings. Table 11 shows that the factor loadings range from 0.663 to
Table 10. Measurement Items of Three Components of Commitment
Latent variable
Affective
commitment
Continuance
commitment
Normative
commitment
Items
I feel emotionally attached to Renren.
Renren has a great deal of personal meaning for me.
I feel a strong sense of identification with Renren.
It would be very hard for me to be inactive on Renren right now even if
I wanted to.
My life would be disrupted if I lurked on Renren.
It would be too costly for me to lurk on Renren right now.
I feel obligated to be active on Renren.
Given all the things my friends and we had done for each other on
Renren, I feel I ought to be active on Renren.
I don’t feel it is right to lurk on Renren.
USERS’ ACTIVE BEHAVIOR ON SNSS
237
Table 11. Measurement Model Statistics
Construct
Indicator Factor loading Cronbach’s alpha
Content creation
Content transmission
Group relationship
contribution
Individual relationship
contribution
Affective commitment
Continuance
commitment
Normative commitment
CC1
CC2
CC3
CT1
CT2
CT3
GRC1
GRC2
GRC3
GRC4
GRC5
IRC1
IRC2
IRC3
IRC4
IRC5
AC1
AC2
AC3
CCO1
CCO2
CCO3
NC1
NC2
NC3
.663
.852
.750
.817
.863
.777
.727
.704
.765
.811
.789
.665
.891
.933
.838
.803
.809
.886
.819
.794
.847
.838
.848
.893
.877
CR
AVE
.789
.877
.703
.857
.913
.778
.871
.906
.659
.918
.939
.756
.874
.923
.799
.864
.918
.788
.905
.941
.841
Notes: All the boldface factor loadings are significant at the 0.001 level.
0.933, which are all significant at p = 0.001. The AVE values range from 0.659 to
0.841, thus displaying convergent validity.
Discriminant validity was assessed using three methods. First, as seen in Table 12,
all the diagonal elements in boldface (the square root of the AVE) were found to be
Table 12. Construct Correlations and Discriminant Validity
Constructs
CC
CT
GRC
IRC
AC
CCO
NC
CC
CT
GRC
IRC
AC
CCO
NC
.838
.610
.508
.466
.411
.361
.323
.882
.535
.497
.372
.369
.270
.812
.440
.357
.375
.343
.869
.347
.231
.216
.894
.633
.625
.888
.695
.917
Note: Diagonal elements in boldface are the square root of the average variance extracted.
238
CHEN, LU, CHAU, AND GUPTA
greater than any other corresponding rows or column entries (the interconstruct
correlation coefficients). Second, we also assessed the factor loading and crossloading. The results (Table 13) demonstrate that each indicator had a higher loading
on its intended construct than on any other construct. Third, for each pair of factors,
we compared the χ2 value for the baseline model with the constraint model (by
constraining their correlation as 1). The results of these tests show significant
differences in each case, suggesting that none of the twenty-one pairs of constructs
could be merged as one construct (Table 14). Thus, based on these tests, our model
demonstrates strong discriminant validity.
Because self-reported data from a single source were used, we measured the extent
of common methods bias using two analyses. First, a Harman’s single-factor test was
conducted by entering all constructs in an unrotated principal component analysis
and examining the resultant variance [43]. The results indicated the variance
Table 13. Factor Loading and Cross-Loading
1
CC1
CC2
CC3
CT1
CT2
CT3
GRC1
GRC2
GRC3
GRC4
GRC5
IRC1
IRC2
IRC3
IRC4
IRC5
AC1
AC2
AC3
CCO1
CCO2
CCO3
NC1
NC2
NC3
Eigenvalue
Variance %
Cumulative %
2
3
4
5
6
7
.103
.300
.222
.283
.212
.214
-.006
.181
.184
.290
.205
.700
.861
.882
.851
.827
.103
.123
.222
.080
.051
.091
.025
.108
.066
.341
.165
.129
.222
.231
.269
.714
.760
.786
.715
.780
.193
.189
.172
.116
.109
.114
.125
.130
.149
.159
.120
.159
.119
.123
.162
.050
.066
.039
.050
.103
.094
-.015
.090
.167
.133
.033
.066
.068
.051
.034
.234
.263
.292
.250
.289
.409
.762
.846
.833
.061
.152
.146
.179
.090
.037
.001
.141
.087
.110
.073
.019
.103
.095
.106
.131
.724
.816
.782
.295
.185
.258
.215
.243
.264
.105
.066
.102
.049
.135
.152
.227
.022
.088
.024
.113
.116
.030
.035
.026
.025
.331
.229
.174
.743
.818
.700
.386
.233
.233
.096
.299
.253
.736
.816
.755
.138
.159
.135
.142
.157
.108
.123
.132
.153
.170
.096
.111
.097
.123
.137
.086
.060
.058
.059
.736
.713
.742
.232
.209
.175
.214
.131
.138
.122
.043
.104
.067
.108
.162
.150
.189
.133
.053
.113
.083
.086
.085
.105
.080
3.857
15.427
15.427
3.010
12.041
27.467
2.834
11.337
38.804
2.736
10.945
49.749
2.291
9.163
58.912
1.903
7.612
66.524
1.781
7.124
73.618
USERS’ ACTIVE BEHAVIOR ON SNSS
239
Table 14. Pairwise Discriminant Validity Analyses
Two-factor combinations for constrained
measurement models
Content creation & Content transmission
Content creation & Individual relationship contribution
Content creation & Group relationship contribution
Content creation & Affective commitment
Content creation & Continuance commitment
Content creation & Normative commitment
Content transmission & Individual relationship contribution
Content transmission & Group relationship contribution
Content transmission & Affective commitment
Content transmission & Continuance commitment
Content transmission & Normative commitment
Individual relationship contribution & Group relationship
contribution
Individual relationship contribution & Affective commitment
Individual relationship contribution & Continuance commitment
Individual relationship contribution & Normative commitment
Group relationship contribution & Affective commitment
Group relationship contribution & Continuance commitment
Group relationship contribution & Normative commitment
Affective commitment & Continuance commitment
Affective commitment & Normative commitment
Continuance commitment & Normative commitment
χ2
χ2 difference
1,627.293
1,748.741
1,779.410
1,720.346
1,750.507
1,762.558
1,667.811
1,648.614
1,640.325
1,657.040
1,681.405
1,765.798
26.868***
148.316***
178.985***
119.921***
150.082***
162.133***
67.386***
48.189***
39.9***
56.615***
80.98***
165.373***
1,714.817
1,771.548
1,767.435
1,758.527
1,759.038
1,764.885
1,605.852
1,605.719
1,604.41
114.392***
171.123***
167.01***
158.102***
158.613***
164.46***
5.427*
5.294*
3.985*
Notes: The unconstrained measurement model: χ2 (254) = 1,600.425.
***p ≤ .001; **p ≤ 0.01; *p ≤ 0.05.
explained by the most significant factor is only 15.4 percent. Second, following
Liang et al. [59], we included a common methods factor whose indicators included
all the principal constructs’ indicators and calculated each indicator’s variances
explained by the principal constructs and by the method. The average substantively
explained variance of the indicators is 0.716, and the average method-based variance
was 0.001. In addition, the principal variable loadings were all significant at the
0.001 level, whereas most common methods factor loadings were not significant.
We thus concluded that common methods bias is not likely to be a problem in our
data set.
Structural Model
Following the standard procedure of testing a structural model [4], we obtained the
results and found that all model fit indices (χ2 = 930.195; df = 351; χ2/df = 2.650;
CFI = 0.973; GFI = 0.950; AGFI = 0.938; NFI = 0.957; TLI = 0.969; RMSEA =
240
CHEN, LU, CHAU, AND GUPTA
Affective commitment
R2 = 33%
.402***
Overall active
behavior in an SNS
.240***
Continuance commitment
.832***
CC
.849***
CT
.717***
–.044
GRC
Normative commitment
.644***
–.053
Controls
Age
.007
–.013 –.095
Gender
Identity
IRC
Relationship status
Figure 4. Results of the Structural Model
Notes: *** p < 0.001 (two-tailed); the insignificant (p > 0.05) paths are depicted with a dashed
line.
0.036) were quite satisfactory. Figure 4 depicts the standardized path coefficients and
explains endogenous variables’ variance for the structural model.
As shown in Figure 4, two of the three hypotheses were supported. Specifically,
affective commitment was found to be positively related to the overall active
behavior on an SNS (b = 0.402, p < 0.001), thus supporting H1. Continuance
commitment was also found to have a positive influence on the overall active
behavior on an SNS (b = 0.240, p < 0.001), thus supporting H2. A positive
relationship between normative commitment and the overall active behavior on an
SNS (i.e., H3) was not supported, however. On comparing the path coefficients of
the three components of commitment, we found that affective commitment was the
most important driver of users’ overall active behavior on an SNS. Furthermore, all
four control variables (i.e., age, gender, identity, relationship status) had no significant impact on the dependent variable. Overall, approximately one-third (33 percent)
of the variance of dependent variables was explained by the antecedent variables.
Because initial items were generated using a student sample in the Delphi study,
we also compared the difference in active behaviors between students and nonstudents. We divided the data set into two subgroups according to the respondents’
identity. An independent-sample t-test was conducted to examine whether the means
of active behaviors between the two subsets were significantly different. As shown
in Table 15, only three out of sixteen active behaviors are significantly different
according to users’ identity. These results support our use of a student sample in the
Delphi study.
To examine the different roles of three components of commitment on users’
active behaviors on SNSs, we further tested the paths from the commitments to
each first-order construct of overall active behavior on an SNS. The results are
shown in Figure 5. As expected, affective commitment has significant influence on
all four dimensions of overall active behavior on an SNS. Continuance commitment
has significant effect on three of the four dimensions (i.e., content creation, content
transmission, and group relationship contribution). Normative commitment is
.05026
.05811
.05715
.06643
.06984
.06727
.05194
.06227
.05845
.05883
.06778
.05712
.06463
.05817
.05843
.06453
3.6143
3.7918
3.9573
4.4744
4.6433
4.5717
Standard
error
2.7133
3.8720
3.3123
4.1041
3.8515
3.1717
1.9812
3.4539
3.0051
3.2201
Means of
samples
Note: *p < 0.05, **p < 0.01, ***p < 0.001; ns = nonsignificant.
Posting blogs/articles
Changing/posting current status
Posting photos/videos
Sharing friends’ blogs
Sharing friends’ photos
Sharing or transmitting friends’ statuses
Creating groups or public profiles
Joining groups or public profiles
Creating events and sending invitations
Responding to invitations to participate in events
from friends
Interacting with groups
Searching friends and sending application for
adding friends
Looking at personal information of friends
Visiting friends’ profiles
Looking at news about friends
Looking at photos of friends
Active behaviors
Students (N = 586)
4.2332
4.4939
4.5671
4.5686
3.5460
3.8643
3.0198
3.7655
3.2759
4.0201
3.4634
3.2210
2.0555
3.3354
3.0473
3.3064
.05824
.05526
.05669
.05910
.06346
.05383
.04879
.05314
.05447
.06062
.06173
.06153
.05320
.05932
.05646
.05760
Standard
error
Nonstudents (N = 656)
Means of samples
Table 15. Comparison of Active Behaviors Between Students and Nonstudents
ns
ns
***
ns
ns
ns
–3.180
–.243
.935
.035
***
ns
ns
ns
***
ns
ns
ns
ns
ns
–4.369
1.899
.046
1.164
4.180
1.853
–1.333
1.378
–.518
–1.046
1.891
–.924
Significance
level
Differences
t-value
USERS’ ACTIVE BEHAVIOR ON SNSS
241
242
CHEN, LU, CHAU, AND GUPTA
Affective
Commitment
Affective
.320***
Commitment
.319***
2
2
R = 17%
R = 19%
Continuance
.120***
Commitment
Normative
Content
Continuance
Creation
Commitment
.041
Normative
Commitment
Affective
.159***
-.043
Commitment
.186***
Commitment
Affective
2
R = 17%
.352***
Commitment
Group
Continuance
.166***
Commitment
Normative
Commitment
Content
Transmission
Relationship
Contribution
.108**
2
R = 14%
Individual
Continuance
.008
Commitment
Normative
Relationship
Contribution
0.022
Commitment
Figure 5. Roles of the Three Components of Commitment on Users’ Active Behaviors
Notes: ***p < 0.001 (two-tailed); the insignificant (p > 0.05) paths are depicted by dashed
line.
significantly associated only with group relationship contribution. These results
explain why affective commitment is the most important driver of overall active
behavior of users of an SNS. Continuance commitment plays only a moderate role,
and the role of normative commitment is almost nonsignificant.
Discussion
THE RESULTS OF THIS STUDY PROVIDE SUPPORT TO the measurement model for use in
exploring users’ overall active behavior on an SNS. The research highlights the
driving role of three components of commitment on the overall active behavior in an
SNS. First, affective commitment is the most important driver of users’ overall
active behavior in an SNS. It may be the most important driver because the overall
active behavior in an SNS is a multidimensional construct consisting of four factors,
and affective commitment may correlate strongly with a wider range of these factors.
Another explanation is that affective commitment is generally defined and operationalized more broadly than continuance and normative commitment [69]. When users
want to engage in a course of action in an SNS because of attachment to, or desire
USERS’ ACTIVE BEHAVIOR ON SNSS
243
for, that particular course of action, they are less sensitive to cues that potentially
delimit their behavior. Rather, their mind-set directs attention to the intended outcome and thereby allows them to regulate their behavior to achieve that outcome
[69]. In contrast, when users pursue a course of action to avoid costs, or out of
obligation, they are more sensitive to conditions that define what is required or
expected of them.
Second, as expected, continuance commitment has significant influence on the
overall active behavior in an SNS. This is consistent with the results of prior studies
[58], which indicate that continuance commitment has a significant effect on the
continuance of website use.
Third, contrary to our expectations, the effect of normative commitment on overall
active behavior of a user in an SNS was not significant. This is because normative
commitment significantly influences only one of the four types of active behaviors,
that is, group relationship contribution. Although the theory of social influence
posits that if people around a user expect a particular kind of behavior that is
being engaged in by most people around them, then the user feels obligated to
behave in that manner [46], a few previous studies [3, 53, 58] have indicated that
normative commitment has little effect on human behavior. One possible explanation
for this contradiction is that SNSs are voluntary communities [12], in which users
are free not to participate.
This study further provides adequate evidence for usability and good performance
of the instruments that were developed in the measurement model of this study. All
the results of reliability and validity tests and the fit indices of the measurement
model and the structural model were found to be satisfactory. This suggests that the
final items retained in the measurement model development study can be used in an
instrument for future research.
Another interesting and somewhat suprising result should be specifically noted.
Through the literature review and the Delphi study, we classified overall active
behavior in an SNS into four categories: content creation, content transmission,
relationship building, and relationship maintenance. However, in the measurement
development study, the two dimensions related to relationship contribution—that is,
relationship building and relationship maintenance—were segregated into individual
relationship contribution and group relationship contribution. Three reasons can
explain this inconsistency. First, the two types of relationships are primary to the
relationship system (individual-individual and individual-group), and users behave
differently in these two relationships [22]. In organizational theorists’ multilevel
research, groups are often defined as collectives. A collective is “any interdependent
and goal-directed combination of individuals, groups departments, organizations, or
institutions” [71]. Individuals undertake different practices to deal with collective
relationships as compared to personal relationships [71]. More important, individuals
often belong to multiple teams concurrently, and different groups develop their own
norms of communication [110], thus provoking members to behave differently in
various groups in SNSs. Second, individuals form groups and participate in group
activities in SNSs that fulfill their interests, whereas the relationships among
244
CHEN, LU, CHAU, AND GUPTA
individuals in SNSs are primarily organized around people seeking social support
and forming social capital, not around interests [12]. Third, from the perspective of
SNS operators, there are multiple relationships in SNSs [5], and both individual
relationships and group relationships are important resources for SNSs’ growth and
survival. No matter how users build or maintain relationships, in practice they
contribute relationship resources to the SNS. Because we did not detect the dimensions of individual relationship contribution and group relationship contribution
from the literature review and the Delphi study, we deemed it prudent to identify
the novel dimensions of users’ active behavior in an SNS from the measurement
development study. Future researchers could more deeply examine a user’s individual and group behavior in an SNS.
Contributions to Research
This research offers three primary contributions to the existing body of knowledge
about users’ active behavior in an SNS. First, the results contribute to research on
social media. We identify one of the research opportunities by providing insights
into the complex human behaviors in SNSs. The thirty-four infrastructural items
provide insights into various behaviors that users perform in SNSs, some of which
have been examined in previous studies [35]. And the classification of users’ active
behavior in an SNS (i.e., content creation, content transmission, and individual and
group relationship building and maintenance) gives us new understanding about the
essence and contributions of active users in SNSs.
Second, the current study complements the existing research on cocreation.
Cocreation is the participation of consumers along with producers in the creation
of value in the marketplace. Zwass [114] categorized cocreation as sponsored and
autonomous. Previous research [20, 88] has investigated company-sponsored
cocreation. Our research focuses on a specific form of autonomous cocreation in
the SNS context. Zwass [114] classified cocreation into four entity components
capturing the most salient aspects of cocreation: (1) cocreators; (2) process; (3)
characteristics of the task to be accomplished; and (4) cocreated value received by
cocreating communities or individuals. In our framework, the task is specified as
contributing to content and relationships in SNSs. The cocreators are active users in
SNSs who cocreate through their active behavior, and the cocreated value is
measured by active users’ contibution level. Thus, the results contribute to our
understanding of users’ cocreation pattern in the specific SNS context.
Third, our study contributes to research in social commerce. The proliferation of
social media has transformed consumer behavior. The social feature of social media
can improve customers’ enjoyment experience and improve their decision quality
[42, 60]. In the context of SNSs, the content creation behavior and content transmission behavior contribute to social knowledge, which provides opportunity for
customers’ social learning. And the relationships embedded in the SNSs facilitate
social interaction and provide an environment for social commerce. Within this
USERS’ ACTIVE BEHAVIOR ON SNSS
245
environment, customers can access social knowledge and experiences by interacting
in forums and communities, by browsing ratings and reviews posted by others, or by
considering others’ recommendations [48, 50]. All of these commercial activities can
only be realized by conducting the basic social activities in SNSs. The various active
behaviors identified in this research capture the most important social activities
performed in an SNS that provide fundamental insight into social commerce
behavior.
Another contribution of this paper is the development of a comprehensive measure
of overall active behavior in an SNS. This paper provides a theoretically sound
measurement instrument that addresses the complex characteristics of users’ active
behavior in an SNS. This measurement instrument should be useful for future studies
of SNSs. The existence of a validated baseline measure would be a useful starting
point and enhance the quality of measurement of specific aspects of users’ active
behavior. Of course, the validity of a measure cannot be fully established on the
basis of a single study. Validation of measures is an ongoing process that requires
assessment of measurement properties over a variety of studies in similar and diverse
contexts [29]. Development of an alternative means of assessing users’ active
behavior in an SNS would be useful for future research.
Last, this study contributes by demonstrating that commitment theory is relevant
for understanding the mind-set driving users’ overall active behavior in an SNS. This
commitment perspective has implications for both human behavior and website
management strategies in the SNS domain. First, the study has presented a different
commitment perspective on online behavior research. In contrast to the dominant
paradigms, such as TAM [31] and social capital theory [73], this study is one of the
first to introduce commitment theory to explain users’ overall active behavior in an
SNS. Our commitment-based research model provides a useful framework for future
studies on online human behavior. A comparative study or an integrated study of
commitment theories and models such as TAM and social capital theory could be of
interest to the research community. Second, the driving role of affective and continuance commitment on users’ active behavior in an SNS provides new insights.
This study also shows the different roles of the three components of commitment. It
is important to consider the different dimensions of commitment rather than confining its conceptualization to a unidimensional construct called organizational commitment [47], or just commitment [91]. Our study provides evidence for the
feasibility of investigating commitment as a multidimensional construct in the
SNS context. Future research can apply commitment theory to other aspects of
website management.
Implications for Practice
The results of our study provide practitioners with a set of interesting insights into
enhancing users’ active behavior in an SNS. They also suggest strategies that SNS
providers should focus on to incerease users’ affective and continuance commitment.
246
CHEN, LU, CHAU, AND GUPTA
First, some personalization features can improve affective commitment. To build
social and psychological bonds with an SNS, SNS designers can incorporate features
that increase the sense of “personal care,” “belongingness,” and “community” for
online customers [58]. To enhance emotional attachment, site managers should
encourage users to participate in activities together with their friends and enhance
connectedness among them. To develop these connections, the SNS design could
incorporate one-to-one personal connections between friends enabled by real-time
communication technologies such as instant messaging.
Second, increased investment should lead to a higher continuance commitment
[58], because the cost of lurking on the SNS has increased and the benefits of being
active have also increased. For instance, users may reveal privacy information on the
SNS and spend time to communicate with friends through through the SNS.
Managers can use personalization technology so that users can share personal data
with websites and friends. The SNS design can develop attractive applications or
games to increase users’ time on the website.
The list of thirty-four active behaviors identified in the Delphi study provide useful
information about users’ various behaviors. Given the classification of the active
behaviors based on their contribution to the sites, we recommend that managers
diagnose the weaknesses of their websites and take suitable strategies to improve
users’ specific contributions. Recent research has demonstrated that content and
relationship are the main contributions of SNS users [61]. Therefore, firms must
take these two contributions into account when designing their websites, and
different design principles should be followed to enhance the four aspects of user
contributions (content creation, content transmission, relationship building, and
relationship maintenance). These identified behaviors are essential for the growth
of social relationships, accumulation of user-generated content, and increased
volume of site visits and traffic [21], all of which are imperative for increased
market capitalization and revenues [16].
With the first-order and second-order measurement models, more comprehensive
understanding about the overall active behavior in SNSs will provide managers with
new insights, thus allowing them to develop more precise strategies to facilitate
users’ participation in various activities offered on the sites. Because users behave
differently in contributing to individual and group relationships, managers should
utilize different strategies and design principles for individual applications and group
applications.
Limitations and Future Research
We acknowledge the limitations of our study. First, we did not include SNS
operators as panelists in the Delphi study. Although our academic panelist and active
user panelist structure helped us obtain sufficient perspectives on our study, including SNS operators as panelists might have given us a broader perspective about
users’ active behavior on SNSs.
USERS’ ACTIVE BEHAVIOR ON SNSS
247
Second, as with most other empirical studies, the spectrum of respondents in our
online survey limits generalizability. Even though the samples include a range of
individuals representing different demographic groups of Internet users, the subjects
were all users of Renren.com in China. The differences between Western and nonWestern culture may limit the external validity of the research findings [52]. One
should be cautious in generalizing the model to other types of SNSs and users in
other countries. Besides, the identity feature of the respondents in this study showed
significant difference from the sample of China Internet Network Information Center
(CNNIC) [28]. Although our sample is representative of the entire active population
of Renren users to some extent, the large proportion of student responses probably
generated some bias. Future research can employ data from a broader sample to
observe users’ actual behaviors.
This research focuses only on developing a classification, a measurement model,
and a simple predicting model of users’ overall active behavior on an SNS. Future
research could explore other factors driving users’ overall active behavior and
investigate the outcomes of various active behaviors. Specifically, it would be
interesting to examine the association between social relationship and social content
creation behaviors or the relationship between the four dimensions of active behavior and human well-being.
This research indicates that identifying different kinds of active behaviors is
important. Specifically, diferent kinds of active behaviors may play different roles
in affecting people’s daily lives, in structuring cocreation patterns, and in leading to
social commerce behavior. Future studies might test the roles of the four categories
of active behaviors identified in the Delphi study.
Finally, commitment is simply a psychological mind-set that drives human behavior [69]. We did not investigate the antecedents producing this mind-set. Although
factors driving the three types of commitment have been investigated in other
domains, such as organizations [3], workplaces [69], and marketing channels [53],
it may not be the same situation for this emerging online service. Therefore,
researchers can explore antecedents driving users’ commitment to active behavior
on an SNS.
Conclusion
DESPITE THE IMPORTANT ROLE OF ACTIVE BEHAVIORS IN SNSs, extant research on this topic
has mainly focused on adoption behavior and single components of active behavior.
To further examine this important yet underexplored issue, we conducted a Delphi
study, a measurement development study, and an empirical examination of the
measurement model study. Our classification framework and the first-order and
second-order measurement models were found to be useful in understanding the
overall active behavior of SNS users. The empirical study applied the commitment
theory in the SNS context, thus generating a few interesting insights. The findings
248
CHEN, LU, CHAU, AND GUPTA
have important implications for practice as they elaborate upon managing and
stimulating users’ active behavior on an SNS.
Acknowledgments: This work was supported by grants from the NSFC (71332001) and Prof.
Yaobin Lu is the corresponding author of this paper.
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