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Exploring the Impacts of Virtual Role Identification on Knowledge Sharing in
Virtual Communities: A Perspective of Structural Symbolic Interactionism
Chengxi Zhou
Wuhan University
2020301041053@whu.edu.cn
Yongqiang Sun
Wuhan University
sunyq@whu.edu.cn
Yiwen Zhang
Wuhan University
zhangyw95@whu.edu.cn
Abstract
Knowledge sharing is crucial to the operation and
sustainability of virtual communities. Against this
background, this study aims to investigate whether and
how users’ virtual role identification influences their
knowledge sharing behavior. Theoretical insights from
structural symbolic interactionism and identity
economics are synthesized and used as a basis for
proposing the mechanism by which virtual role
identification influences knowledge sharing behavior.
We collected data to test the research model from 250
community users via an online survey. The results
suggest that virtual role identification can facilitate
users’ knowledge sharing behavior by increasing role
utility and perceived role expectations. The theoretical
contributions and practical implications of this study
are also discussed.
Keywords: Virtual role identification, Virtual
communities, Knowledge sharing, Structural symbolic
interactionism, Identity economics
1. Introduction
Since the long-term growth of virtual communities
depends on the active exchange of knowledge,
understanding the drivers of knowledge sharing
behavior is essential for the sustainable development of
virtual communities. Considering that knowledge
sharing is an individual behavior that occurs within a
group, previous studies have extensively used social
identity theory (SIT)—a key theory describing the
affiliation and behavior of individuals and groups
(Tajfel, 1978) to explain the significant effects of social
identification on knowledge sharing in virtual
communities (Ho, 2015; Kumi & Sabherwal, 2019; Yen,
2016). Their fruitful findings also offer compelling
evidence for the validity and applicability of this theory.
However, it is worth noting that these studies have
been limited to examining the identification of users
from social groups or the community they are in
(Rosendaal & Bijlsma-Frankema, 2015; Wu, 2021). We
assume that in addition to considering the impacts of
use-to-user and user-to-community identification
(Prentice et al., 2019), the significant influence of an
individual’s role identity should also be highlighted
because individuals’ behaviors are closely linked to the
role they play and the behavioral expectations
associated with the role (Anglin et al., 2022). Emphasis
on the mediation role of role identities on individual
behaviors rather than intergroup relations and group
behavior on that (Hogg et al., 1995) facilitates a micro-
oriented understanding of the influence of roles on
behavior at an individual level (Anglin et al., 2022). In
the field of information dissemination, it has been
demonstrated that identifying the user role identification
based on social behavior facilitates the dissemination of
information in a social networking environment(Zhou et
al., 2019).
And, more importantly, this research focuses on the
effects of virtual role identities that users create and
maintain in virtual communities. People tend to express
themselves by combining virtual and real-world
identities as their own alter egos in virtual spaces, which
are neither entirely separate nor identical (Wang &
Evans, 2008). Most current role identification research
focuses on the many roles that people play in real life,
such as volunteers (Behnia, 2021; Xu et al., 2020a),
employees (Wrzesniewski et al., 1997), and
family/work roles (Erdogan et al., 2021; Gao & Zhao,
2014; Yang & Su, 2022), while ignoring the virtual roles
people create in the online environment. Considering
that virtual roles may differ from actual identities and be
more directly tied to users’ behavior in the virtual
community, this paper is dedicated to exploring the
mechanisms by which virtual role recognition
influences knowledge sharing behavior in virtual
communities.
This paper intends to address this research question
from the structural symbolic interactionism (SSI)
perspective. It is a social psychological framework that
focuses on linking social interaction to roles (Stryker,
2007). With role identity, identity salience, and
commitment as its core, SSI explains the impact of role
Proceedings of the 57th Hawaii International Conference on System Sciences | 2024
Page 2404
URI: https://hdl.handle.net/10125/106675
978-0-9981331-7-1
(CC BY-NC-ND 4.0)
identity on social behavior in terms of both identity
salience and identity commitment. SSI emphasizes the
relatively stable structure of social networks constructed
at the level of role interaction and uses role identity to
link the dual characteristics of social structure and
individual self-concept. Thus, this paper employs SSI as
a theoretical foundation to construct a model of virtual
role identification’s influence on knowledge sharing
behavior. Furthermore, our research draws on identity
economics to supplement the model. Identity economics
examines the effect of identity on individual preferences
by incorporating identity into the utility function
(Akerlof & Kranton, 2010). In light of this, we add to
the portrayal of identity salience from the standpoint of
utility.
To summarize, we noticed that many earlier studies
concentrated on the influence of social identification on
virtual community users rather than role identification,
and that academics have given less attention to virtual
roles in network environments than real-life roles. As a
result, this study uses SSI and identity economics as the
theoretical foundation for further investigation into the
process by which virtual role identification encourages
knowledge sharing behavior. In the next section, we
first review role identification, SSI, and identity
economics, then give the study model and hypotheses,
followed by the methodology and data collection
process. Following that, we present the data analysis
results. Finally, the key findings, theoretical and
practical implications, and limitations of the study are
discussed.
2. Literature review
2.1. Role identification
Roles are broadly defined as a set of behavioral
expectations imposed on people according to their
position in a social structure (Anglin et al., 2022).
Individuals play various roles in daily life (Biddle,
1986), which affect how they behave and see
themselves. Role identity refers to the meaning
prescribed to a role by oneself (Burke & Tully, 1977).
Thus role identification is the internalization or self-
definition of the role expectation that individuals
possess (Stryker & Burke, 2000).
The level of role identification may influence
people’s behavior. For instance, employees who
identify their role as a ‘job’ focus on rewards and
necessity. Those who identify it as a ‘career’ focus on
advancement and achievement, and those who describe
a ‘calling’ focus on socially useful work (Wrzesniewski
et al., 1997). Volunteers with high role identification
commit to their volunteer organizations more rapidly
when they have low levels of psychological capital (Xu
et al., 2020b). Strong identification with the family role
drives the acquisition and transfer of valuable resources
from the family role to the work role (Dumas & Stanko,
2017).
Current research on the influence of role
identification on behavior largely focuses on typical
roles in daily life that have behavioral paradigms widely
accepted in society, for example, family (Yang & Su,
2022), volunteers (Xu et al., 2020b), and police officers
(Grawitch et al., 2010). Studies have proved that people
can create their own set of behaviors in the virtual world
by using virtual roles. For example, players adopt the
virtual world for their new social identities and develop
distinctive lifestyles, a strong criterion for explaining
their behavior patterns in the virtual world (Whang &
Chang, 2004). Therefore, the virtual role that people
play in the online environment should be considered
when talking about how people behave online.
Taking into account that an individual’s virtual role
in the online world may differ from their real-life
identity (Huang et al., 2018) and that an individual’s
online behavior takes place virtually, this paper
investigates the impact of virtual role identification on
the knowledge sharing behavior of online community
users. To address this research question, the paper draws
on SSI, the basis of identity theory.
2.2. Structural symbolic interactionism
Structural symbolic interactionism (SSI) is a social
psychological framework developed by Stryker (1980).
Different from the “traditional” symbolic interactionist
framework (Abel et al., 1934; Blumer, 1988), SSI
emphasizes the importance of social structure, which is
defined by the patterned regularities in human
interactions (Stryker, 2007). SSI suggests that social
structure influences, but does not determine, what
humans do. The theory suggests that individual behavior
is dependent on a “classified” world. The symbols used
to describe “social positions” within these categories
develop into comparatively stable components of the
social structure, and these positions carry the role
expectations. People who act within the framework of
the social structure identify and refer to one another as
occupants of particular social positions in the same way.
They also define themselves in terms of the roles they
play. However, these structural criteria do not define
how people behave. Rather, the development – and even
reshaping of behavior – occurs during experimental,
nuanced, and negotiated processes of interaction during
the role-making process.
The core concepts of SSI include self, role identity,
identity salience, and commitment. Self is
conceptualized as composed of a set of discrete
identities, with persons having, potentially, as many
Page 2405
identities as there are organized systems of role
relationships in which they participate. Greater identity
salience is indicated by the increased likelihood that a
particular identity will be invoked or played out in a
variety of situations (Stryker, 2007). Commitment refers
to the “degree to which the individual’s relationships to
particular others are dependent on being a given kind of
person (Stryker & Staham, 1985, p. 345)”, and reflects
the extent to which important others are judged to want
the person to occupy a particular role position (Hogg et
al., 1995). SSI uses identity salience and commitment to
account for the impact of role identities on social
behavior (Anglin et al., 2022).
This research makes the case that virtual role
identification influences online community users’
knowledge sharing behavior through identity salience
and commitment using SSI as a foundation. The study
measures commitment in terms of the degree to which
the individual perceives that other community members
expect the individual to fulfill the behavioral pattern of
the role (knowledge sharing) because other community
members in a virtual community are most intimately
associated with an individual and constitute the most
important social relationships the individual has in the
community. That is, the higher the perceived role
expectations, the higher the individual’s commitment to
that virtual role. The concept of role salience defined in
role theory refers to the importance people attach to
roles (Greer & Egan, 2012), and is used to quantify
identity salience because, according to SSI, a person’s
identity corresponds to the role they play.
2.3. Identity economics
Akerlof and Kranton (2010) brought identity into
the analytical framework of neoclassical economics to
study how identity influences decision-making. They
created a utility function that comprises three elements:
social categories, norms, and utility. Each person has a
particular identity that corresponds to their behavior and
serves as a paradigm. Identity utility refers to the change
in utility that results from the adaptation of individual
behavior to identity norms, and utility maximization is
a general and fundamental process that determines the
subject’s survival (Ferrari-Toniolo et al., 2021). In light
of this, we proposed to measure identity salience from
the perspective of utility, suggesting that the more utility
a role brings to an individual, the higher its salience. We
introduced the variable of role utility into our research
model, combining it with role salience to measure
identity salience. In summary, this paper constructs a
multiple mediating effects model in which virtual
identification influences knowledge sharing behavior
through three mediating variables: role salience, role
utility, and perceived role expectations.
3. Research model and hypothesis
development
Using SSI, this study proposes the mechanism
through which virtual role identification impacts users’
knowledge sharing behavior in the online community.
Specifically, we propose that virtual role identification
affects knowledge sharing through identity salience and
commitment, where identity salience is measured by
role salience and role utility, and commitment is
measured by perceived role expectations. Furthermore,
community identification was included as a control
variable because it has been shown to play a significant
role in affecting knowledge sharing in many studies
(Lee et al., 2014; Lin et al., 2022; Usman & Yennita,
2018). Figure 1 presents the research model.
Figure 1. Research model.
3.1. Virtual role identification and knowledge
sharing
A person’s role identities dictate what they should
do based on the roles they play in society (Stryker, 1987).
Identity theorists conceptualize a role identity as a set of
behavioral tendencies (White et al., 2008), and engaging
in behaviors that are consistent with role identity helps
to confirm and validate one’s status as a member (Hogg
et al., 1995). Thus, this paper argues that greater
individual role identification means a greater likelihood
of knowledge sharing – a behavioral norm associated
with the role of a virtual community member.
H1: Virtual role identification positively affects
knowledge sharing behavior.
3.2. The mediating effects of role salience, role
utility, and perceived role expectations
Role salience is a reflection of the importance and
value that people attribute to the roles central to their
lives and identities. One pivotal aspect of role salience
is an individual’s responsibilities to organizational roles
Page 2406
(Greer & Egan, 2012). It refers to the likelihood that a
person will play a specific role in a variety of contexts,
with the order of probability indicating the salience
level. Stronger role salience means that individuals are
more likely to integrate that identity into their self-
concept, perceive a specific situation as an opportunity
for role performance, and find opportunities to display
that role’s behavioral patterns (Stryker, 1968). For
example, women entrepreneurs who have a high work
salience place more emphasis on work and are more
willing to adjust their responses to family demands to
accommodate their work requirements (Neneh, 2021).
As a result, this study contends that the more important
the virtual role of the online community user is to the
individual, the more likely the individual will view the
community interaction scenario as an opportunity to
exhibit the behavioral patterns of the role, and to share
knowledge to solidify the role.
H2: Role salience mediates the relationship
between virtual role identification and knowledge
sharing behavior.
Economists define personal motivation using the
utility function, and utility maximization is a key
element of many theoretical approaches explaining
human behavior (Howes et al., 2014). Drawing directly
on the social identity approach and self-categorization
theory, Akerlof and Kranton (2010) introduced the
concept of identity into the neoclassical utility-
maximizing framework to create the economics of
identity. They described the gain when a person’s acts
follow the norm – and the loss when they do not – as
identity utility. Based on identity economics and utility
maximization, this research applies role utility to define
the benefits achieved when individual behavior
corresponds to the standards of the virtual role of online
community user (knowledge sharing). We argue that the
more individuals identify with the virtual role of online
community user, the more likely they are to perceive the
benefits of this virtual role for them and to perform the
role better to continue to benefit from the role.
H3: Role utility mediates the relationship between
virtual role identification and knowledge sharing
behavior.
In this study, the concept of perceived role
expectations was used to describe the expectations that
other community members have of individuals who
have taken on the role of community members to share
their knowledge. Identification is a person’s
understanding of who they are and the group to which
they belong, and it influences how they interpret their
role and the expectations that accompany it (Sluss &
Ashforth, 2007). Role expectations refer to people’s
shared understanding of the behavioral expectations
connected to roles (Stryker, 1968), and significantly
affect people’s intents and behavior (Du et al., 2017).
This study argues that users with a stronger sense of role
identification better understand the behavioral norms of
the role (knowledge sharing) and have higher perceived
role expectations, making them more motivated to act.
H4: Perceived role expectations mediate the
relationship between virtual role identification and
knowledge sharing behavior.
4. Methods
4.1. Measurement
A survey was conducted to validate the proposed
hypotheses, and the measures for the constructs were
adapted from well-established scales in prior studies.
Specifically, we assessed virtual role identification
using items from Lewis et al. (2008). Perceived role
expectations were measured with items from Carmeli
and Schaubroeck (2007), and we used the scale from
Eddleston et al. (2006) to measure role salience.
Besides, the sense of identification that community
members develop by being a member of a virtual
community is known as community identification,
which may inspire the members of virtual communities
to engage in constructive behaviors that help the
community grow (Pei et al., 2022). Therefore,
community identification was introduced into the
research model as a control variable. We adapted the
scale from Kalliath et al. (2019) to gauge role utility, and
selected items to measure community identification
from Gusar et al. (2021). Adapted items from Radaelli
et al. (2014) were used to measure knowledge sharing
behavior. All items involved a 5-point Likert scale
ranging from 1=strongly disagree to 5=strongly agree.
4.2. Data collection
The Naodao platform (www.naodao.com) was used
to administer and collect the data for this study. The
platform is a dedicated psychological research platform
for conducting online experiments, with a large
participant base and a wide distribution in terms of
region, age, and education level, which met all the needs
of the study. A pre-test of five undergraduate students
was conducted before the formal survey. The pre-test
participants were invited to evaluate the readability and
clarity of the questionnaire, and the wording and format
were adjusted based on their feedback.
After ensuring that the questionnaire was
understandable and unambiguous, we posted it online.
The survey was aimed at people with experience in
virtual communities where users share information and
knowledge (e.g., Facebook, Twitter, or YouTube),
although the type of community was not limited.
Page 2407
Participants were asked to select the virtual community
that they use most often and then to answer questions
focusing on that community as an example. Each subject
was paid 2 yuan for completing the questionnaire. We
excluded invalid cases with incomplete answers or
incorrect answers for the attention-checking questions,
and finally obtained a total of 250 valid responses, with
a recovery rate of 95.4%.
Of the 250 respondents, almost 23% were male and
77% were female. The majority of respondents were
aged between 18 and 25 years (82.0%). Approximately
75% had a bachelor’s degree. A total of 97.2% of
respondents (n=243) reported experience using online
communities.
4.3. Data analysis
This study used partial least squares (PLS) for data
analysis and model validation. PLS does not require the
same normal distribution assumptions to be used for the
data and is suitable for small to medium-sized samples
(Chang, 2013). Specifically, this study used smartPLS
to validate the research model in two steps (Hair et al.,
2022; Hair et al., 2013). First, the measurement model
was tested to check the reliability and validity of the
model. Second, the structural model was evaluated to
verify the hypotheses in the research model.
4.4. Measurement model
Cronbach’s alpha, Composite Reliability (CR), and
Average Variance Extracted (AVE) are three widely
accepted indicators of reliability that reflect the internal
consistency of each latent variable. As shown in Table
1, the Cronbach’s alpha values for all constructs ranged
from 0.727 to 0.827, above the threshold of 0.7; the CR
values ranged from 0.842 to 0.897, above the threshold
of 0.7; and the AVE values ranged from 0.62 to 0.744,
above the recommended value of 0.5, indicating that the
scales have good reliability.
Next, convergent and discriminant validity were
tested by assessing item loadings and cross-loadings.
Specifically, requirements for convergent validity were
satisfied when the loadings of the indicators on the
relevant constructs were greater than 0.70, and for
discriminant validity when the loadings of the indicators
on the relevant constructs were greater than the loadings
on other constructs. As shown in Table 2, the loadings
of all the indicators on their respective scales were
greater than 0.70 and greater than the loadings on other
constructs. Therefore, the constructs indicated good
convergent and discriminant validity.
In addition, discriminant validity can be assessed by
comparing the correlation coefficients of the variables
with other variables with the square roots of their AVEs.
The square root of AVE for each variable was greater
than its correlations with other variables, indicating
good discriminant validity of the constructs. We
conducted the Harman single-factor test (Podsakoff et
al., 2003) and found that the single component explains
38.1% of the variation, which is less than the 40%
criterion, indicating that the common method bias was
not a serious issue in the data (Tan, 2022).
4.5. Structural model
Figure 2 presents the results of the PLS analysis,
including the path coefficients, t-values, and the overall
theoretical explanatory power of the model. First, the
structural model was assessed using the path structure
coefficients and R2 values, where R2 values indicate the
amount of variance explained in the dependent variable.
Overall, virtual role identification explained 35.4% of
the variance in role salience, 23.9% of the variance in
role utility, and 13.7% of the variance in perceived role
expectation. In turn, the overall model explained 52.5%
of the variance in knowledge sharing behavior.
Next, the strength of the relationship in the
structural model was explored through the path
coefficients. As shown, the path coefficients from
virtual role identification to knowledge sharing behavior
were not significant (β=-0.014, t=0.198), indicating that
H1 is rejected. Virtual role identification had a positive
and significant relationship with role salience (β=0.595,
t=13.096), role utility (β=0.489, t=9.805), and perceived
role expectations (β=0.370, t=5.490). Role utility
(β=0.216, t=3.534) and perceived role expectations
(β=0.417, t=8.137) had a significant positive effect on
knowledge sharing behavior. The path coefficients from
role salience to knowledge sharing behavior were not
significant (β=0.084, t=1.108). The results also implied
that the controlled variable (community identification)
had a significant positive effect on knowledge sharing
behavior (β=0.249, t=3.371).
Figure 2. Research model including the results
of the analysis.
Page 2408
4.6. Testing the multiple mediation effects
To gain insight into the mechanisms by which
virtual role identification influences knowledge sharing
of online community users, this study further tested the
mediating effects of role salience, role utility, and
perceived role expectations. As the model proposed in
this study contains multiple mediating effects, the
evaluation method proposed by Preacher and Hayes
(2008) was used.
Table 1. Reliability and validity.
Cronbach’s alpha
AVE
CR
CI
PRE
KSB
RU
RS
VRI
CI
0.795
0.62
0.867
0.787
PRE
0.827
0.744
0.897
0.465
0.862
KSB
0.809
0.724
0.887
0.588
0.604
0.851
RU
0.802
0.628
0.871
0.578
0.375
0.538
0.792
RS
0.826
0.657
0.884
0.589
0.485
0.521
0.546
0.81
VRI
0.727
0.641
0.842
0.578
0.37
0.423
0.489
0.595
0.801
Notes: AVE=average variance extracted, CR=composite reliability, CI=community identification,
PRE=perceived role expectations, KSB=knowledge sharing behavior, RU=role utility, RS=role
salience, VRI=virtual role identification
Table 2. Descriptive information, factor loadings, and cross-loadings.
Items
Mean(SD)
CI
PRE
KSB
RE
RS
VRI
Community
identification
CI1
3.25(0.933)
0.744
0.471
0.462
0.442
0.448
0.489
CI2
3.94(0.685)
0.777
0.341
0.448
0.473
0.438
0.457
CI3
3.58(0.834)
0.817
0.343
0.451
0.489
0.533
0.48
CI4
3.45(0.816)
0.808
0.312
0.488
0.42
0.439
0.397
Perceived role
expectations
PRE1
2.85(0.990)
0.404
0.868
0.538
0.31
0.411
0.296
PRE2
2.96(0.958)
0.418
0.877
0.499
0.338
0.41
0.318
PRE3
2.36(0.973)
0.383
0.842
0.524
0.32
0.433
0.343
Knowledge
sharing
behavior
KSB1
2.70(1.072)
0.545
0.569
0.875
0.48
0.491
0.4
KSB2
3.32(1.015)
0.481
0.529
0.865
0.448
0.439
0.332
KSB3
3.38(0.929)
0.47
0.433
0.811
0.446
0.393
0.345
Role utility
RU1
3.75(0.747)
0.462
0.26
0.401
0.822
0.451
0.44
RU2
3.71(0.801)
0.447
0.264
0.419
0.765
0.513
0.378
RU3
3.78(0.723)
0.475
0.258
0.358
0.815
0.425
0.399
RU4
4.02(0.694)
0.449
0.396
0.518
0.765
0.346
0.332
Role salience
RS1
3.58(0.809)
0.471
0.438
0.424
0.457
0.817
0.543
RS2
3.46(0.897)
0.466
0.368
0.361
0.417
0.776
0.438
RS3
3.24(0.976)
0.511
0.381
0.424
0.475
0.853
0.486
RS4
3.52(1.072)
0.463
0.381
0.473
0.42
0.794
0.455
Virtual role
identification
VRI1
3.62(0.720)
0.358
0.21
0.242
0.284
0.343
0.732
VRI2
3.63(0.846)
0.507
0.309
0.365
0.377
0.466
0.839
VRI3
3.46(0.901)
0.5
0.344
0.382
0.477
0.576
0.827
Notes: SD = standard deviation, CI=community identification, PRE=perceived role expectations,
KSB=knowledge sharing behavior, RU=role utility, RS=role salience, VRI=virtual role identification
Table 3. Summary of multiple mediation effects tests.
Effects
t
p
Bootstrap BC 95% CI
LLCI(2.5%)
ULCI(97.5%)
Total indirect effect
VI-> KSB
5.118
0.000
0.193
0.407
Specific indirect effects
VI->PRE->KSB
4.060
0.000
0.078
0.204
VI->RU->KSB
3.308
0.001
0.043
0.168
Page 2409
According to Preacher and Hayes (2008), the
investigation of multiple mediations should involve two
parts: the investigation of the total indirect effect, and
the testing of hypotheses about individual mediators in
the context of multiple mediator models. Furthermore, a
significant total indirect effect is not a prerequisite for
investigating specific indirect effects. As a result, in our
multiple mediator model, we used smartPLS and
employed Bootstrapping for assessing total and specific
indirect effects. The bootstrap test indicated when the
path coefficient of a 95% CI did not include 0, the effect
was significant.
As shown in Table 3, the total indirect effect of
virtual role identification on knowledge sharing
behavior was significant (95% Boot CI [0.193, 0.407]).
Further, the first path was significant from virtual role
identification support to knowledge sharing behavior
through the first mediator, role utility (95% Boot CI
[0.043, 0.168]), supporting H3. The second path through
the second mediator of perceived role expectations
showed significance (95% Boot CI [0.078, 0.204]),
supporting H4. However, the mediating effect of role
salience (95% Boot CI [-0.038, 0.142]) was not
significant, rejecting H2.
5. Discussion and implications
To promote the knowledge sharing behavior of
users in virtual communities for community
development, this study explored the effects of virtual
role identification on knowledge sharing behavior in the
virtual community context. We also explored the
influence mechanism of virtual role identification on
knowledge sharing behavior from the perspective of
structural symbolic interactionism. Results showed that
virtual role identification exerts a significant influence
on knowledge sharing by increasing role utility and
perceived role expectations.
5.1. Theoretical contributions
Our results offer several significant theoretical
contributions. First, by examining the influence of
online community users’ virtual role identification on
knowledge sharing behavior, this paper extends the
influence of identification on community users’
knowledge sharing from the level of group identification
to the level of role identification. Much of the literature
on identification as an object of study in the context of
online community knowledge sharing has focused on
whether individuals’ identification with the community
and other members affects intra-community behavior.
However, the effect of individual identification on the
role of community users has been overlooked.
Registering for an account and logging in is a
prerequisite for using an online community, and through
this process, individuals are assigned the role of
community user. Our findings suggest that role
identification increases role utility and perceived role
expectations, in turn promoting knowledge sharing
behavior within the community.
Second, most studies about identification are based
on social identity theory, introducing group identity
(community identity) as one of the influencing factors.
However, this paper focuses on role identification, using
SSI as a theoretical basis to explore the influential
effects of role identification. There is a wealth of
research exploring the impact of role identification on
behavior and individual perceptions, but few studies
have explored the specific mechanisms of influence.
This paper builds on SSI to explain the impact of role
identification on behavior in terms of both identity
salience and commitment. Specifically, from an identity
salience perspective, the stronger an individual’s
identification with a role, the greater the utility to the
individual of performing the behavioral paradigm of that
role. In turn, that role becomes more important in the
individual’s mind, which encourages the user to
continue to act in accordance with the behavioral
paradigm of that role. From a commitment perspective,
the more an individual’s relationship with significant
others depends on a role, the more likely the individual
is to remain in that role. In this paper, commitment was
represented in terms of the expectations of individuals
by other community members. Expectations were found
to play a mediating role between role identification and
knowledge sharing behavior. Our study extends the
application of SSI to the field of information behavior.
In addition, this paper enriches the research model
with reference to identity economics, introducing
identity into the utility function to explain the effect of
identity on individual preferences (Akerlof & Kranton,
2000). In light of this, this paper introduces the variable
of role utility to represent the benefits that a role brings
to individuals, and presents identity salience from a
utility perspective. The results show that role utility
mediates between role identity and knowledge sharing
behavior.
5.2. Practical implications
Our study offers several practical insights for
community builders. First, community designers need to
be aware of the importance of constructing and
improving the role of community members. For
example, Zhihu, a high-quality Chinese Internet Q&A
community, previously used “Share your knowledge,
experience and insights with the world” as its slogan.
The slogan directly defined the role of the “Zhihu user”
from a behavioral paradigm perspective, based on the
Page 2410
main functions and features of the community. To
improve users’ role identification, community builders
should clearly define the role of the community user and
clarify and encourage the behavioral paradigm of the
role, for example, by emphasizing it in the brand slogan,
and promoting the content produced by quality users in
the community.
Second, community builders should take care to
emphasize the utility of the role of the community user,
thereby increasing the importance of that role in the
minds of individuals and encouraging knowledge
sharing behavior by users. When community users
experience more positive emotions and a greater sense
of accomplishment through their roles as individuals,
the quality of the content disseminated by the
community is likely to improve. Further, the actual
utility that the individual perceives from the role is
likely to increase, thereby inclining individuals to
engage more in the role behaviors, i.e., to share
knowledge and continue to benefit from the role.
Community builders can increase role utility by
providing incentives that give users a continuing sense
of accomplishment and positive emotions, and by
disseminating high-quality content to solve problems or
provide help to users.
It is recommended that community builders
improve the ease of interaction within the community,
promote an optimal atmosphere for discussion, actively
guide users to communicate with each other in depth,
and allow users to fully feel the expectations of other
community members, ultimately promoting knowledge
sharing among members. For example, the convenience
and ease of use of questioning, answering, commenting,
and retweeting in the community should be optimized,
and functions such as private messaging, group chat,
adding friends, and following should be further
improved, thereby increasing users’ willingness to
participate in information interaction activities.
5.3. Limitations and future research
The study had some limitations. First, we focused
only on the mediating effects of role utility and
perceived role expectations, although role identification
may also influence knowledge sharing behavior through
other factors. Future research could build on other
theories to determine more mediating effects and further
enrich the influence mechanisms of role identification.
Second, research on virtual community users’ role
identification is still developing, and scales for
measuring role identification are limited. Future
research needs to further develop and validate relevant
scales. Third, this study did not restrict the types of
communities used by the respondents, thus ignoring the
influence of the characteristics of each type of
community itself on the users, and to what extent the
culture of a specific online community interacts with
user identity and utility. The applicability of the findings
needs to be further validated by narrowing the scope of
the study in the future. Finally, this study recorded a
response rate of 77% female and 23% male, while
ignoring any gender disparities. Future research should
look into the gender breakdown of online community
membership.
6. Acknowledgement
The work described in this paper was partially supported
by the grants from the Major Projects of Ministry of
Education Humanities and Social Sciences Key
Research Base (Project No. 22JJD870002), and the
National Natural Science Foundation of China (Project
No. 71974148, 71904149, 71921002).
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