Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
A Model of Consumers’ Knowledge Sharing Motivation in the
Online Travel Communities
Junhwa Jang*, Sunghyup Hyun**, Insin Kim*** and Kwangho Lee ****
As participation in online travel community becomes popular, it is important to
know how to encourage individuals to contribute actively and assist other
members share their expertise. To employ valid respondents for this study, a
convenience sample of online travel community members was obtained using
Amazon Mechanical Turk (https://www.mturk.com/mturk/welcome). The study
hypotheses that there are the relationships between knowledge sharing
motivations, knowledge contribution (KNO), knowledge-sharing continuance
intention (COI), and community promotion (COP); and suggests the
hypotheses that the moderating effect of positive group perception exists in the
relationships between KNO and COI and between KNO and COP, respectively.
The results provide support for all hypotheses except for the two about the
relationship between the expected relationship of knowledge sharing
motivation and KNO and the moderating effect of PGP on the KNO-COP
relationship; have important implications; and offer interesting avenues for
future research.
Field of Research: Hospitality and Tourism Industry Management
1. Introduction
As a significant source of knowledge, online communities have stimulated interest in
knowledge management research with regard to their survival and longevity (Chalkiti and
Sigala, 2008; Gu et al., 2007) in the proliferation of online communities of practice (e.g., Wasko
and Faraj, 2005; Chang and Chuang, 2011). In the fields of psychology, marketing, and
communication, A number of studies have pointed out the knowledge supply-side question of
how to persuade individuals to contribute their knowledge with other colleagues or members
in an organization or an online community (e.g., Huang, Basu, and Hsu, 2010; Kwok and Gao,
2004; Wasko and Faraj, 2005). Meanwhile, most studies to date have investigated the
demand-side knowledge perspective such as knowledge acquisition in online travel
communities (Lin, Hung, and Chen, 2009). This phenomenon may reveal a need to focus on
the cognitive process of knowledge sharing from a supplier perspective in online travel
communities. Therefore, there is a critical need to examine why knowledge providers (supplyside knowledge sharing) share the specialized travel-related knowledge in online travel
communities (Huang, Basu, and Hsu, 2010; Yu and Chu, 2007).
Among various types of cognitive factors, individuals’ motivations on knowledge sharing such
as such as altruism, expected reciprocal benefit, trust, reputation, and expected relationship
have been acknowledged as salient antecedents of knowledge contribution (Fang and Chiu,
*Ms. Junhwa Jang, School of Tourism, Hanyang University, Seoul, Korea. Email: junbstar@yahoo.ca
**Dr. Sunghyup Hyun, School of Tourism, Hanyang University, Seoul, Korea. Email: sshyun@hanyang.ac.kr
***Dr. Insin Kim, Department of Tourism and Convention, Pusan National University, Busan, Korea. Email:
insinkim@pusan.ac.kr
****Dr. Kwangho Lee, Department of Family and Consumer Sciences, Ball State University, Muncie, USA
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
2010), which in turn influence the consequences of knowledge-sharing continuance intentions
and community promotion in online communities (Chen and Hung, 2010). In a vein, an
understanding of members’ perceptions towards community (group) climate is also significant
for detecting if it stimulates the relationship between knowledge contribution and behavioural
consequences (for example, see Boer and Berends, 2003). Thus, identifying the knowledge
suppliers’ cognitive factors affecting knowledge sharing behaviors may enable researchers
and practitioners to understand the cognitive process of knowledge sharing behaviors in the
context of online travel communities.
Given the literature above-mentioned, the primary purpose of this study is to examine the
structural relationships between the five factors of knowledge sharing motivation (altruism,
expected reciprocal benefit, trust, reputation, and expected relationship), knowledge
contribution, knowledge-sharing continuance intentions, and community promotion. The
results have important theoretical implications for future research on sustaining online travel
communities. Additionally, by testing the moderating effect of positive group perception, the
study offers new insights into how those with high positive group perception strengthen both
the effect of knowledge contribution on knowledge-sharing continuance intentions and the
effect of knowledge contribution on community promotion. Consequently, the results are
expected to help researchers and practitioners of interactive marketing recognize the
operational strategies of online travel communities in a long-term perspective.
2. Literature Review
2.1. Knowledge Sharing Motivation
The Social Capital Theory, which encompasses the expected collective benefits derived from
the preferential cooperation between individuals, has promoted researchers to examine a
model for exploring individuals’ motivations on knowledge sharing in online (or virtual)
communities (Chiu et al., 2006). Online communities are utilized as resources to serve the
needs of members for communication, information, and knowledge sharing (Lin, Hung, &
Chen, 2009). Among these, the function of online communities in favor of knowledge sharing
with other members calls for understanding types of motivations on knowledge sharing in
online communities (Chiu et al., 2006; Fang & Chiu, 2010; Wasko & Faraj, 2005). This may.
According to Chang and Chuang (2011), knowledge sharing motivations can be developed
primarily based on three significant dimensions such as structural dimensions (e.g., trust),
relational dimensions (i.e., relationship and reciprocity), and individual dimensions (i.e.,
reputation and altruism). Therefore, knowledge sharing motivation in online travel communities
can be divided into five dimensions such as altruism, expected reciprocal benefit, trust,
reputation, and expected relationship in determining knowledge contribution.
Altruism is defined as the degree to which individuals take spontaneous helping actions without
apparent compensation, which leads to either for the direct benefit of other individuals or
indirectly to the community (Batson et al. 2002; Hsu & Lin, 2008; Lin, Wub, & Lu, 2012). Given
this, online community members tend to contribute their knowledge and experience to helping
others at their own time and effort. Altruism is also applied for the context of online communities
such that online users may contribute to sharing certain information in a volunteer participation
(Palmer, 1991). In this regard, Kwok and Gao (2004) highlighted altruism as one motivator for
knowledge contribution in peer-to-peer (P2P) online communities. Furthermore, some
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
scholars have pointed out altruism as a driver for knowledge-sharing behavior (Ba, Stallaert,
& Whinston, 2001; Fang & Chiu, 2010).
According to the social exchange theory, the norm of reciprocity could represent the motivation
and commitment of community members to sharing knowledge (Hall, 2001). In general,
reciprocity calls for mutual reinforcement and help by the two sides in benefits of a mutual
exchange (Miller & Kean, 1997). Due to the phenomenon of individuals’ contributions to an
online community in the knowledge sharing process, reciprocity has been highlighted as a
critical factor affecting knowledge sharing (Chiu et al., 2006; Huber, 2001; Lin, Hung, & Chen,
2009; Wasko & Faraj, 2005). As one type of knowledge sharing motivations, expected
reciprocal benefit which is regarded as “the degree to which a person believes he or she could
obtain mutual benefits through knowledge sharing” (Hsu & Lin, 2008), and it will influence
knowledge contribution to online travel communities (Chiu et al., 2006).
Trust is defined as the degree of belief in good intentions, behaviors, competence, and
reliability of members with respect to sharing knowledge in online communities (Lee & Choi,
2003). As a marketing concept, trust is particularly important in online markets to facilitate the
transfer of sensitive consumer information to onlineretailers. An empirical study pointed out
the concept of trust to measure if an individual tended to believe in others and in their shared
information on social networking sites (Hsu & Lin, 2008). In this regard, it has been posited
that trust is as one of the positive and significant factors on online user’s decision making in
information transaction (Kim et al., 2007). Other studies have also addressed that trust played
a significant role in encouraging members to share knowledge and information in an online
environment (Chai & Kim, 2010; Chiu et al., 2006; Hsu et al., 2007).
Reputation refers to “the degree to which a person believed that participation could enhance
personal reputation through knowledge sharing” (Hsu & Lin, 2008, p. 68). Some studies stated
that individuals may actively contribute to virtual communities with knowledge sharing so that
they can receive many attentions (i.e., reputation) from community members and advertising
companies (e.g., Lin et al., 2009). Despite the significant role of reputation in online
contributions (Hsu & Lin, 2008), there has been a paucity of research on personal reputation
in online communities, particularly in travel-related knowledge sharing. According to Kollock
(1999), individuals’ efforts to share high quality information and to help others with impressive
sharing information stemmed from the desire for acquiring prestige from others in a
community. This may indicate that individuals are willing to contribute their time and effort to
online communities in travel-related knowledge sharing. Furthermore, empirical evidence that
reputation positively influenced behavioural attitudes toward online social sites (blog) may
reveal the positive effect of reputation on knowledge sharing contribution (Hsu & Lin, 2008).
Expected relationship refers to “the degree to which a person believed he or she could obtain
an improved mutual relationship through knowledge sharing” (Hsu & Lin, 2008, p. 68).
Expected relationship is derived from the concept of identification. Identification has been used
as an index for measuring the degree of mutual relationships between individuals in online
communities. In this regard, maintaining good relationship with other members (i.e.,
identification) should be an essential motivation for knowledge sharing in an online community
(Chiu et al., 2006). According to Ma and Agarwal (2007), knowledge contribution to the
community enables members to maintain a better relationship. Therefore, it is assumed that
expected relationship may enhance the level of knowledge contribution.
Given the literature above-mentioned, the following hypotheses are proposed:
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
Hypothesis 1: Altruism will have a positive effect on knowledge contribution in an online travel
community.
Hypothesis 2: Expected reciprocity benefit will have a positive effect on knowledge contribution
in an online travel community.
Hypothesis 3: Trust will have a positive effect on knowledge contribution in an online travel
community.
Hypothesis 4: Reputation will have a positive effect on knowledge contribution in an online
travel community.
Hypothesis 5: Expected relationship will have a positive effect on knowledge contribution in an
online travel community.
2.2. Knowledge Contribution, Continuous Intention and Community
Promotion
Knowledge contribution refers to “individuals do engage in pro-social behaviors in online
communities” (Ma & Agarwal, 2007 p. 43.). As a significant consequence of knowledge sharing
motivations (Wasko & Faraj, 2005), knowledge contribution can be used for an antecedent of
knowledge-sharing continuance intention and community promotion. Knowledge-sharing
continuance intention is referred to as “the subjective probability that an individual will continue
sharing knowledge in the future” (Fang & Chiu, 2010, p. 236) and it is regarded as a key
dependent variable in the realm of online communities. Community promotion indicates the
extent to which a member promotes online communities to introduce my peers or friends to
our virtual community and invite my close acquaintances to join our virtual community (Chen
& Hung, 2010). With regard to the relationships among knowledge contribution, continuous
intention, and community promotion, those who actively participated in more knowledgesharing activities in online communities were more likely to promote online communities or
invite new potential knowledge contributors (Koh & Kim, 2004; Lin et al., 2009), which indicates
the positive effect of knowledge contribution on knowledge-sharing continuance intentions and
community promotion. Based on the literature in computer-mediated communication fields, it
is expected that there are significant relationships among knowledge contribution, knowledgesharing continuance intention, and community promotion in the context of online travel
communities. In this regard, the following hypotheses are suggested:
Hypothesis 6: Knowledge contribution will have a positive effect on continuous intention in an
online travel community.
Hypothesis 7: Knowledge contribution will have a positive effect on community promotion in
online travel community.
Hypothesis 8: Continuous intention will have a positive effect on community promotion in online
travel community.
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
2.3. Moderating Role of Positive Group Perception
Positive group perception refers to “overall interaction patterns among members and the
atmosphere that characterizes interactions within the group” (Choi, Rpice, & Vinokur, 2003, p.
4). Positive group perception is a component of ambient stimuli (Choi et al., 2003), indicating
how individuals perceive the climate of an organization. It is posited that members’ perception
of group climate played an important role in their interactions and behaviors such that
individuals’ behaviors may vary depending on the group environment based on their
interactions within a certain group (Anderson & West, 1998). In a vein, behavioural research
concluded that employees’ willingness to share their knowledge could be influenced by their
perceived relationships with other colleagues in an organization (Boer & Berends, 2003). In
addition, Choi et al. (2003) stated that a positive perception of the group may enable members
to perceive the group setting as a psychologically safe environment for exploring and practicing
new skills. In this regard, it is assumed that if an individual would perceive a friendly climate
within a community, he or she may keep contributing sharing of relevant knowledge or attempt
to promote the organization (community). Based on the literature on the role of positive group
perception in an organization, the following hypotheses are proposed:
Hypothesis 9a: Positive group perception will moderate the relationship between knowledge
contribution and continuous intention in an online travel community.
Hypothesis 9b: Positive group perception will moderate the relationship between knowledge
contribution and community promotion in an online travel community.
3. The Methodology and Model
3.1. Procedures
The data collection procedure focused on U.S. consumers who were members of online travel
communities. Data were collected over a period of one month in April 2014. A convenience
sample of online travel community members was obtained using Amazon Mechanical Turk
(https://www.mturk.com/mturk/welcome), which was used to employ valid respondents
because of its dominant advantage in assessing frequent online users. To ensure the
appropriateness of respondents, the following item was employed at the beginning of the
online questionnaire: “Online travel communities (e.g., Tripadvisor.com, lonelyplanet.com, and
virtualtourist.com) represent travel review sites that offer a great opportunity for travel
searchers to find out what other people think about potential travel products (e.g., destinations)
and facilities (e.g., hotels, restaurants, and attractions).” In the online questionnaire, a
screening question ("Are you a member of an online travel community site?") was included to
exclude unqualified respondents, that is, those who were never members of any online travel
communities (e.g., TripAdvisor.com, Travelblog.org, and other travel blogs.). Those who
answered “yes” (n=441) were invited to continue the online survey, whereas those who chose
“no” (n=57) were told to stop. As a result, a total of 441 responses were obtained. Among
these, 31 were eliminated because of incomplete responses, resulting in a usable sample of
410 responses.
3.2. Data Analysis
The present study utilized a two-step approach for empirical analysis based on the
recommendation by Anderson and Gerbing (1998). The first step involves the analysis of the
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
measurement model, while the second step tests the structural relationships among latent
constructs. The aim of the two-step approach is to assess the reliability and validity of the
measures before their use in the full model. Confirmatory factor analysis (CFA) using the
maximum likelihood approach was implemented to assess the construct validity of the eight
scales (altruism, expected reciprocal benefit, reputation, trust, expected relationship,
knowledge contribution, continuous intention, and community promotion) with AMOS 21.0.
Each item was modeled as a reflective indicator of its latent construct. The ten constructs were
allowed to co-vary freely in the CFA model.
3.3. Measurement Instrument
All measurement items were adapted from previous studies. A seven-point Likert-type scale
ranging from "strongly disagree" (1) to "strongly agree" (7) was used for all measurement items
except for positive group perception. Knowledge sharing motivation was measured using 16
items adapted from Hsu and Lin (2008) to assess the extent to which a respondent shared
travel-related knowledge for altruism, expected reciprocal benefit, reputation, trust, and
expected relationship. Knowledge contribution was measured using 5 items from Ma and
Agarwal (2007). Here the respondents were asked to reveal the level of knowledge
contribution to online travel communities. Knowledge-sharing continuance intentions were
assessed using 3 items based on Fang and Chiu (2010). Positive group perception, the
moderating variable, was measured using 5 items adapted from Choi et al. (2003). Responses
were measured on a seven-point Likert-type scale ranging from "not at all" (1) to "very much"
(7). The instrument included items for demographic information such as gender, the education
level, work status, and household income. Before the actual survey, a pilot test was conducted
to determine the validity of the instrument. The respondents were asked to evaluate the
relevance of each item for members of online travel communities. Based on the feedback,
some changes were made to the questionnaire.
4. The findings
4.1. Profile of Respondents
Table 1 represents the profile of respondents. With regard to gender, about 49% and about
51% of the respondents were male and female. A majority of the respondents were in their
twenties (46.6%) and thirties (36.3%). The respondents had a high level of education, with
42.7% obtaining a bachelor’s degree. About 42%, 23%, and 18% of the respondents indicated
their household income to be $25,000-$54,999 (42.2%), $55,000-$84,999 (22.7%), and less
than $24,999 (18.0%), respectively. In terms of employment, a majority of the respondents
were full-time (58.8%) or part-time (16.1%) employees.
4.2 Online Travel Communities Used by the Respondents
The frequency of virtual travel communities used by respondents is depicted in Table 2.
Respondents were asked to answer questions about the online travel community where they
have been members. The results showed that “Tripadvisor.com (59.5%)” was the most
prominently displayed online travel community, followed by “Lonelyplanet.com (23.0%)”,
“Travelblog.org (3.2%)”, “Travbuddy.com (2.7%)”, “Travellerspoint.com (1.2%)”,
“Couchsurfing.org (1.0%)”, etc. In addition, others included the online travel communities of
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
“Budgettravel.com”, “Roadjunky.com”, “Stay.com”, and “Travelchannel.com”. Others include
Budgettravel.com, Roadjunky.com, Stay.com, Travelchannel.com, etc.
Table 1. Respondent profile (N=410)
Demographic Variables
Gender
Age
Education
Household income
Work status
Frequency (n)
Ratio (%)
Male
202
49.3
Female
208
50.7
20-29
191
46.6
30-39
149
36.3
40-49
42
10.2
50-59
19
4.6
60 and over
9
2.2
A high school degree or less
88
21.5
An associate degree
99
24.1
A bachelor's degree
175
42.7
A graduate degree
31
7.6
A professional degree
17
4.1
Less than $24,999
70
17.1
$25,000 - $54,999
173
42.2
$55,000 - $84,999
93
22.7
$85,000 or more
74
18.0
Student
36
8.8
Full-time employee
241
58.8
Part-time employee
66
16.1
Unemployed
26
6.3
Parent/homemaker
29
7.1
Retired
5
1.2
Other
7
1.7
Table 2. The frequency of online travel communities used by respondents
Sites
Tripadvisor.com
Lonelyplanet.com
Travelblog.org
Travbuddy.com
Travellerspoint.com
Couchsurfing.org
Cruisecritic.com
Everytrail.com
Matadornetwork.com
Virtualtourist.com
Yelp.com
Frommers.com
Skift.com
Travelzoo.com
Trippy.com
Barclaycardtravel.com
Others
Frequency(%)
241
93
13
11
5
4
3
3
3
3
3
2
2
2
2
2
13
Ratio(%)
59.5
23.0
3.2
2.7
1.2
1.0
0.7
0.7
0.7
0.7
0.7
0.5
0.5
0.5
0.5
0.5
3.2
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
Total
405
100
4.3. Confirmatory Factor Analysis (CFA)
A CFA using the maximum likelihood estimation method with 410 cases was conducted to
assess the underlying structure of all measurement variables in the model, including their
unidimensionality, construct validity, and reliability. The results reveal a satisfactory fit to the
data (Anderson & Gerbing, 1988): χ2= 765.033, df=296, χ2/df= 2.585, Comparative Fit Index
(CFI)= .948, Normed Fit Index (NFI)= .918, Incremental Fit Index (IFI)= .948, Root Mean
Square Error of Approximation(RMSEA)= .062. The composite reliability (CR) of study
constructs (the internal consistency of multiple indicators for each construct) ranged from .81
to .93, exceeding the recommended threshold of 0.70 (Hair et al., 1998). In terms of the
discriminant validity of major constructs, the average variance extracted (AVE) was calculated
for the measures. All AVE values except for OTS-SEL ranged from .59 to .80, exceeded the
recommended threshold of .50 (Fornell & Larcker, 1981). The square root of the AVE for each
construct exceeded the correlation between the construct and all other constructs and ranged
from .18 to .52, suggesting sufficient discriminant validity (Fornell & Larcker, 1981).
4.4 Hypotheses Tests from H1 to H8
The full structural model was tested to verify the relationships between ATL, ERB, REP, TRU,
EXR, KNC, COI, and COP. Table 3 shows the model’s overall fit to the data based on various
goodness-of-fit measures. Overall, the results show a reasonable fit (Anderson & Gerbing,
1988): χ2= 886.491, df=306, χ2/df= 2.897, CFI= 0.936, NFI= 0.906, IFI= 0.936, RMSEA=
0.068. As expected, the results provide support for all hypotheses except for the relationship
between EXR and KNC. More specifically, ALT (β = .220, t = 2.339, p < .05), ERB (β = .244, t
= 2.931, p < .01), REP (β = .111, t = 2.136, p < .05), and TRU (β = .335, t = 6.435, p < .01)
had significant positive effects on KNC. KNC had a significant positive effect on COI (β = .699,
t = 14.477, p < .01) and COP (β = .406, t = 6.388, p < .01) respectively. COI had a significant
positive effect on COP (β = .348, t = 5.562, p < .01). Consequently, hypotheses H1 to H8 were
supported except for hypothesis 5.
4.5. Multi-Group Test of Moderator
Positive group perception (PGP) was hypothesized to moderate the relationship between KNC
and COI and between KNC and COP. To test these, the respondents were assigned to two
subgroups based on the mean value for the five items of the PGP scale by taking a cluster
analysis approach (Wang et al., 2012). The cluster solution yielded two subgroups: 230
respondents with high PGP and 180 with low PGP. With these two subgroups, a multiplegroup analysis was conducted to verify the moderating effect. The structural model was tested
using free parameter estimates (χ2= 1373.043, df=612), and a model with an equality
constraint (χ2= 1377.522, df=623) imposed on the path between KNC and COI was tested at
the same time. A poor fit indicated by a high chi-square value was assumed to verify a
significant difference between models for the high- vs. low-PGP groups. The moderating effect
of PGP was significant (Δχ2=4.479, Δdf=1). The result indicated that the estimated path
coefficient from KNC to COI was higher in the high-PGP group (β=.574, t=7.772, p <. 01) than
in the low-PGP group (β=.522, t= 6.135, p < .01). On the other hand, the relationship between
KNC and COP was not moderated by the high- vs. low-PGP groups because of the low chisquare value (< 3.8) between models for the high- vs. low-PGP groups. Consequently, the
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
direct effect of KNC on COI was more evident for users of online travel communities in the
high-PGP group.
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
Table 3. A confirmatory factor analysis
Constructs
Items
(Cronbach’s)
Standardized
t-value
estimate
ALT1. I like helping other people.
.647
Altruism
ALT2. Writing and commenting on online travel communities can help
.842
(.806)
others with similar problems.
ALT3. I enjoy helping others through online travel communities.
.797
ERB1. I find that writing and commenting on online travel communities
.895
can be mutually helpful.
Expected
reciprocal
ERB2.I find my participation in online travel communities can be
.871
benefit
advantageous to me and other members.
(.913)
ERB3.I think that participating in online travel communities can improve
.881
reciprocal benefit.
REP1. I earn respect from others by participating in online travel
.783
communities.
Reputation
REP2. Participating in community activity would enhance my personal
.900
(.900)
reputation in online travel communities.
REP3. Participating in an online travel community would improve my
.927
status in the online travel community.
TRU1. I would trust community members to do the work right even if not
.742
monitored.
Trust
(.864)
TRU2. I trust member’s information to be true.
.861
TRU3. Members are trustworthy.
.896
EXR1. Sharing my knowledge would strengthen the tie between other
.835
members and me.
EXR2. Sharing my knowledge would create new relationships with new
.843
Expected
friends.
relationships
EXR3. My knowledge sharing would expand the scope of my
(.911)
.864
association with other users
EXR4. My knowledge sharing would create strong relationships
.856
with members who have common travel interests
KNC1. I contribute my knowledge to other online travel communities.
.850
KNC2. I take an active part in the community.
.873
KNC3. I try to share my unique knowledge with other online
Knowledge
.818
travel communities in more effective ways.
contribution
KNC4. I have contributed knowledge to other members that resulted in
(.916)
.869
their development of new insights.
KNC5. I usually actively share my knowledge with other online travel
.745
communities.
COI1. If I can, I would like to continue sharing knowledge with others at
.898
Knowledge- online travel communities in the future.
sharing
COI2. It is likely that I will continue sharing knowledge with others at
continuance
.938
online travel communities in the future.
intention
COI3. I expect to continue sharing knowledge with others at online travel
(.917)
.841
communities in the future.
COP1. I would like to recommend the online travel community to others. .864
Community
COP2. I would like to introduce the online travel community to others. .915
promotion
COP3. I will continuously talk to others about benefits of the online travel
(.843)
.681
community.
fixed
13.844**
13.338**
fixed
24.796**
25.392**
fixed
20.374**
20.900**
fixed
17.165**
17.641**
fixed
20.705**
21.496**
21.201**
fixed
22.838**
20.483**
22.673**
17.734**
fixed
29.456**
23.690**
fixed
22.556**
15.421**
Note: X2= 765.033, df=296, X2/df= 2.585, CFI= .948, NFI= .918, IFI= .948, RMSEA= .062
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
Table 4. Correlations, reliability, and validity for salient constructs
Constructs 1
2
3
4
5
6
7
8
1. ALT
1
2. ERB
.720**(.52) 1
3. REP
.497**(.25) .526**(.28) 1
4. TRU
.511**(.26) .521**(.27) .652**(.42) 1
5. EXR
.581**(.34) .599**(.36) .677**(.46) .538**(.29) 1
6. KNC
.594**(.35) .628**(.39) .563**(.32) .613**(.38) .584**(.34) 1
7. COI
.632**(.40) .619**(.38) .428**(.18) .528**(.28) .447**(.20) .628**(.39) 1
8. COP
.480**(.23) .558**(.31) .535**(.29) .425**(.18) .511**(.26) .556**(.31) .542**(.29) 1
Mean
SD
AVE
CR
5.89
0.86
0.59
0.81
5.81
0.92
0.78
0.91
5.06
0.98
0.76
0.90
5.33
0.97
0.70
0.87
5.20
1.03
0.72
0.91
5.71
0.86
0.72
0.93
6.02
0.95
0.80
0.92
5.41
1.09
0.68
0.86
Note: **P < .01. a values in parentheses indicate the square of correlations for each construct.
5. Summary and Conclusions
The current study has contributed to both theory and practice. The present study extends the
extant knowledge management literature based on interactive marketing research,
emphasizing the knowledge supply-side perspective within the context of online travel
communities. To reach the proposed hypotheses, the current study examined the structural
relationships among the five factors of knowledge sharing motivation, knowledge contribution,
knowledge-sharing continuance intention, and community promotion. In addition to this,
positive group perception was utilized as a moderator in the relationship between knowledge
contribution and knowledge-sharing continuance intention, while not in the relationship
between knowledge contribution and community promotion.
Specifically, the results showed that there were significant relationships between the five
factors of knowledge sharing motivation and knowledge contribution, except for the
relationship between expected relationship and knowledge contribution. This is in line with the
view interpreted by Chang and Chuang (2011), Chiu et al. (2006), and Wasko and Faraj (2005).
It was also found that knowledge contribution had positive effects on knowledge-sharing
continuance intention, and community promotion, respectively, and knowledge-sharing
continuance intention had a positive effect on community promotion. These findings are in line
with the view suggested by Koh and Kim (2004) and Lin et al. (2009). Additionally, positive
group perception was found to play a moderating role in the relationship both between
knowledge contribution and knowledge-sharing continuance intentions. This is in line with the
view interpreted by Anderson and West (1998) and Choi et al. (2003).
Given these significant results, the current study offers several practical implications. A
practical implication is based on the findings that the knowledge suppliers’ motivation factors
such as altruism, expected reciprocal benefit, trust, and reputation significantly and positively
influence knowledge contribution, knowledge-sharing continuance intention, and community
promotion. In this regard, a majority of online travel communities to date have not recognized
the importance of knowledge sharing motivations which can stimulate individuals’ knowledge
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Proceedings of Annual South Africa Business Research Conference
11 - 12 January 2016, Taj Hotel, Cape Town, South Africa, ISBN: 978-1-922069-95-5
contributions to online travel communities. Thus, an understanding of the motivations of
knowledge-sharing with suppliers should be useful for interactive marketers to encourage
individuals to contribute actively to online travel communities. Moreover, operators of online
travel communities should monitor the climate (atmosphere) of the communities in terms of
recipients’ attitudes towards knowledge sharing contribution, such as “Replies”, “Like”, and
“Recommendation to share with others”. This implies that operators of online travel
communities need to control the atmosphere of the communities, which enhances knowledge
suppliers’ willingness to continue knowledge-sharing in the future. Despite important
contributions, this study has some limitations. the results cannot be generalized to all online
users. Future research should reveal demographic differences (e.g., gender, ethnicity, and
employment status) in knowledge sharing behaviors.
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