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Based on Social Identity Theory and Trust Theory, this paper discusses the impact of online brand community identity and trust on customer engagement, as well as the impact of customer engagement on customer loyalty. The results show that the two community factors have a significant impact on customer engagement, and customer engagement has a positive correlation with customer loyalty. These results reflect that online brand community identity and trust have positive impact on customer engagement, which increases the motivation of customers to participate in community activities, when customers obtain greater satisfaction and perceived benefits from community activities, the satisfaction and perceived benefits can be transformed into customers' sustainable stickiness and loyalty to the brands. In addition, customer engagement plays a partial intermediary role between online brand community identity, trust and customer loyalty. The results of this study are helpful to improve the management of online brand community, provide companies with ideas to improve business model, innovation, and bring new experience and value to companies and customers.
marketing, customer relation management, community identity, community trust, innovation, online business
In recent years, with the popularization of mobile communication devices and the rapid development of the Internet economy, people's social, consumption, business work and other ways have undergone great changes. According to the 48th Statistical Report on Internet Development in China released by China Internet Network Information Center (CNNIC), as of June 2021, the number of Chinese Internet users was 1.011 billion, including 872 million online payment users and 812 million online shoppers, accounting for 80.3% of the total number of Internet users. It can be seen that online shopping consumption has become the main consumption mode of residents, and more and more consumers choose to make purchase decisions through the information provided by the Internet. As a result, the corporate marketing scene has shifted accordingly: some offline Brand communities have established Online Brand Communities (OBC) to maintain brand-customer relationship, help companies manage consumer relationship and maintain customer loyalty. Online brand community is a portal for companies to communicate with users on the Internet, and a platform for communication and dialogue between brands and customers as well as between customers. It is composed of fans of products or services of specific brands without geographical restrictions [1].
Recent studies have found that antecedent factors such as trust, identity and perceived value affect customer engagement. In turn, engagement influences outcomes such as consumer purchasing decisions, behavioral intentions and company performance [2], community factors are the bonds between social interaction participants, leading to the creation of brand and customer communities. These factors are key drivers of customer engagement in online brand communities [3].
Customer loyalty to the brand is the business goal pursued by companies via establishing brand communities. Members of online brand communities mainly communicate with each other about brand experience and attitude towards the brand through community platforms, which is easier to get more attention from members [4]. However, most online brand community members are "one-time" participants, and customers' persistent intention is generally low [5]. Therefore, how to maintain and improve the sustainable loyalty of community members has become a major challenge for companies [6]. In recent years, the competition of enterprise online brand community is particularly fierce. Companies have invested a lot of money in the establishment and maintenance of their own online brand community, but they still know little about the motivation or successful factors that drive customers' continuous engagement [4]. Many online brand communities only took a few months from germination to disappearance. Hence, in order to successfully attract and retain consumers to participate and maintain customer loyalty, researches on consumers’ motivation and behavior are very urgent and important.
Based on the above background, this study aims to explore the internal influence mechanism and path of online brand community on customer loyalty. The purpose includes two aspects: To study how community factors (such as brand community identity and community trust) affect customer engagement and customer loyalty. To explore the impact of community factors on customer loyalty by taking customer engagement as a mediating variable.
To sum up, this study draws on social identity theory and trust theory literature to explore the antecedents of customer engagement from two aspects of online brand community identity and community trust, and takes customer engagement as a mediating variable to explore the impact of brand community factors on customer loyalty. The specific research questions are as follows:
To explore the influence of brand community identity on customer engagement and customer loyalty among online brand community factors. To discover the influence of brand community trust on customer engagement and customer loyalty among online brand community factors. To confirm the influence of customer engagement as a mediating variable on customer loyalty.
Social Identity Theory (SIT) originated in the early 1970s. It refers to the self-conceptualization and spontaneous categorization of individuals within specific social groups. Once an individual classifies himself or herself as a member of a social group, he or she will have a positive emotion towards the internal group, which helps to improve the attention, engagement and cohesion of the internal group [7].
2.1 Online brand community identity and customer engagement
According to social identity theory, members of online brand communities think that themselves as part of a group or community [8]. The cognitive and emotional significance of members' sense of belonging to the online brand communities will produce social identity as a part of personal self-concept. Online brand community members often show similar preferences for the same brand, purchase the products with that brand, share experiences and perceived values, and interact with other members. Recent studies have accumulated evidence on the influence of brand identity on loyalty [9]. Also, online brand community identity develops higher community engagement among community members [10].
In terms of online brand communities, customer engagement refers to the iterative interaction between community members or brands [4]. Engagement is also related to the willingness of members to interact, collaborate and participate in the community [11]. According to the customer engagement, every aspect that affects the iterative relationship between consumer and OBC trust or perceived community experience has an impact on engagement [12]. Brodie et al. [13] believes that engagement is a psychological state that occurs through the interaction with brands and the co-creation of customer experience. Where brand becomes experience, customer engagement must be defined as a behavioral construct [14]. In this study, customer engagement will be expressed as positive behaviors and attitudes towards the brand community.
Users' identity of brand community is an important factor for companies to establish online brand community successfully. The identity of the brand community makes users believe that the interaction in the community is consistent with the personality and values of the enterprise brand [15], and is recognized by other members. Therefore, the more users identify with online brand communities, the more motivated they are to actively participate and continue to participate in community activities by helping other users build relationships [16]. Previous research in the field of online brand community has shown that online brand community identity has a positive impact on customer engagement [17]. Identity with online brand communities is a strong predictor of customer engagement [18]. Therefore, the following assumptions are obtained:
H1a: Brand community identity has a significant positive correlation with customer engagement.
2.2 Online brand community trust and customer engagement
Trust involves relationships between individuals, groups and organizations [19]. Trust is a psychological state, including willingness, belief, feeling etc., and expectation that the other party will act honestly [20]. In online brand communities, this state can be defined as community trust. Yeh and Choi [18] proposed two types of trust: community cognitive trust and community emotional trust [21]. Under the condition of Web3.0, scholars define online brand community trust as consumers' belief in the integrity, friendliness and ability of community members, as well as the authority, sincerity and fairness of the community [22]. In an online community environment, trust is a key variable in the customer-brand relationship [23]. Based on the trust theory, this study interprets the trust of online brand community as its members' dependence on each other and its members dependence on companies’ brands.
Trust is an essential element of online social commerce, which reduces the uncertainty and perceived risk of online relationships among brand communities. Trust is a motivator and has a positive impact on customers' attitudes and behavior. According to previous studies, trust in online brand communities can influence users' attitudes towards transactions on brand communities [24], Brand business intentions that encourage users to engage and stay engaged [25]. In online brand communities, community trust has a positive impact on customer engagement [26]. Trust is a key factor in building long-term relationships in online brand communities [27]. Therefore, the following assumptions are listed in this paper:
H2a: Brand community trust has a significant positive correlation with customer engagement.
2.3 Online brand community identity and customer loyalty
Previous studies have conceptualized customer loyalty as one of the most important outcomes of customer engagement [28]. Participating members develop a strong psychological connection with the brand community, which increases the likelihood of loyal reactions to it and its products [29]. Loyalty is considered to reflect the structure of three behaviors: willingness to continue paying attention, willingness to buy back and willingness to spread word of mouth [30]. In recent years, the emergence of social media has enhanced customer loyalty and developed the relationship between companies and customers. Internet-based online brand communities can influence customer loyalty. In this study, customer loyalty is defined as the willingness of online brand community members to continuously pay attention to corporate branded products and repeat purchases, and to actively spread word of mouth on products to others [31].
It has been found that brand community identity has a positive impact on brand loyalty. Online brand community identity is a crowd-based phenomenon. Therefore, establishing a good community identity in online brand community can attract users to actively connect, support the community and contribute to the brand. Therefore, companies establish online brand communities to build, maintain and strengthen customer loyalty [32]. The following hypotheses are given for research purposes.
H1b: Brand community identity has a significant positive correlation with customer loyalty.
2.4 Online brand community trust and customer loyalty
Brand trust is an important factor in establishing and maintaining lasting customer brand relationships [33]. A high level of trust in a brand will generate favorable attitudes [34]. Previous studies have shown that brand trust is one of the most important Antecedents of consumer loyalty and repurchase intention [35]. Studies have shown that once members feel trusted by the community, they are more inclined to show positive behaviors towards the community brand and other community members [36]. Trust in online brand communities is a direct and indirect antecedent of customer loyalty [27]. Therefore, this paper puts forward the following hypothesis.
H2b: Brand community trust has a significant positive correlation with customer loyalty.
2.5 Customer engagement and customer loyalty
In participating in the online brand community, customers who feel that they produce their own value may experience pleasant emotions [37]. In addition, the reputation or social reputation gained in the community, as well as the satisfaction of helping others and feeling valued, can bring customers psychological satisfaction. These satisfactions positively influence customers' attitudes towards the brand community, which may result in customers visiting the community more frequently and more widely. In addition, the cognitive effort required to generate and process community-related information or information takes a long time on the site [38]. Previous studies have shown that customer engagement has a direct and positive impact on brand community stickiness [39]. Therefore, this paper believes that the higher the customer engagement, the higher the degree of customer loyalty will be:
H3: Customer engagement has a significant positive correlation with customer loyalty.
2.6 Mediation effect
According to the above derivation, Brand community identity has a direct relationship on Customer engagement, and Brand community identity also has a direct influence on Customer loyalty. Many literatures indicate that there is a positive correlation between Customer engagement and Customer loyalty. Therefore, this paper assumes that the following mediating effects exist and are significant.
H4: Customer engagement plays a significant mediating role between brand community identity and customer loyalty.
In the same reasoning process, Brand community trust has a significant direct relationship with Customer engagement and Customer loyalty, which has been supported by many researchers. There is also a correlation between Customer engagement and Customer loyalty. Therefore, this paper makes the following hypothesis about the mediation effect:
H5: Customer engagement plays a significant mediating role between brand community trust and customer loyalty.
2.7 Research model
According to the relationship and path between variables, the following conceptual framework (Figure 1) is constructed and relevant research work is carried out.
Figure 1. The conceptual framework
This study used questionnaire survey to obtain data. The research subjects were part of registered users of two mobile online brand communities with large numbers of active members in China, Huawei Pollen Club and Xiaomi Community. With the help of Questionnaire star software, the questionnaire will be sent to Huawei Pollen Club and Xiaomi community after obtaining the consent of the community management personnel. In this study, a convenient sampling method was adopted. A total of 480 questionnaires were issued and 425 valid questionnaires were recovered, with an effective rate of 88.5%.
In this study, SPSS22.0 software was used for descriptive analysis of personal statistical variables and reliability and validity analysis of the questionnaire. Then AMOS22.0 was used for path analysis and hypothesis testing, and structural equation model (SEM) was used for empirical test of the proposed research model.
In this study, the scale developed by Algesheimer et al. [40] was used to measure brand community identity. Brand community trust comes from Kim and Park [41]; Customer engagement comes from Cheung et al. [42]; Customer loyalty comes from Shin et al. [43]; All items were measured using the Likert Scale, which measures items from " totally disagree" to " totally agree."
4.1 Sample descriptive statistics
Descriptive statistical analysis was conducted on brand community usage of demographic data of the sample population, and the results are shown in Table 1 below:
Table 1 shows the demographic characteristics of participants. Among the valid samples, 58.6% are members of Huawei pollen club and 41.4% are members of Xiaomi community. The majority of participants were female, accounting for 51.8%; The main age range is 19-35 years old, accounting for 88.5%, indicating that young people are the main active members of the technology brand community. The proportion of members with a bachelor's degree was 86.8%. In terms of the time of registering as a member of this community, 63.3% of the members have registered for 1-3 years, and 30.1% have registered for 4-6 years. The frequency of community use was 1-3 times per week, accounting for 56.7%. Most members use the community for 1-3 hours per week, accounting for 58.6%, indicating that most members use the community for no more than 3 hours per week.
4.2 Reliability analysis
Reliability refers to the reliability or consistency of the measurement results. Reliability is usually analyzed according to Cronbach's alpha coefficient, and coefficients exceeding 0.70 are considered acceptable. The larger the coefficient is, the better the reliability of the scale is. Among them, α ≥ 0.9 indicates high reliability and ideal data effect. The reliability analysis results of this study (Table 2): α= 0.976, which shows that the scale has good reliability and can be analyzed in the next step.
4.3 Validity analysis
Validity refers to the degree to which a measuring tool or means can accurately measure the things to be measured, that is, validity. Kaiser-Meyer-Olkin (KMO) test, Bartlett sphericity test, variance interpretation rate, factor load factor value and other indicators were used to confirm validity. The value of KMO is between 0 and 1. When the value of KMO is greater than 0.7, it is suitable for factor analysis.
The results of this study can be seen in the following series of tables. KMO=0.967, between 0.7-0.9, bartlett significance =0.000, less than 0.05 (Table 3). When principal component analysis and eigenvalue were greater than 1, four factors were extracted, which were in line with the original dimensions of the scale, and total variance interpretation accumulated 73.062% (Table 4). Table 5 shows the component matrix after the removal coefficient load is lower than 0.5, and four factors are obtained, which are brand community identity (PPSQRT), brand community trust (PPSQXR), customer engagement (GKCY) and customer loyalty (GKZCD). Then, confirmatory factor analysis (CFA) was conducted for the four factors. CR values and AVE values corresponding to the four factors were both greater than 0.5 and greater than 0.9, presenting significance (Table 6), indicating that data aggregation (convergence) is effective. In conclusion, the scale in this study has good structural validity and can be further studied.
Table 1. Demographics
Content |
Option |
Frequency |
Percentage |
Effective percentage |
Community |
Hua Wei |
249 |
58.6 |
58.6 |
Xiao Mi |
176 |
41.4 |
41.4 |
|
Total |
425 |
100.0 |
100.0 |
|
Gender |
Male |
205 |
48.2 |
48.2 |
Female |
220 |
51.8 |
51.8 |
|
Total |
425 |
100.0 |
100.0 |
|
Age |
19-25 |
56 |
13.2 |
13.2 |
26-30 |
215 |
50.6 |
50.6 |
|
31-35 |
105 |
24.7 |
24.7 |
|
Age 36 and above |
49 |
11.5 |
11.5 |
|
Total |
425 |
100.0 |
100.0 |
|
Degree of education |
College |
24 |
5.6 |
5.6 |
Undergraduate |
369 |
86.8 |
86.8 |
|
Master's degree |
32 |
7.5 |
7.5 |
|
Total |
425 |
100.0 |
100.0 |
|
Registration time |
Within 1 year |
12 |
2.8 |
2.8 |
1-3years |
269 |
63.3 |
63.3 |
|
4-6years |
128 |
30.1 |
30.1 |
|
6years and above |
16 |
3.8 |
3.8 |
|
Total |
425 |
100.0 |
100.0 |
|
Weekly frequency of use |
1 time a day |
55 |
12.9 |
12.9 |
1-3 times |
241 |
56.7 |
56.7 |
|
4-6 times |
90 |
21.2 |
21.2 |
|
Once a week or none |
39 |
9.2 |
9.2 |
|
Total |
425 |
100.0 |
100.0 |
|
Average weekly usage time |
Less than 1 hour |
111 |
26.1 |
26.1 |
1-3 hours |
249 |
58.6 |
58.6 |
|
4-6 hours |
45 |
10.6 |
10.6 |
|
7 hours and above |
20 |
4.7 |
4.7 |
|
Total |
425 |
100.0 |
100.0 |
Table 2. Reliability analysis
Dimension |
Cronbach'sα |
Cronbach'sα(total) |
PPSQRT |
0.921 |
0.976 |
PPSQXR |
0.936 |
|
GKCY |
0.953 |
|
GKZCD |
0.960 |
Table 3. Validity test
Variable |
KMO test |
Bartlett test |
KMO value |
Significance level |
|
total variable |
0.967 |
0.000 |
PPSQRT |
0.861 |
0.000 |
PPSQXR |
0.944 |
0.000 |
GKCY |
0.951 |
0.000 |
GKZCD |
0.956 |
0.000 |
Table 4. Explanation table of total variance
Total Variance Explained |
|||||||||
Component |
Initial Eigenvalues |
Extraction Sums of Squared Loadings |
Rotation Sums of Squared Loadings |
||||||
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
Total |
% of Variance |
Cumulative % |
|
1 |
19.592 |
54.422 |
54.422 |
19.592 |
54.422 |
54.422 |
7.140 |
19.832 |
19.832 |
2 |
2.129 |
5.915 |
60.336 |
2.129 |
5.915 |
60.336 |
6.846 |
19.018 |
38.850 |
3 |
1.941 |
5.391 |
65.728 |
1.941 |
5.391 |
65.728 |
6.395 |
17.765 |
56.615 |
4 |
1.557 |
4.324 |
70.052 |
1.557 |
4.324 |
70.052 |
4.837 |
13.437 |
70.052 |
Table 5. Factor loading component table
Rotated Component Matrixa |
||||
Component |
||||
1 |
2 |
3 |
4 |
|
PPSQRT2 |
0.786 |
|||
PPSQRT1 |
0.761 |
|||
PPSQRT5 |
0.755 |
|||
PPSQRT6 |
0.745 |
|||
PPSQRT4 |
0.714 |
|||
PPSQRT3 |
0.711 |
|||
PPSQXR3 |
0.715 |
|||
PPSQXR1 |
0.710 |
|||
PPSQXR5 |
0.705 |
|||
PPSQXR2 |
0.688 |
|||
PPSQXR9 |
0.685 |
|||
PPSQXR8 |
0.664 |
|||
PPSQXR4 |
0.644 |
|||
PPSQXR6 |
0.642 |
|||
PPSQXR10 |
0.604 |
|||
PPSQXR7 |
0.550 |
|||
GKCY3 |
0.755 |
|||
GKCY9 |
0.749 |
|||
GKCY8 |
0.748 |
|||
GKCY6 |
0.746 |
|||
GKCY7 |
0.726 |
|||
GKCY4 |
0.707 |
|||
GKCY2 |
0.704 |
|||
GKCY1 |
0.701 |
|||
GKCY5 |
0.700 |
|||
GKCY10 |
0.676 |
|||
GKZCD4 |
0.780 |
|||
GKZCD6 |
0.735 |
|||
GKZCD9 |
0.730 |
|||
GKZCD8 |
0.725 |
|||
GKZCD7 |
0.696 |
|||
GKZCD10 |
0.693 |
|||
GKZCD5 |
0.682 |
|||
GKZCD1 |
0.680 |
|||
GKZCD2 |
0.670 |
|||
GKZCD3 |
0.665 |
4.4 Hypothesis testing
Based on the conceptual framework (Figure 1), a structural equation model is established in this study. The relationship between the four variables is shown in Figure 2.
After the model data is modified, the following values are obtained. The model fit CMIN\/DF=2.963, satisfying CMIN\/DF<3; indicating that the model fitting index has reached an acceptable range and degree. The root mean square error of RMSEA is 0.068, satisfying the upper limit RMSEA<0.08. The standard of 0.08 indicates that the model fits well. GFI=0.865, NFI=0.900, RFI=0.890, IFI=0.931, TLI=0.924, CFI=0.931, all meet the index greater than 0.80, which further confirms the goodness of fit of the model (Table 7).
Figure 2. Structural model of the study
Table 6. Model AVE and CR indicator results
Model AVE and CR index results |
||||||||
Factor |
Measurement item |
Coef. |
Std. Error |
CR |
p |
Std. Estimate |
AVE |
CR |
PPSQRT |
PPSQRT1 |
1 |
- |
- |
- |
0.885 |
0.66 |
0.921 |
PPSQRT |
PPSQRT2 |
0.976 |
0.038 |
25.436 |
0.000 |
0.876 |
||
PPSQRT |
PPSQRT3 |
0.915 |
0.046 |
19.995 |
0.000 |
0.77 |
||
PPSQRT |
PPSQRT4 |
0.855 |
0.045 |
19.101 |
0.000 |
0.749 |
||
PPSQRT |
PPSQRT5 |
1.001 |
0.045 |
22.233 |
0.000 |
0.817 |
||
PPSQRT |
PPSQRT6 |
0.877 |
0.044 |
19.814 |
0.000 |
0.765 |
||
PPSQXR |
PPSQXR1 |
1 |
- |
- |
- |
0.8 |
0.597 |
0.937 |
PPSQXR |
PPSQXR2 |
1.277 |
0.076 |
16.865 |
0.000 |
0.738 |
||
PPSQXR |
PPSQXR3 |
1.567 |
0.081 |
19.298 |
0.000 |
0.816 |
||
PPSQXR |
PPSQXR4 |
1.39 |
0.079 |
17.589 |
0.000 |
0.762 |
||
PPSQXR |
PPSQXR5 |
1.44 |
0.08 |
17.975 |
0.000 |
0.774 |
||
PPSQXR |
PPSQXR6 |
1.455 |
0.084 |
17.369 |
0.000 |
0.754 |
||
PPSQXR |
PPSQXR7 |
1.326 |
0.082 |
16.178 |
0.000 |
0.714 |
||
PPSQXR |
PPSQXR8 |
1.538 |
0.085 |
18.117 |
0.000 |
0.779 |
||
PPSQXR |
PPSQXR9 |
1.726 |
0.088 |
19.687 |
0.000 |
0.827 |
||
PPSQXR |
PPSQXR10 |
1.548 |
0.089 |
17.319 |
0.000 |
0.753 |
||
GKCY |
GKCY1 |
1 |
- |
- |
- |
0.827 |
0.67 |
0.953 |
GKCY |
GKCY2 |
1.242 |
0.059 |
21.213 |
0.000 |
0.836 |
||
GKCY |
GKCY3 |
1.16 |
0.06 |
19.396 |
0.000 |
0.788 |
||
GKCY |
GKCY4 |
1.219 |
0.063 |
19.508 |
0.000 |
0.791 |
||
GKCY |
GKCY5 |
1.172 |
0.058 |
20.198 |
0.000 |
0.81 |
||
GKCY |
GKCY6 |
1.292 |
0.06 |
21.508 |
0.000 |
0.843 |
||
GKCY |
GKCY7 |
1.125 |
0.055 |
20.357 |
0.000 |
0.814 |
||
GKCY |
GKCY8 |
1.207 |
0.057 |
21.352 |
0.000 |
0.839 |
||
GKCY |
GKCY9 |
1.222 |
0.061 |
20.184 |
0.000 |
0.809 |
||
GKCY |
GKCY10 |
1.343 |
0.065 |
20.707 |
0.000 |
0.823 |
||
GKZCD |
GKZCD1 |
1 |
- |
- |
- |
0.867 |
0.704 |
0.96 |
GKZCD |
GKZCD2 |
0.923 |
0.044 |
20.743 |
0.000 |
0.787 |
||
GKZCD |
GKZCD3 |
0.994 |
0.042 |
23.931 |
0.000 |
0.852 |
||
GKZCD |
GKZCD4 |
1.051 |
0.043 |
24.439 |
0.000 |
0.861 |
||
GKZCD |
GKZCD5 |
1.067 |
0.045 |
23.639 |
0.000 |
0.847 |
||
GKZCD |
GKZCD6 |
1.032 |
0.045 |
22.88 |
0.000 |
0.832 |
||
GKZCD |
GKZCD7 |
1.005 |
0.044 |
23.004 |
0.000 |
0.835 |
||
GKZCD |
GKZCD8 |
1.024 |
0.045 |
22.566 |
0.000 |
0.826 |
||
GKZCD |
GKZCD9 |
1.064 |
0.045 |
23.69 |
0.000 |
0.848 |
||
GKZCD |
GKZCD10 |
1.002 |
0.043 |
23.073 |
0.000 |
0.836 |
Table 7. Model fit index
Model |
NPAR |
CMIN |
DF |
P |
CMIN/DF |
Default model |
61 |
1022.388 |
345 |
0.000 |
2.963 |
Model |
NFI |
RFI |
IFI |
TLI |
CFI |
Default model |
0.9 |
0.89 |
0.931 |
0.924 |
0.931 |
Model |
RMSEA |
LO 90 |
HI 90 |
PCLOSE |
|
Default model |
0.068 |
0.063 |
0.073 |
0 |
Table 8. Path coefficient analysis of the structural equation
Estimate |
S.E. |
C.R. |
P |
Label |
|||
GKCY |
<--- |
PPSQXR |
0.495 |
0.037 |
13.395 |
*** |
par_27 |
GKCY |
<--- |
PPSQRT |
0.255 |
0.033 |
7.695 |
*** |
par_28 |
GKZCD |
<--- |
PPSQRT |
0.268 |
0.039 |
6.781 |
*** |
par_25 |
GKZCD |
<--- |
PPSQXR |
0.477 |
0.05 |
9.539 |
*** |
par_26 |
GKZCD |
<--- |
GKCY |
0.291 |
0.067 |
4.351 |
*** |
par_29 |
Through the analysis of path regression coefficient, if the P value is less than 0.05, the hypothesis is true; However, if it is greater than 0.05, this hypothesis is not supported. As shown in Table 8, brand community identity (PPSQRT) has a significant positive impact on customer engagement (GKCY) and customer loyalty (GKZCD) (P value<0.05),so H1a and H1b are valid; Similarly, brand community trust (PPSQXR) has a significant positive impact on customer engagement (GKCY) and customer loyalty (GKZCD) (P value <0.05), H2a and H2b hypotheses are valid; Customer engagement (GKCY) and customer loyalty (GKZCD) had significant positive effects (P value <0.05), H3 hypothesis is true.
The tie-Bootstrap method was used to detect the mediating effect, and it can be seen from Table 9. The P values of PPSQRT=>GKCY=>GKZCDP and PPSQXR=>GKCY=>GKZCD were both less than 0.01 (P value less than 0.05 indicates that the hypothesis is true), indicating that the mediation effect was established and H4 and H5 hypotheses were established. In addition, a and b are significant, and c' is significant, and a*b and c' have the same name, indicating that the two mediation paths are partially mediated. As can be seen from Table 10, calculated by formula a*b/c, the mediation effect of PPSQXR=>GKCY=>GKZCD was 23.878%, PPSQRT=>GKCY=>GKZCD accounted for 22.744%.
Table 9. Mediating effect analysis
Summary of mediation test results |
||||||||||
Item |
c total effect |
a |
b |
a*b Intermediate effect value |
a*b (Boot SE) |
a*b (z) |
a*b (p) |
a*b (95% BootCI) |
c’ Direct effect |
Conclusion |
PPSQXR=>GKCY=>GKZCD |
0.684** |
0.651** |
0.251** |
0.163 |
0.03 |
5.481 |
0 |
0.084 ~ 0.203 |
0.520** |
Part of the intermediary |
PPSQRT=>GKCY=>GKZCD |
0.287** |
0.260** |
0.251** |
0.065 |
0.016 |
3.977 |
0 |
0.039 ~ 0.104 |
0.222** |
Part of the intermediary |
Note: * p<0.05 ** p<0.01
Table 10. Mediating effect size results
Mediating effect size results |
||||||
Item |
Conclusion |
c total effect |
a*b Intermediate effect value |
c’ Direct effect |
Calculation formula of effect proportion |
Effect of |
PPSQXR=>GKCY=>GKZCD |
Part of the intermediary |
0.684 |
0.163 |
0.52 |
a * b / c |
23.878% |
PPSQRT=>GKCY=>GKZCD |
Part of the intermediary |
0.287 |
0.065 |
0.222 |
a * b / c |
22.744% |
The results of this study provide guidance for online brand communities to formulate customer loyalty strategies, help to improve the management level of online brand communities, help to maintain customer loyalty to companies, better maintain the relationship between enterprise customers and brands, develop and expand enterprise brand influence, and contribute to the long-term development of enterprise brands.
The results show that brand community identity and brand community trust have significant positive effects on customer engagement and customer loyalty. Similarly, customer engagement is positively correlated with customer loyalty. This shows that identity and trust in online brand communities have a positive impact on customer engagement, which increases customer motivation to participate in community activities, and customers get greater satisfaction from engagement in community activities, and perceived benefits are translated into stickiness and loyalty to brands and products. In addition, the partial mediating role of customer engagement as a mediating variable also highlights the important role of customer active engagement in the relationship between online brand community and customer loyalty.
According to the research results, this study puts forward the following two suggestions for companies. First, companies should strengthen community identity. Managers can strive to ensure that community members are satisfied with their sense of belonging by providing practical value (such as preferential purchase or priority purchase of scarce and popular products) and hedonic value (such as sharing experience and celebrations). The more members identify with the community, the higher their engagement. Second, community managers should provide mechanisms to strengthen community trust to increase customer engagement. For example, by establishing a rating system to confirm the usefulness of reviews, the system will identify users with the highest ratings and reward them; Create comprehensive user behavior rating standards; In addition, companies should operate and manage honestly and reward loyal customers.
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