Antecedents of Engagement within Online Sharing Economy Communities

Abstract

In this article, the authors’ study focused on the role of community engagement in online sharing economy communities, which have become increasingly popular. Drawing on social psychology insights, a research model was proposed to explore and analyze community engagement as a multidimensional concept that takes on various forms of action in different contexts. The study then examined the factors that influence community engagement, such as community identification and relationship commitment. By surveying 432 users of online sharing communities, the researchers found that their hypotheses were supported, providing insights into the motivations that drive consumers to participate in sharing economy communities. This study is relevant for managers of online sharing communities seeking to understand the factors that encourage user engagement.

1. Introduction

Over the past few years, the sharing economy (SE) has rapidly grown and has caused significant impacts on various sectors of the socio-economic systems. This phenomenon has attracted considerable research attention and has resulted in the emergence of new players in different fields [1]. Some of these players have surpassed their predecessors, while others are gaining popularity in their respective areas [2]. The SE’s growth is closely linked to several factors, including an improved value distribution along the supply chain, reduced ecological harm, scientific and technological advances, and socio-economic improvements for end-users. Additionally, the SE has altered people’s attitudes toward product ownership and social demand [3]. Although the size of the sharing economy has not been precisely calculated, industry experts predict that it could reach CNY 5.44 trillion by 2025 in China [4]. This implies that business models based on the sharing economy will continue to be in the spotlight in the future.

In the current business landscape, consumers are more concerned about who uses goods rather than who owns them. This has led to the emergence of the sharing economy, where consumers have access to goods for their use without necessarily owning them. The popularity of this business model has led to the growth of various sharing-focused companies, the most representative being Airbnb, DIDI, Mobike, Erento, and OFO in China [5]. In addition, online communities centered around the sharing economy have also emerged. Unlike other businesses, the sharing economy is highly focused on improving the community’s service level and gaining insight into user needs to enhance user engagement [6].

Online communities are social media platforms that facilitate the formation of online connections among a company’s customers, leading to the development of strong relationships. Scholars suggest that these communities can impact customer cognition and emotions, and provide a platform for companies to advertise their products and services [7]. Community members are more likely to visit a company’s website, and they are willing to help and provide answers to questions from other customers [8]. Furthermore, consumer community engagement behaviors are dynamic, and they change as the members’ involvement in the community evolves [9]. Therefore, marketers are interested in understanding how to establish and promote community engagement effectively. Additionally, several dimensions are associated with community engagement behavior. Prior studies have emphasized the variety of community engagement concepts, which are incorporated as engagement scales and developed and validated across selected social media platforms [10].

Previous studies have provided a limited understanding of how a member’s perceived identification and relational commitment within a community influence the relationship of community engagement. This article aims to develop and test a model that examines the role of community engagement in online sharing economy spaces. First, we propose a research paradigm based on social psychology studies to explore and analyze community engagement. Our model suggests that the primary drivers of community engagement are two mechanisms—identification [11] and commitment [12]—and that there is a relationship between these two mechanisms. Moreover, we propose that entitativity, trust among community members, and satisfaction with relationships impact community engagement through these two mechanisms. Second, we argue that community engagement is a multidimensional concept that leads to different forms of engagement in various contexts. Community engagement can be broken down into three dimensions: cognitive, emotional, and behavioral engagement [13,14]. Ultimately, our research has practical implications for those who manage online communities, especially online social communities. By applying the insights provided by this research, managers can focus on promoting what drives customer engagement.

2. Literature Review and Hypotheses

2.1. The Characteristics of Sharing Economy

Initially, the sharing economy was seen as a charitable initiative, such as lending books or giving away power banks in public transportation hubs. However, it has since evolved into a lucrative business model where a percentage of each sharing fee is collected as revenue [15]. Today, academics view the sharing economy as a viable alternative to permanent ownership, where people engage in collaborative consumption by exchanging, sharing, or renting resources without owning them. These resources can be tangible or intangible [16]. The term “sharing economy” in economic transactions refers to the use of a commodity that is divided into individual parts and shared [1]. In contrast, the sharing economy involves using technology platforms to facilitate shared activities effectively [17].

The sharing economy primarily relies on online platforms, where customers can act as both providers and consumers of resources [18,19]. The sharing economy has had a significant impact on traditional views of marketing [20]. The sharing economy has disrupted conventional marketing by changing how consumers engage with products and services. In the sharing economy, consumers are not only consumers but also producers, and they play an active role in shaping the market. This has resulted in a shift from a traditional one-way marketing model to a more participatory and collaborative approach. Additionally, the sharing economy has made it easier for consumers to access and evaluate products and services through online platforms, reviews, and ratings, which has increased the importance of reputation and trust in marketing. Overall, the sharing economy has challenged traditional marketing practices and has forced businesses to adapt to new consumer expectations and preferences [17]. As a result, the sharing of experiences and resources has become more widespread than it would have been without these online spaces [21].

Previous studies have identified economic, ideological, social, and environmental factors as the key determinants of consumers’ participation in the sharing economy [22]. Intrinsic motivation, which arises from the satisfaction derived from engaging in the activity, drives behavior, whereas extrinsic motivation stems from outcomes other than the behavior itself [23].

2.2. Online Sharing Economy Community Engagement

The term online sharing economy community engagement in this study encompasses various direct or indirect activities carried out by consumers within online sharing economy communities [24]. These communities are formed around a shared purpose, with participatory behaviors based on shared identities, responsibilities, and rituals [25]. Most interactions within these communities take place virtually, mediated through the Internet [26]. Scholars have described community engagement as a multidimensional concept that includes cognitive, emotional, and behavioral dimensions, involving interactions between consumers and the brand or other community members [14,27]. Based on this understanding, we defined online community engagement in the sharing economy as a manifestation that should not be limited to behavioral activities alone but should also include cognitive and emotional activities that enhance the customer experience.

Online community engagement can be categorized into three dimensions from a multidimensional perspective [28,29]. The first dimension is cognitive engagement, which can be further divided into attention and absorption [30]. Attention refers to the cognitive resources devoted to engaging with an object, whereas absorption is the state of being completely engrossed in the object and losing track of time [31,32]. This is implicated when, for example, potential customers lose track of time as they engage with other customers online. Consequently, these two parts have been made into two dimensions of a second-order concept that will be referred to as cognitive engagement. The second dimension is emotional engagement, which encompasses factors such as enjoyment, support, belonging, and attitudes [33]. The third dimension is behavioral engagement, which includes the observable actions of consumers as they engage with sharing economy communities [34]. These actions are driven by intrinsic stimuli that consumers find appealing or feel a deeper connection with, particularly within online platform environments [35]. These behaviors are crucial for spreading the message on social media [36].

2.3. Online Community Identification and Online Sharing Economy Community Engagement

Humans tend to develop strong attachments to communities and can even become addicted to them [37]. This is because humans have an innate need for a sense of belonging within a group [38]. Community identification is a way to address this need, and refers to the extent to which people derive their sense of self from their membership in a particular group [39]. The more identified individuals become with a community, the more likely they are to invest their time and energy in it to ensure its success [40]. Community members also influence each other’s psychology and behavior, providing help and support within the group [41]. The level of community identification is a reflection of an individual’s psychological connection to the community and can drive changes in attitudes and behaviors to better align with the community’s expectations [42]. Research has demonstrated that community identification is a stronger predictor of user behavior than other related factors, such as adherence to subjective norms or confidence in others, in electronically mediated groups [43].

Community identification has been found to have a significant impact on individuals’ willingness to participate in communal activities, collaborate, and engage in corporate social responsibility initiatives [44]. However, the extent to which community identification affects engagement after segmentation is not well established. Therefore, we suggest the following hypothesis.

Hypothesis 1 (H1). 

Community identification has a positive effect on online sharing economy community engagement.

2.4. Online Relationship Commitment and Online Sharing Economy Community Engagement

Effective communication is crucial for building buyer–seller connections, both formally and informally, by exchanging timely and relevant information [45]. This information exchange and communication lead to continued commitment in a relationship [46]. Most researchers concur that commitment is a critical factor that motivates individuals to continue a relationship. Anderson and Sullivan [47] defined commitment as a subjective psychological state that drives various related actions such as building and maintaining friendships. Additionally, commitment reflects a person’s internal perception of dependence on the established relationship [48]. Relationship commitment is an ongoing need that both parties in a relationship experience in order to maintain it. If the parties have developed a positive rapport, they will continue to cooperate [49]. The stronger the desire of an individual to maintain a relationship, the deeper their attachment to it, and the more time and effort that they will devote to preserving and nurturing it [50]. Engaging in the sharing economy can lead to the development of strong relationships and can be viewed as a form of social support facilitated by prosocial behavior [51]. Consequently, individuals who are committed to their relationships are more willing to engage in sharing behaviors that benefit others [52]. Therefore, we propose that:

Hypothesis 2 (H2). 

Relationship commitment has a positive effect on online sharing economy community engagement.

2.5. Online Community Identification and Online Relationship Commitment

Attitudes and community commitment have been considered crucial for the psychological connection between individuals and groups [12]. Commitment is described as having an emotional attachment to, identification with, and involvement in the organization [53]. There is some overlap between the concepts of identification and commitment [54]. Identification refers to how integrated a group is in one’s self-concept, whereas commitment is usually seen as a relationship with the community and its members. However, once an individual includes their relationship with the group in their self-concept, their identification is influenced by their perceived commitment [55]. In sharing economies, membership-based communities are no exception. The more a user contributes to an online sharing economy community, the higher the cost that they would incur if they moved to another community [56]. Therefore, we propose that:

Hypothesis 3 (H3). 

Relationship commitment has a positive effect on community identification.

2.6. Community Entitativity, In-Group Trust, and Online Community Identification

Community members tend to share similarities and develop strong relationships, leading to increased cooperation within the community [57]. In highly entitative communities, members tend to have more contact with each other, which further fosters collaboration. Members of in-groups tend to maintain a greater social distance from out-groups, and they have a stronger preference for their in-group members [58]. Entitative communities provide a sense of security and satisfaction, which helps to meet the self-protection needs of community members and reduce the perceived threat from out-groups [59,60]. These factors contribute to the willingness of individuals to maintain close relationships with community members, thus strengthening their community identification [61]. Therefore, we propose that:

Hypothesis 4 (H4). 

Community entitativity has a positive effect on community identification within the sharing economy.

Previous studies have found that trust plays a crucial role in the adoption of various information systems by consumers [62]. To promote trust-building mechanisms on online platforms, researchers have focused on guiding intentions and behaviors [63]. Trust can help to reduce uncertainty in online exchange platforms and social networks have been identified as playing a key role in establishing and maintaining trust within communities and organizations [64]. Studies have also suggested that an individual’s willingness to engage in trusting behavior is influenced by their identification with a community and its members based on research in social psychology on the link between community identity and cooperation [11]. Furthermore, the willingness to engage in trusting behavior reflects one’s community identity [25] and, as such behavior deepens, individuals become more aware of their place within the community. Finally, perceptions of trust and distrust among community members can also affect an individual’s identification with the group [65]. Thus, we propose the hypothesis that:

Hypothesis 5 (H5). 

In-group trust has a positive effect on community identification within the sharing economy.

2.7. In-Group Trust, Relationship Satisfaction, and Relationship Commitment

A number of researchers have suggested that the most important prerequisite for commitment is trust [66]. Trust arises when one party has faith in the dependability and honesty of the other [67,68]. Thus, we define trust as the willingness to rely on other users for reliable exchanges and transactions, i.e., in-group trust. In the online service provisioning space, building trust with users can help to improve the transaction performance and reduce uncertainty in the relationship [69]. Communication trust plays a key role in enhancing mutual relationships, which leads to their continuation [70]. Exchanges that express trust, respect, and familiarity come from trusting the relationship [71]. In addition, most research on relationship marketing concurs that trust positively affects relationship commitment [72]. Specifically, the trust of internal members is essential to relationship commitment [66,73]. Based on these findings and previous studies, the following hypothesis is proposed:

Hypothesis 6 (H6). 

In-group trust has a positive effect on relationship commitment within the sharing economy.

In interpersonal relationships, reliance increases proportionally with the partnership’s ability to produce a positive consequence. If an individual is pleased with the relationship, he or she is more inclined to commit to it [74]. In addition, positive feelings of individuals toward the group and group members will lead to positive evaluations of the group [75]. Moreover, positive evaluations contribute to the satisfaction of each group’s members with their relationships, thus promoting their relational commitment to the group and individual members [76]. In short, a consumer must find a relationship satisfactory in order to commit to continuing it. If group members are satisfied with the relationships among members, they will elect to maintain their commitment to the group and further promote group loyalty. We therefore suggest that:

Hypothesis 7 (H7). 

Relationship satisfaction has a positive effect on relationship commitment within the sharing economy.

2.8. Research Model

Based on these hypotheses, we developed the research model in Figure 1.

3. Methodology

3.1. Data Collection and Sample

The study aimed to survey Chinese customers who participated in an online sharing economy community. To gather data, English questionnaires were created based on previous scales and then translated into Chinese through a back translation process. The questionnaires were distributed to users via the online research platform “Questionnaire Star”. To ensure the authenticity of the responses, participants were required to spend at least three minutes filling out each form. A total of 432 valid responses were collected, and the demographic characteristics of the sample are presented in

Table 1. Sample demographics (n = 432).

	Variable	Frequency	Percentage
Gender	Male	251	58.1
	Female	181	41.9
Age	Under the age of 20	26	6.0
	21–30	152	35.2
	31–40	109	25.2
	41–50	129	29.2
	Over 50	19	4.4
Income	Under CNY 3000	96	22.2
	CNY 3000–7000	147	34.0
	CNY 7000–10,000	126	29.2
	Over CNY 10,000	63	14.6
Total Response		432	100

.

3.2. Measures

We first asked the respondents to answer whether they had joined the online sharing economy community. Only respondents who answered “yes” to this question were used in the analysis.

Table 2. Measures and scales.

Constructs	Number	Sources	Measurement Items
Community Identification	Five items	Obst (2004); Ray, Kim, and Morris (2014) [43,77]	I think the members of the online sharing economic community are one.
			I think I am very similar to other members of the online sharing economy community.
			I have a strong sense of belonging to the online sharing economy community.
			I have a lot in common with members of the online sharing economy community.
			I am no different from members of the online sharing economic community.
Relationship Commitment	Five items	Hsu, Liu, and Lee (2010); Ma and Yuen (2011) [52,78]	I am committed to maintaining relations with other members.
			I hope the relationship with other members will last for a long time.
			I would be sad if the relationship with other members ends in the near future.
			I hope that the relationships with the other members will continue.
			I hope to build good relations with the other members.
Community Entitativity	Four items	Hogg, Sherman, Dierselhuis, Maitner, and Moffitt, (2007); Blanchard (2020) [61,79]	My online sharing economy community is a unit.
			All members of the online sharing economic community are in a group.
			For members, joining an online sharing economic community is equivalent to joining a community.
			For its members, the online sharing economy community is a group.
In-group Trust	Five items	Chen, Zhang, and Xu (2009); Semerciöz, Hassan, and Aldemr (2011); Dabija, Csorba, Isac, and Rusu (2022) [54,68,80]	I believe the members of the online sharing economy community.
			I believe that other members of the online sharing economy community are trustworthy.
			I think the members of the online sharing economy community are honest.
			Members of the online sharing economy community perceive high credibility.
			Even if not monitored, I would trust other members of the online sharing economy community.
Relationship Satisfaction	Six items	Semerciöz, Hassan, and Aldemr (2011); Røysamb, Vittersø and Tambs, (2014) [81,82]	I have a very good relationship with the members of the online sharing economy community.
			I am very happy with the members and relationships of the online sharing economy community.
			I am serious about my relationship with members of the online sharing economy community.
			I want to communicate regularly with members of the online sharing economy community.
			I feel very happy with my relationship with the members of the online sharing economy community.
			I have many problems with the membership of the online sharing economy community.
Cognitive Engagement	Five items	Hollebeek, Glynn, and Brodie (2014); Stefansson, Gestsdottir, Geldhof, Skulason, and Lerner (2016) [10,83]	While engagement in the online sharing economy community, you will get more attention and evaluation than usual.
			In the process of engagement in the online sharing economy community, I got a lot of attention and evaluation about me, and I found it very meaningful.
			With the support and encouragement of others, there is a greater desire to join the online sharing economy community.
			The more you use the sharing economy, the more you understand what an online sharing economy community is.
			I often talk to people about what my gains have been from using the online sharing economy community.
Emotional Engagement	Five items	Han, Jun, and Kim (2019); Ni, Shao, Geng, Qu, Niu, and Wang (2020) [27,84]	It is very convenient to engage in the online sharing economy community.
			It is comfortable to engage in the online sharing economy community.
			It is happy to engage in the online sharing economy community.
			I think the online sharing economy community is interesting.
			I am excited by engagement in the online sharing economy community.
Behavioral Engagement	Five items	Baldus, Voorhees, and Calantone (2014); Wong and Lee (2022) [29,85]	I engagement in the online sharing economy community.
			I am familiar with the use of online sharing economy communities.
			I will actively choose to engage in online sharing economy communities.
			I recommend online sharing economy communities to others.
			I consider online sharing economy communities to be positive.

lists eight constructs designed to test the hypotheses of this study.

4. Results

4.1. Reliability and Validity Analysis

The validity and reliability of the construct used in this study were examined using SPSS 22.0 and AMOS 26.0. Cronbach’s α was used to assure reliability and analyze internal consistency. A varimax-based principal component analysis was undertaken to investigate principal components. The results are shown in

Table 3. Results of principal component analysis.

Rotated Component Matrix
	Component
		1	2	3	4	5	6	7	8	α
Relationship Satisfaction	RS6	0.835	−0.003	0.194	0.127	0.030	0.113	0.116	0.131	0.904
	RS5	0.783	−0.010	0.164	0.148	0.100	0.080	0.127	0.074
	RS1	0.763	0.019	0.183	0.097	0.063	0.197	0.140	0.106
	RS4	0.753	−0.013	0.147	0.076	0.080	0.125	0.110	0.023
	RS2	0.746	−0.037	0.157	0.127	0.043	0.117	0.100	0.095
	RS3	0.728	−0.048	0.185	0.156	0.105	0.103	0.119	0.057
Emotional Engagement	EE5	−0.042	0.865	0.118	0.037	0.092	0.148	0.124	0.081	0.915
	EE1	−0.022	0.838	0.118	0.078	0.046	0.063	0.112	0.104
	EE3	0.060	0.829	0.085	0.101	0.168	0.118	0.087	0.022
	EE2	−0.030	0.826	0.046	0.044	0.147	0.119	0.135	0.131
	EE4	−0.066	0.766	0.094	0.076	0.071	0.227	0.133	0.079
In-group Trust	IT1	0.169	0.082	0.834	0.024	0.075	0.127	0.092	0.011	0.894
	IT5	0.209	0.089	0.807	0.091	0.044	0.138	0.116	0.105
	IT3	0.241	0.099	0.770	−0.030	0.027	0.068	0.099	0.127
	IT4	0.186	0.034	0.758	0.083	0.070	0.138	0.143	0.121
	IT2	0.186	0.173	0.751	−0.010	0.057	0.105	0.085	0.071
Behavioral Engagement	BE5	0.140	0.095	0.045	0.853	0.087	0.056	0.137	0.134	0.890
	BE4	0.116	−0.025	0.052	0.826	0.037	0.169	0.108	0.156
	BE1	0.145	0.187	0.096	0.783	0.098	0.006	0.081	0.094
	BE2	0.146	0.045	0.002	0.766	0.117	0.117	0.167	0.166
	BE3	0.140	0.047	−0.041	0.704	0.079	0.250	0.071	0.077
Cognitive Engagement	COE5	0.082	0.041	0.084	0.098	0.810	0.087	0.128	0.129	0.881
	COE3	0.151	0.032	0.045	0.048	0.770	0.139	0.095	0.104
	COE2	0.144	0.162	−0.001	0.120	0.768	0.096	0.142	0.133
	COE1	0.004	0.152	0.087	0.062	0.765	0.115	0.062	0.231
	COE4	0.004	0.150	0.053	0.080	0.760	0.121	0.121	0.179
Relationship Commitment	RC5	0.130	0.187	0.176	0.135	0.112	0.754	0.233	0.007	0.889
	RC2	0.169	0.187	0.151	0.107	0.178	0.744	0.180	0.110
	RC1	0.185	0.184	0.138	0.089	0.229	0.736	0.189	−0.005
	RC4	0.113	0.101	0.092	0.211	0.095	0.720	0.207	0.011
	RC3	0.230	0.144	0.128	0.123	0.072	0.702	0.230	0.070
Community Identification	CI5	0.159	0.133	0.123	0.167	0.109	0.145	0.754	0.031	0.878
	CI1	0.197	0.127	0.180	0.177	0.110	0.217	0.744	0.057
	CI3	0.140	0.130	0.011	0.104	0.193	0.176	0.742	0.022
	CI4	0.161	0.132	0.126	0.113	0.079	0.170	0.736	0.097
	CI2	0.078	0.130	0.157	0.054	0.116	0.260	0.725	0.068
Community Entitativity	CE4	0.084	0.027	0.043	0.137	0.233	0.028	0.078	0.812	0.865
	CE3	0.085	0.147	0.093	0.135	0.151	0.008	0.037	0.798
	CE1	0.145	0.097	0.181	0.168	0.161	0.119	0.032	0.787
	CE2	0.131	0.148	0.111	0.178	0.238	0.004	0.092	0.716
Eigenvalue		11.682	3.935	3.168	2.702	2.298	1.594	1.508	1.305
Variance Explained		29.204	9.838	7.92	6.754	5.746	3.986	3.771	3.263
Variance Cumulative		29.204	39.043	46.963	53.717	59.463	63.449	67.22	70.483
KMO and Bartlett’s Test					0.918
Bartlett’s Test of Sphericity					Approx. Chi-Square			11,080.371
					Df			780
					Sig.			0.000

.

To test correlations among community identification, relationship commitment, community entitativity, in-group trust, relationship satisfaction, and cognitive, emotional, and behavioral engagement, we conducted a correlation analysis using AMOS 26.0. As shown in

Table 4. Correlations between constructs.

	1	2	3	4	5	6	7	8
Community Identification	0.768
Relationship Commitment	0.645	0.786
Relationship Commitment	0.298	0.279	0.785
In-group Trust	0.420	0.446	0.337	0.793
Relationship Satisfaction	0.453	0.462	0.346	0.523	0.784
Cognitive Engagement	0.413	0.436	0.524	0.243	0.273	0.773
Emotional Engagement	0.394	0.445	0.304	0.286	0.064	0.342	0.828
Behavioral Engagement	0.420	0.408	0.438	0.203	0.394	0.312	0.230	0.790

Note: The bold font is the square root of the AVE.

, all average variances extracted (AVE) are bigger than 0.5. This indicates that the scale in this paper has good convergent validity. Moreover, all AVEs are bigger than the square root of the correlation coefficients, which means that the scale has good discriminant validity.

4.2. Hypothesis Testing

AMOS 26.0 was used to test the hypotheses. All results are shown in

Table 5. Results of hypotheses testing.

H	Path	Estimate *	S.E.	C.R.	p	Results
H1	Community Identification →Engagement	0.437	0.042	4.913	0.000	Supported
H2	Relationship Commitment →Engagement	0.504	0.049	5.505	0.000	Supported
H3	Relationship Commitment → Community Identification	0.555	0.064	9.924	0.000	Supported
H4	Community Entitativity → Community Identification	0.117	0.058	2.449	0.014	Supported
H5	In-group Trust → Community Identification	0.140	0.057	2.650	0.008	Supported
H6	In-group Trust → Relationship Commitment	0.279	0.056	4.773	0.000	Supported
H7	Relationship Satisfaction → Relationship Commitment	0.327	0.055	5.539	0.000	Supported
χ2 = 1404.131 (df = 727, p = 0.000), GFI = 0.863, AGFI = 0.846, RFI = 0.869, IFI = 0.937, TLI = 0.932, CFI = 0.937, RMSEA = 0.046, SRMR = 0.0740

Note: * standardized estimate.

and Figure 2. All hypotheses were supported. A good model fit is shown in Table 5 (χ² = 1404.131 (df = 727, p = 0.000), GFI = 0.863, AGFI = 0.846, RFI = 0.869, IFI = 0.937, TLI = 0.932, CFI = 0.937, RMSEA = 0.046).

As presented in Table 5, community identification positively affects engagement (b = 0.437, C.R. = 4.913, p = 0.000), and thus H1 is supported. Additionally, relationship commitment positively affects engagement (b = 0.504, C.R. = 5.505, p = 0.000), and thus H2 is supported. Relationship commitment positively affects community identification (b = 0.555, C.R. = 9.924, p = 0.000) and thus H3 is supported.

When group members feel a high level of community entitativity (b = 0.117, C.R. = 2.449, p = 0.014) and in-group trust (b = 0.140, C.R. = 2.650, p = 0.008), they are more likely to have community identification; therefore, H4 and H5 are supported. Members that have higher levels of in-group trust (b = 0.279, C.R. = 4.773, p = 0.000) and relationship satisfaction (b = 0.327, C.R. = 5.575, p = 0.000) show more relationship commitment. Therefore, H6 and H7 are supported.

5. Conclusions

5.1. Key Findings and Research Contribution

Although academic researchers agree that community engagement is made up of multiple components, including cognitive, emotional, and behavioral aspects [86], few scholars have measured these dimensions individually. Therefore, this study aimed to fill the research gap by taking a multi-dimensional perspective on community engagement, not just in terms of behavioral engagement but also non-behavioral aspects such as cognitive engagement and emotional engagement. In this study, we hypothesized that these three sub-dimensions are second-order factors that compose online community engagement. The purpose of this study was to explore the factors affecting consumers engagement in online sharing economy communities. Consumers who are deeply involved in online communities may perceive that they are a member of the community, which motivates them to engage in both direct (e.g., usage) and indirect activity (e.g., referrals and feedback). Research into the underlying causes of engagement is still in its infancy, though it is common knowledge that a customer-engaged community considerably boosts company profitability. Each member’s identification with and interpersonal links to their communities are the most determinative factors concerning whether they will engage in the online community. Specifically, we focused on community identification and relationship commitment on the sharing economic community engagement, and the factors that influence community identification and relationship commitment.

Our results show that consumers who perceive a high level of community identification and relationship commitment positively affect online community engagement in the sharing economy (H1, H2). As members work to situate themselves within the community, they take action to increase their sense of belonging, which is achieved through the voluntary performance of certain behaviors. According to interpersonal bonding theory, participation is one way to build intimate relationships within a community. As they work to maintain established relationships, individual users become increasingly willing to participate further in the community by sharing, helping, and comforting other members. As we observed, the more users contribute to the community, the more cost-prohibitive it becomes to switch to other communities. Each user’s sense of belonging to the community therefore increases. Accordingly, community being influenced by relationship commitment was identified (H3).

Understanding the factors that affect potential community identification and relationship commitment is crucial. Community identification is determined by two main factors: users and the community. The cohesiveness of the community as perceived by users is an important factor that affects their sense of belonging to the group. Greater cohesiveness results in stronger connections, leading to higher identification and a stronger sense of belonging to the community (H4). Establishing trust in relationships within the sharing economy community is also critical. Trust is a fundamental aspect of human connections, particularly in an online environment. It brings community members closer, thereby increasing their sense of belonging (H5–H6). Unlike cognitive satisfaction, relational satisfaction is a positive emotional state. High relational satisfaction motivates people to express their feelings more to the community, leading to a higher evaluation of the community and its members and a higher level of relationship commitment (H7).

5.2. Implications

First, we incorporated both community identity [87] and interpersonal relationship theories [88] to explore the determinants of community engagement in the sharing economy. This represents a new attempt made to study social psychology and relationship marketing, and it is hoped that the connection between the two will provide confirmation and support for future researchers. Second, this paper extends the existing research concerning online shared communities and user engagement behavior. We argue that engagement is not only behavioral but that cognitive and emotional engagement are also present in community engagement. Just as Fredricks [28] argues that engagement is a pluralistic concept that includes these components, our research led us to the same conclusion.

The practical implications of our findings highlight several factors that managers of online communities in the sharing economy should consider. First, our online community engagement scale is a useful tool for measuring consumer motivation to engage within communities, and our results suggest that improving members’ sense of belonging and relationship strength can increase engagement. Therefore, community managers should focus on improving these factors to incentivize involvement in the community. Second, entitativity, trust, member satisfaction, community identification, and relationship commitment are also critical factors in enhancing engagement. By understanding the reasons why community members engage, managers can develop strategic marketing actions to better connect with them. They should segment community members and develop communication efforts that aim to enhance the sense of belonging of members for marketing purposes. Third, in the context of online community engagement in the sharing economy, trust is an essential factor that facilitates the exchange of goods and services between users. The level of trust within a sharing platform can impact the willingness of users to engage in sharing activities. Finally, as an emerging online community, the sharing economy community requires active engagement from major users. The online community engagement scale can help managers to identify leading users and target them to spread messages.

5.3. Limitations and Future Research

This study has several limitations that can be addressed in future research. First, it is important to note that this was an exploratory study conducted solely in China. Given that China is a developing country, the findings may not necessarily generalize to other less developed or developed countries. To enhance the generalizability of the results, future studies should include a more diverse range of countries, such as the United States, and should conduct further investigations.

Second, the sharing economy is still a relatively new economic model, dating back to 2008; therefore, online sharing economy communities may not be well established, especially in China, where it is not yet mainstream [1]. However, this does not diminish the potential of the sharing economy, which has been identified by many scholars.

Third, while our study focused on community identification and relationships as measurements of community engagement, it is acknowledged that other factors such as community values and rewards may also play a role.

Finally, as the sharing economy continues to evolve and the number of online communities increases, our research model may need to be adapted. Nonetheless, given the potential of the sharing economy, it remains an interesting area for future exploration.