The Impact of User Benefits on Continuous Contribution Behavior Based on the Perspective of Stimulus–Organism–Response Theory

Abstract

With the rapid development of the Internet, enterprises have integrated internal and external innovation resources through the establishment of open innovation communities, guided users to participate in innovation activities, and promoted product improvement and development. Users’ continuous contribution behavior is a key factor for open innovation communities to achieve sustainable development, yet most communities do not collect enough data on them. This study investigates the mechanism of user benefits on continuous contribution behavior in open innovation communities based on the Stimulus–Organism–Response (S-O-R) theory, which creatively takes self-verification as a member of the organism (O). This was chosen to overcome the aforementioned issues. Based on the questionnaire data of 469 users in open innovation communities, the SEM method was applied to test the relationship between user benefits, self-verification, and continuous contribution behavior, and the moderating role of future work self-salience on self-verification. The empirical results show that user benefits positively affected both continuous contribution behavior and self-verification. Self-verification positively affected continuous contribution behavior and mediated the relationship between economic, functional, and self-fulfillment benefits and continuous contribution behavior. Meanwhile, future work self-salience positively moderated the relationship between these three types of benefits and self-verification. These findings provide a theoretical basis for the sustainable development of open innovation communities and guiding users to engage in continuous contribution behavior.

1. Introduction

With the rapid development of the Internet, the innovation paradigm has gradually evolved from traditional closed innovation to open innovation that coordinates internal and external resources. Open innovation is a process in which an enterprise introduces external innovation capabilities based on its internal innovation resources, and improves the innovation efficiency of its products/services through the complementarity of internal and external resources [1]. Open innovation exposes enterprises to more external resources, which can break the bottleneck of enterprise innovation and maintain the sustainability of business development [2]. Therefore, some enterprises have created open innovation communities to encourage external users to participate in the creation of company knowledge, products, and services, creating a novel method of communication between users and enterprises [3].

An open innovation community is a web-based online virtual community created by companies to collect innovative contributions from a wide range of users [4,5]. These communities allow businesses to ask questions online, and receive answers or ideas from knowledge contributors to ensure social and business coherence and maintain business sustainability. Open innovation communities provide a platform to bring together knowledge contributors and acquirers, attracting people with common interests or expertise to come together to exchange and share knowledge. Through demand feedback, idea provision, and knowledge sharing, a rising number of consumers actively participate in the development and improvement activities of products within the community [6,7]. Numerous enterprises have successfully utilized open innovation communities, combined with internal R&D, to facilitate the development of new products [8]. For example, LEGO has created a website community around its fans called “Lego Ideas”. The community brings together fans and creators from all over the world to imagine and propose new LEGO products, making it an open innovation community for “consumer co-creation”. Another example is Huawei’s comprehensive website, which serves as a platform for consumer communication and interaction. Online users can provide product ideas or feedback to help the company develop new products and services. Therefore, open innovation communities have received increasing attention from scholars as an important source of product and service innovation [9,10]. Many researchers believe that the continuous contribution behavior of users is a key factor for the long-term healthy development of the community [11]. However, the majority of open innovation communities still struggle with low user activity and limited user commitment to continuous contribution behavior. These problems make it difficult for communities to support the sustainability of business innovation. Therefore, exploring the motivations and influences behind users’ behaviors and effectively improving their contribution behaviors are important methods which open innovation communities can use to function and achieve business sustainability.

The focus of previous research has been on the influence of factors like material incentives [12], user trust [13], user reciprocity [13,14], and self-presentation [15,16] on users’ continuous contribution behavior, but the mechanisms underlying this influence and its effects have not been discussed. According to the Stimulus–Organism–Response (S-O-R) theory, users’ continuous contribution behavior is a response to the stimuli they experience from the perception of benefits. Studies have embedded the mechanism of user benefits on continuous contribution behavior into the S-O-R theory. However, most scholars regard user benefits as a component of the organism (O), reflecting the interaction process between external stimuli and user contribution behavior [17]. In an open innovation community scenario, user benefits, as the stimulus (S), can stimulate their intrinsic perceptions, prompting users to influence the output of subsequent behaviors through self-verification. Therefore, this study uses the S-O-R theory as the main framework to explore the influence of user benefits as a stimulus component on continuous contribution behavior. On this basis, this study tests the mediating role played by self-verification, which represents intrinsic organismal perception.

Additionally, Strauss et al. [18] proposed the concept of “future work self-salience” based on the “future work self”, which includes the degree of clarity and ease of visualization of the future work self [19]. As an individual motivational resource, future work self-salience guides the emergence and development of behaviors through a clear perception of the self. It also plays a significant role in influencing people’s behavior of actively seeking feedback and making contributions. When they obtain the necessary benefit stimulation in an open innovation community, users with high future work self-salience may quickly determine whether the level of acknowledgment given by themselves, others, and the community matches their expectations. This process helps to increase the level of self-verification, which in turn has an impact on continuous contribution behavior. According to research, future work self-salience is a key moderator of the connection between job meaning and self-verification [20]. However, few studies have explored how future work self-salience affects the relationship between user benefits and self-verification in the context of open innovation communities. By taking future work self-salience into account as a moderating variable, this study enriches and expands research on the influencing factors of continuous contribution behavior and deepens research on self-verification. The results of the study provide new ideas for exploring ways to increase ongoing user contributions and maintain community sustainability within open innovation communities.

Based on the above, this research mainly focuses on three objectives: (1) Analyze the influence mechanism of user benefits on continuous contribution behavior based on S-O-R theory. (2) Explore the mediating role of self-verification between user benefits and continuous contribution behavior based on self-verification theory. (3) Analyze the moderating role of future work self-salience between user benefits and self-verification based on the future work self-concept. Using questionnaire data from 469 users of an open innovation community, SEM methodology was applied for empirical testing. The findings partially support our model and confirm the influential role of self-verification and future work self-salience on continuous contribution behavior.

2. Literature Review

2.1. Collective Action

Collective action is a term that refers to instances in which people/communities come together and combine their efforts to help innovate or help each other to complete a beneficial action to complete a task [21]. Collective action emphasizes the importance of group collaboration as a source of open innovation and a driving force for the sustainability of open innovation communities. Further, when users contribute their ideas and knowledge to a community, collective action encourages continuous contribution behaviors by facilitating access to individual benefits [22].

The rapid development of Internet technology has directly contributed to the formation of online collective action [23]. Enterprises have established open innovation communities to connect internal and external user resources and improve innovation efficiency through resource complementarity [1]. Community users, as common stakeholders, are gradually becoming subjects of collective action. Open innovation communities provide a way for users to participate proactively, thus removing barriers to participation [24]. More people are receiving their information from communities. Accordingly, they become active proponents and innovators, helping companies to develop new products that meet their needs [25]. Community members collaborate with others to produce useful content and information [26] and generate collective intelligence through collective action, and the results obtained can improve the efficiency of open innovation in companies and push the boundaries of their innovation [27]. In addition, when many users contribute their knowledge and ideas, open and useful information creates a pool of resources in the community that attracts more people to join the collective action [22]. This information is more contextualized [28], localized, and up-to-date. When community members benefit from this information, they continue to provide expertise or contribute new ideas in return. In short, community users use their knowledge base [26] to help companies innovate their products and services through collective action.

In summary, open innovation communities become the de facto source of information and medium of communication and interaction for users. Online communities allow all users wishing to contribute ideas to work together and develop collective action capabilities to break innovation bottlenecks and maintain the commercial sustainability of their enterprises.

2.2. User Benefits

A synthesis of relevant studies by scholars at home and abroad found that users’ sense of gain greatly influences their continuous contribution behavior to open innovation communities. An important component of the sense of gain is perceived gain, which includes economic and functional benefits on the objective side as well as social and self-fulfillment benefits on the subjective side. Perceived gain serves as the foundation for users’ perceptions of value.

Maslow’s Hierarchy of Needs Theory suggests that a person’s current dominant need serves as the main driver of their conduct. Therefore, user benefit perception in the early stages of open innovation communities mostly concentrates on lower-level demands, such as economic rewards on an objective level and social benefits on a subjective level. Dholakia [29,30] explored three types of benefits that users can obtain in brand communities, namely social, recreational, and economic benefits. Zhang et al. [31] argued that online community users expect returns including economic and social benefits. According to Cai et al. [32] and Li et al. [33], reciprocity within open innovation communities offers members a larger spectrum of social trust and emotional benefits, and social capital influences users’ continuous involvement.

A portion of the users in the expanding open innovation community are dedicated to an organization’s vision and mission and are not looking for payment. Users of open innovation communities therefore also have higher-level requirements distinct from economic and social benefits, i.e., functional benefits connected to knowledge acquisition and self-fulfillment benefits that call for self-expression [34]. Functional benefits are the perceived benefits to users of valuable information and knowledge within the community, which are attributed to the consumer’s perspective of directly and beneficially supporting the open innovation community [30]. Dholakia et al. [30] state that functional drivers include functional income, uncertainty avoidance, information quality, and economic and regulatory incentives. In addition, self-fulfillment benefits, which are the sense of accomplishment and satisfaction that users gain through the act of contributing, likewise constitute an important part of the benefit stream [35]. Law et al. [36] argued that self-expression gains arise from the user’s internal motivation to derive pleasure and self-efficacy through knowledge contribution, which is essentially an affective gain.

Existing studies have been more comprehensive in their classification of user benefits in open innovation communities, covering economic and social benefits at the primary stage of the community, as well as functional benefits based on user perceptibility [37,38] and self-fulfillment benefits based on self-expression [39]. In the scope of research addressing open innovation communities, Tang et al. [40] classified users’ perceived gains into functional benefits from consumption on the objective side, as well as social and self-actualization benefits and hedonic gains on the subjective side. Yu et al. [41] explored how users’ willingness to interact online is influenced by the participation atmosphere and claimed that perceived gains are mostly divided into two categories: outward benefit and internal benefit. External benefit, which can also be referred to as material gain, is that which is obtained through the exchange of products, labor, or money. Internal benefits, which can be further broken down into psychological and social gains, include the social dignity and pleasure that come with a transaction. Based on the S-O-R theory, Ye et al. [42] offered value co-creation elements that determine user dominance, where four types of requirements can be combined in an open innovation community: the need for economic benefits, social benefits, the need for product knowledge, and the need for individual emotional needs. In conclusion, the study divided user benefits into four categories: social, economic, functional, and self-fulfillment.

2.3. Continuous Contribution Behavior

User contribution behavior is the act of sharing the user’s experience of using a product or service actively and consciously [43]. This behavior can propose innovative or feasible solutions to the problems of the company’s products or services [44]. Users who are willing to contribute are essential to the prosperity of open innovation communities and the sustainability of corporate innovation [11].

Based on this, some scholars have begun to notice that users’ participation in innovation activities and contribution of ideas is only the beginning of an open innovation community’s progress toward success. Users’ continuous participation is the key to the community’s success. Continuous participation refers to the activity of users repeating their will or behavior based on previous experience [45]. Continuous user participation promotes the generation of user continuous contribution behavior. Users’ continuous contribution behavior refers to the ability of users to maintain a certain frequency of contribution to an open innovation community over some time, reflecting the innovative ability of participating users in the community [46]. Chen et al. [47] argue that online user continuous contribution behavior is an additional temporal dimension to user contribution behavior. The implication is that users continue to participate in the community in various ways, such as continuing to submit ideas or posting comments and other behaviors. According to Wang et al. [17], user continuous contribution behavior includes users’ continuous content generation or creation behavior. This action includes users who continue to transfer information and knowledge they possess to other users through the community platform.

The majority of a recent study on users’ continuous contribution behavior is concerned with affecting factors. Wan and Cheng [48] found that both internal and external motivations affect users’ willingness to contribute knowledge. Users’ willingness to contribute knowledge and user satisfaction are important factors that motivate users to maintain continuous contribution behavior. In UGC short video communities, Wang et al. [49] discovered that user self-presentation and interpersonal interactions have an indirect influence on users’ continuous contribution behavior through community identity. Jin et al. [14] showed that through the moderating effect of point levels, users are willing to engage in continuous contribution behavior for reputation and reciprocity. Shen et al. [50] argued that the reputation system not only stimulates users’ sustained contribution but also positively influences community members’ acceptance level of knowledge sharing. The popularity effect and the quality and quantity effects of online word of mouth are significant in knowledge-sharing activities. In summary, in open innovation communities, the motivation possessed by users and the benefits obtained have a certain impact on continuous contribution behavior.

2.4. Future Work Self-Salience

Future work self-salience is a concept based on the future work self that includes the degree of clarity and ease of visualization of the future work self [18,19]. Future work self-salience can be used as a source of motivation to improve the self and create a need for future resources. Further, future work self-salience motivates individuals to proactively seek out existing resources and acquire other valuable resources.

According to Strauss et al. [18], the future work self can be divided into two categories: salience and granularity, with this study concentrating on the self-salience category. Individuals with high future work self-salience have a clear perception of their desired image in their future work. People are sometimes greatly motivated to engage in positive actions (such as contributing to the community) when they contrast the desired end state with the actual situation [51,52]. According to Zhu et al. [53], a high level of future work self-salience motivates people to take risks, broadens their creative thinking about future possibilities, and enhances their level of innovative behavior. At the same time, studies have shown that future work self-salience motivates individuals to maintain an open attitude, acquire more knowledge and skills, communicate positively, and share with team members. The process of integrating and interacting with knowledge and experience strengthens innovative behavior. In addition, future job self-salience brings intrinsic psychologically motivated resources that play an important role in individuals’ innovative behavior. Taber and Blankemeyer [54] argued that future job self-clarity motivates individuals to acquire career-related individual and work resources. Strauss et al. [18] and Guan et al. [55] argued that future work self-clarity is a motivational resource for individuals to change and improve themselves, which can stimulate the need for future resources. Future work self-salience then promotes individuals to proactively invest in existing resources in order to gain access to other valuable resources.

In conclusion, future work self-salience can give people incentives and intrinsic motivational resources that encourage novel behavior. This process is important for enhancing the sustainability of corporate innovation. Therefore, future work self-salience is also a source of motivation for individuals to seek feedback spontaneously.

2.5. Self-Verification Theory

Swann [56,57,58] created the self-verification theory, which contends that people seek and value feedback to support their perceptions of their self-concept. People often employ two self-verification strategies: (1) they seek information and behaviors from others to confirm their self-views; and (2) they tend to interpret and remember these interactions as behaviors that confirm their self-views. In other words, self-verification acts to some extent as a filter for how people perceive and recall encounters with others, including supportive interactions.

As self-views are typically relaxing and stress-free, the tendency to self-verify is rooted in the desire to maintain a sense of predictability and control. Individuals seeking self-verification typically receive information from members of their social network that improves their self-image and self-esteem. People who have a high self-concept typically show behaviors that are consistent with that perception by working hard, whereas people who have a poor self-concept typically limit their performance to be consistent with that perception [59]. In addition, research on self-verification theory has focused on people’s efforts to validate their selves; in this case, their unique qualities such as intelligence and social skills [60]. Self-verification has been linked to team processes, finding that team members perform better when their self-views are verified. Chen et al. showed that people endeavor to verify their individual views related to group membership or “collective self-view” [61,62].

According to the self-verification theory, people will always look for or produce responses that support their self-concept when they wish to create a sense of ownership or control over external information. At the same time, the self continually integrates signals from the outside world that support and reinforce the individual’s inherent self-concept. Therefore, it is inferred from the mechanism of the cognitive action process that the self-verification behavior of users in an open innovation community will determine whether or not they will continue to engage in continuous contributing behavior.

2.6. Stimulus–Organism–Response (S-O-R) Theory

The Stimulus–Organism–Response theory (SOR Theory) is derived from Watson’s Stimulus–Response theory. This theory focuses on the process and purpose of complete behavior to better reflect the user’s psychological changes and the associated reactions brought about. In this theory, stimulus (S) refers to factors that affect a person’s cognition and emotions, organism (O) refers to a person’s internal state being affected by an external stimulus, and response (R) refers to a person’s behavior due to a change in their internal state [63]. The S-O-R theory investigates how individuals construct their internal emotional perceptions in response to an external stimulus and how they act following those views.

The S-O-R theory was initially used to research consumer behavior in the conventional retail sector. In recent years, it has been extended to other areas in other businesses, including the community experience on websites. In the research of the traditional retail industry, most scholars focus on exploring the influence of stimulus from products, environment, user experience, and other aspects on the changes of users’ affective state, and then on user behavior [64]. With the diversification of online platforms, the online environment is more complex compared to that of the traditional retail industry. Users will be stimulated by various elements in various online environments, changing internal conditions and in turn driving various changes in user behavior and desires. As a result, the S-O-R theory is now mostly applied in studies to forecast user behavior; for example, on user participation in online brand communities, user interaction in online communities, shared value creation in social media communities, and user behavior and loyalty research in online commerce [65].

The S-O-R theory can provide a deeper understanding of an individual’s response to environmental stimuli. Therefore, this study attempts to build on the S-O-R theory to explore the mechanism of action that influences users’ continuous contribution behavior through self-verification in response to the stimulation of appropriate benefits in an open innovation community.

3. Hypothesis

3.1. User Benefits and Continuous Contribution Behavior

According to the social exchange theory, people who are exchanging resources with outside parties determine whether to continue the exchange by evaluating if the benefits and costs are acceptable [66]. Research has shown that the perceived benefit gained from user participation in open innovation communities can motivate users to produce continuous contribution behavior. This study categorizes user benefits into social, economic, functional, and self-fulfillment benefits to examine the effect of user benefits on continuous contribution behavior.

Social benefits are the social and emotional gains from interactions between members in an open innovation community [67]. Open innovation communities provide social benefits because by interacting with other community members, people feel intrinsically satisfied and integrated. When a user posts a message in an open innovation community seeking help from other members, there are dedicated community members who can help solve the problem [30]. Users actively participate in community interactions and exchanges during this process, which can strengthen ties among participants in the open innovation community and raise awareness of the social benefits. Users change from strangers to friends and have a stronger sense of identity towards that community, which in turn enhances their willingness to integrate and contribute behavior [67]. Additionally, users can fulfill their individual social needs while forming social bonds with other community members through communication. As a result, the social benefits can encourage them to participate more actively in community activities [67] and be more willing to help other members of the community [30]. These communication interactions increase the perception of membership of users within the open innovation community. Likewise, it enhances their integration into the open innovation community and facilitates the emergence of continuous contribution behavior. In conclusion, this research proposes the following hypothesis:

Hypothesis (H1a).

Social benefits have a positive impact on continuous contribution behavior.

Financial incentives are based on market-based feedback to users for their contribution, which in this paper is referred to as economic benefits, and mainly include virtual coins, vouchers, and cash rewards [68]. Individual users in open innovation communities often expect a certain amount of material reward for their efforts. Therefore, Ye et al. [42] contend that user participation in value co-creation behaviors in virtual brand communities is largely driven by economic demand. Users may stop contributing out of frustration if their expectations of economic benefits are not satisfied over a long period. However, this can be avoided with adequate compensation [69]. In open innovation communities, economic benefits such as virtual currencies and cash rewards can stimulate users to develop continuous contribution behavior. The reason for this is that people are willing to pay for what is right when a behavior is in their interest, especially financial interest. While financial benefits can increase the psychological cost to users in some contexts, user retention can be guaranteed if users are motivated through the right incentives. Similarly, encouraging users to participate in community interactions through comments and feedback can facilitate the creation of continuous contribution behavior and maintain the sustainability of business innovations. In summary, the following hypothesis is proposed:

Hypothesis (H1b).

Economic benefits have a positive impact on continuous contribution behavior.

Functional benefits come from users perceiving direct, informational support from the open innovation community that helps them solve problems and realize real benefits [30]. Users who post their ideas in the community will discover that many community members are very knowledgeable about specific aspects of the community. They have great insight into certain topics, such as product purchasing decisions, problems and solutions, tips on how to use the product, etc. [30]. Users who are “diving” can gain from active users actively posting information about product features and pricing, as well as their experiences with the product [70]. Users will develop a sense of responsibility to the community by sharing information after receiving the information they require from the open innovation community [71]. In open innovation communities, the more feedback and answers users receive to the questions they ask, the more knowledge contribution behaviors they show in the community [72]. Users can gain functional benefits from receiving service support, seek help, and help others [37], continuously generating contribution behavior. In summary, this study proposes the following hypothesis:

Hypothesis (H1c).

Functional benefits have a positive impact on continuous contribution behavior.

The user community of an open innovation community is distinctive in that certain members are dedicated to advancing the organization’s vision and objectives without expecting payment for their efforts. Therefore, users of open innovation communities have higher-level needs that distinguish them from other benefits. By helping the organization realize a lofty vision or objective, they hope to advance their sense of self-fulfillment. Users can contribute their knowledge and experience and thus enjoy obtaining and discussing details of product information. Users become more empowered as they engage with an open innovation community and share more product knowledge. This process enhances the user’s identity and status in the community, making it possible for them to become leading users and opinion leaders [73], which in turn motivates them to sustain their output contribution behavior. Leading users not only share their experiences and influence other users but also contribute ideas and suggestions for product improvement, thus influencing the company’s product development process. By influencing others or the organization during this process, users can boost their sense of achievement on a personal level, validating their sense of community membership. This sense of belonging keeps them contributing and favors the sustainability of the community. In conclusion, the following hypothesis is proposed:

Hypothesis (H1d).

Self-fulfillment benefits have a positive impact on continuous contribution behavior.

3.2. User Benefits and Self-Verification

Self-verification involves an individual’s overall assessment of their self-worth and competence. In open innovation communities, users make an overall assessment of their skills, importance, and value as community members [74]. Related studies have confirmed this self-verification effect several times using different methods and found that people prefer self-assessments and interaction partner feedback. These assessments and interaction partners verify their negative and positive self-views, and identify perceived successes and failures in the process of engagement [75,76]. In open innovation communities, users’ benefits for contributing behaviors signify their level of community recognition and have an impact on their sense of self-verification.

It has been argued that humans have a universal need for self-verification and that all individuals are equally motivated to self-verify [77]. On this basis, they will certainly seek self-verifying assessments. It has been found that people with negative self-views will prefer negative evaluations. Similarly, individuals with a positive self-view would show a corresponding preference for positive evaluations. In contrast, self-enhancement theory predicts that all people will prefer positive evaluations, regardless of their positive self-view. These opposing hypotheses have been supported by plenty of research [78].

Although the self-verification perspectives are more variable, they all show a positive bias. User benefits indicate the recognition of the contribution made by the user in a particular situation in an open innovation community. This affirmation gives users a sense of trust and, through feedback, gives them confidence that the community can create a favorable environment for their participation. User benefits stimulate positive emotions and self-evaluation [79]. These types of positive events give users a sense of achievement during task execution and promote self-verification. In summary, the following hypotheses are proposed:

Hypothesis (H2a).

Social benefits have a positive impact on self-verification.

Hypothesis (H2b).

Economic benefits have a positive impact on self-verification.

Hypothesis (H2c).

Functional benefits have a positive impact on self-verification.

Hypothesis (H2d).

Self-fulfillment benefits have a positive impact on self-verification.

3.3. Self-Verification and Continuous Contribution Behavior

Self-verification represents an effort to maximize one’s awareness of a coherent self and self-stability, and its success is critical to mental health. In open innovation communities, users gain self-verification by taking action. Further, the resulting self-verification also influences whether they continue to engage in continuous contribution behavior [59].

The self-verification theory contends that individuals receive, integrate, interpret, and adjust external information to shape their self-concept as it develops [80,81]. People seek and accept feedback that is consistent with their self-concept to gain a sense of control and the ability to anticipate the external world. As a result, users interact and connect with communities that affirm their image and leave those that differ from their image. At the same time, people are constantly receiving information from the external world to maintain and strengthen their initial self-perception. When community users’ self-views are validated, they show more active participation and interaction behaviors, leading to continuous contribution behavior.

Therefore, a high level of self-verification will promote continuous contribution behaviors and achieve behaviors that are consistent with the original intention of participating in the community, whereas users with a low level of self-verification will attenuate continuous contribution behavior. In summary, the following hypothesis is proposed in this study:

Hypothesis (H3).

Self-verification has a significant positive impact on continuous contribution behavior.

3.4. The Mediating Role of Self-Verification

The Stimulus–Organism–Response (S-O-R) theory states that when a person receives appropriate stimuli from the environment, they will form an internal emotional perception and then react accordingly. Therefore, in an open innovation community, a user will perform self-verification within themselves when they receive a beneficial incentive for their contribution behavior. When their self-opinion is validated, they will continue to create continuous contribution behavior.

First of all, users who have social benefit expectations want to develop friendships, support networks, and other connections through conversation. When users participate in community activities through interactive exchanges, the social benefits enable the verification of their self-views, which facilitates continuous contributing behaviors. On the contrary, if the innovation tasks given by the community do not include collaboration, it will result in users not feeling stimulated by social benefits. This process does not allow for internal self-verification, and does not participate in the innovation process.

Second, users who anticipate financial benefits desire to obtain monetary compensation, points, or honors for their contributions. Therefore, appropriate financial incentives may satisfy users’ demands and motivate them to keep contributing. However, if the community does not provide the promised rewards or value, the user creates a gap in expectations of personal benefits. This gap makes self-verification inconsistent with expectations. Users will be reluctant to invest time and effort in “useless” innovation activities, which is not conducive to continuous contribution behavior.

Furthermore, users who anticipate functional benefits seek out useful information resources from others through actions like information sharing. Participation and interaction in the community can lead to the acquisition of information about products or services. However, users will not be able to perceive the stimulation of functional benefits if the community does not retain high-quality “leading users” to share knowledge and ideas. As a result, users will leave the community without contributing to it through self-verification and discovering that they cannot achieve the desired results.

Finally, users who have high expectations for their worth desire to contribute their knowledge in order to live up to the company’s noble objective and value proposition. Users who are recognized by others and the community for sharing information can self-verify through self-actualization gains and further contribute to the community. However, if the community and other users do not provide timely feedback on the user’s self-verification feelings, they will stop their contributing behaviors. Users believe that there is a gap between the actual situation and the self-worth expectations. They are no longer willing to invest time and energy to participate in community innovation activities [82], which is not conducive to community sustainability.

Therefore, according to self-verification theory, in open innovation communities, gain stimuli provide positive feedback to users. As users internalize and integrate these perceived benefits into their self-perceptions, self-verification increases, prompting a willingness to engage in continuous contribution behavior within the community. In conclusion, this research proposes the following hypothesis:

Hypothesis (H4a).

Social benefits influence continuous contribution behavior through the mediating role of self-verification.

Hypothesis (H4b).

Economic benefits influence continuous contribution behavior through the mediating role of self-verification.

Hypothesis (H4c).

Functional benefits influence continuous contribution behavior through the mediating role of self-verification.

Hypothesis (H4d).

Self-fulfillment benefits influence continuous contribution behavior through the mediating role of self-verification.

3.5. The Moderating Role of Future Work Self-Salience

Future work self-salience is a source of motivation for people to change and develop, which is precisely divided into future work self-salience and imagination [18]. This factor stimulates the need for future resources primarily through clarity of self, which in turn motivates people to actively invest in existing resources and acquire other valuable resources [18].

Future work self-salience is an important prerequisite for influencing people to actively seek feedback and will have an impact on the self-verification process within the user. If users are clear about their future work when they receive benefits from their contributions to an open innovation community, they will be able to estimate the range of benefits they will receive from their contributions and assess the worth of the benefits they have already received. Users may determine whether the level of recognition given by themselves, others, and the community has fulfilled their expectations during the self-verification process, which raises the level of self-verification.

In contrast, a low level of self-salience about a user’s future work means that it is difficult for the user to develop an accurate perception of the range of benefits to be gained after the act of contribution has been made. After the user perceives the benefit stimulus, on the one hand, there may be a situation where the level of contribution behavior is low, but the user believes that they deserve a high level of benefit. Therefore, users are dissatisfied with the available benefits, which affects the self-verification process. On the other hand, there may be a situation where the level of contribution is high, but the existing benefits do not match the level of contribution. However, the low self-salience of the user’s future work makes it difficult for them to be motivated by the corresponding benefits, making the degree of self-verification lower. In summary, the following hypotheses are proposed in this study:

Hypothesis (H5a).

Future work self-salience has a significant positive moderating role on the relationship between social benefit and self-verification.

Hypothesis (H5b).

Future work self-salience has a significant positive moderating role on the relationship between economic benefit and self-verification.

Hypothesis (H5c).

Future work self-salience has a significant positive moderating role on the relationship between functional benefit and self-verification.

Hypothesis (H5d).

Future work self-salience has a significant positive moderating role on the relationship between self-fulfillment benefit and self-verification.

The conceptual model of the paper is shown in Figure 1.

4. Research Methodology

4.1. Sampling

We collected data from China to test our conceptual model. Due to the rapid development of the Internet, many Chinese companies have established communication websites as “online branding communities” that allow community users to participate in innovation activities. Users interact with each other to provide product ideas and help companies develop new products and services. These communities are known as open innovation communities. Among the many website communities, Huawei and Xiaomi successfully realized open innovation by establishing the Pollen Online Community and Xiaomi Online Community to connect the internal and external resources of the enterprises. They are typical examples of such communities. In addition, both Huawei’s and Xiaomi’s community users possess high motivation to participate in corporate innovation activities. Therefore, surveying the online users of these two communities can help us explore how to increase the continuous contribution behavior of users in open innovation communities, and provide references and lessons for other enterprises.

Data for the study were collected through online questionnaires sent to members of the Xiaomi Online Community (http:bbs.xiaomi.cn (accessed on 15 September 2022)) and members of the Huawei Pollen Club Community (https:club.huawei.com (accessed on 28 September 2022)). The survey mainly concentrated on the traits of users within the open innovation community as well as their continuous contribution behaviors. After three back-translations and pre-surveys, the research questions were further adjusted to form the final questionnaire. With a return rate of 60.27%, 487 questionnaires were collected after 808 questionnaires were sent out over 60 days in two parts. A total of 469 valid questionnaires were gathered, giving rise to an overall valid return rate of 58.04%, after 18 invalid questionnaires were eliminated for reasons such as omission or blatant outliers.

In total, 469 responses were received with the following characteristics: 49% male and 51% female, and there were 348 people aged from 18 to 40 years old accounting for 74.2% of the total sample. In terms of educational distribution, 21.7% of the total number of users in the sample had a bachelor’s degree; 104 users had a master’s degree, accounting for 22.2% of the total number of users in the sample; and 65 users had a doctoral degree or higher, accounting for 13.9% of the total number of users in the sample. In terms of the length of time users had been in the community, 292 users had been in the community for more than 1 year, accounting for 62.3% of the total number of samples, and 49 users had been in the community for more than 5 years, accounting for 10.4% of the total number of samples. To sum up, the target group of this questionnaire survey mainly focused on young and middle-aged community users with bachelor’s degrees or above and under 40 years old. They came from typical open innovation communities, had high education and rich experience in participating in the community, and were the main body of users who generated continuous contributing behaviors. Surveying these users can help to accurately test our conceptual model and provide the corresponding theoretical basis for the sustainability of open innovation communities and guide users to engage in continuous contribution behaviors.

Table 1. Demographic characteristics of the sample.

Item	Responses	Frequency	Percentage (%)
Gender	Male	230	49.00
	Female	290	51.00
Age	Under 18 years old	35	7.50
	18–25	97	20.70
	26–30	69	14.70
	31–35	93	19.80
	36–40	89	19.00
	41–45	50	10.70
	Over 46	36	7.70
Education	Lower secondary and below	18	3.80
	High school	33	7.00
	Junior college	147	31.30
	Undergraduate	102	21.70
	Master	104	22.20
	PhD	45	9.60
	Postdoc	20	4.30
Length of time in the community	Under 1 month	21	4.50
	1 month–6 months	53	11.30
	6 months–1 year	54	11.50
	1 year–2 years	100	21.30
	2 years–3 years	93	19.80
	3 years–5 years	99	21.10
	More than 5 years	49	10.40

shows the descriptive statistics for the sample.

4.2. Measures

We used previous measures from high-quality journal literature on economic benefits, functional benefits, social benefits, self-fulfillment benefits, self-verification, future work self-salience, and continuous contribution behavior to ensure the questionnaire’s validity and fit the open innovation community’s context. After three back-translations and pre-studies, the research questions were further adapted to form the final questionnaire, which consisted of two main parts:

The first part consisted of the basic conditions of the interviewees, including gender, education, age, and length of time in the community in four questions.

The second part dealt with measures of all constructs in the theoretical framework. Based on the previous section, the study constructed measurement scales for economic benefits (4 items), social benefits (4 items), functional benefits (4 items), self-actualization benefits (3 items), self-verification (8 items), future work self-salience (4 items), and continuous contributing behavior (7 items), with a total of 34 items. All items were measured on a seven-point Likert scale from (1) “strongly disagree” to (7) “strongly agree”. See

Table A1. Variable measurement question design.

Concept	Title
Social Benefits	1. the social aspect of being in an open innovation community is important to me.
	2. I meet like-minded others in the community.
	3. I enjoy the dialogue interactions in the open innovation community.
	4. I enjoy interacting with other open innovation community members.
Economic Benefits	1. I believe that there are economic benefits to be gained through my contributions in an open innovation community.
	2. I expect to receive some financial reward for the act of contributing in an open innovation community.
	3. I would like to be rewarded for my participation in the community (e.g., virtual community coins, brand partnerships).
	4. I will be more willing to contribute on an ongoing basis if I can derive more tangible benefits from my participation in the open innovation community.
Functional Benefits	1. The information provided by the open innovation community is valuable.
	2. The information provided by the open innovation community applies to me.
	3. The open innovation community provides information at an appropriate level of detail.
	4. There are some great features in the community to help me accomplish my tasks.
Self-Fulfilment Benefits	1.I want to gain a role or reputation as a key opinion leader or key opinion consumer in the community.
	2. I want to increase the trust and authority with which I post product-related information about a brand in the community.
	3. I have provided product-related information and advice in reviews posted in the community that has influenced other users’ product use or purchase decisions and enhanced my sense of personal fulfillment.
Self-Verification	1. I am honest about my habits and personality so that others in the community understand what I can contribute.
	2. When meeting new people, I will be truthful about my current abilities even if they may be less than idealized in the minds of others.
	3. Even if people recognize my limitations, I want the community to see what I think I can achieve.
	4. In the community, I try to be honest about my personality and style.
	5. I like to be myself and not pretend to be someone else.
	6. I prefer to let people know the real me in order to not expect too much from me.
	7.I’m willing to gain less to stay in the same community as the people in the community who already know me.
	8. When participating in an online innovation community, I try to find a place where people will accept me.
Future Work Self-Salience	1. I can conceptualize very early on the level of benefits (social, economic, functional, and self-fulfillment benefits) I can achieve in the future.
	2. I build a clear mental picture of the level of benefits I will achieve in the future.
	3. I can get a clear picture of my current level of competence.
	4. I can get a clear picture of how many corresponding benefits I can achieve with my current level of competence.
Continuous Contribution Behavior	1. I often community-share product/service experiences.
	2. I often community post product/service opinions.
	3. I often community-post solutions for products/services.
	4. I often post ideas for new products/services in the community.
	5. I often participate in product/service interactions with others in the community.
	6. I regularly comment on others’ discussions of new product/service ideas in the community.
	7. I often comment on other people’s questions about products/services in the community.

in the Appendix A for specific measurement questions.

4.3. Common Method Variance

There may have been a tendency to answer the related questions given that the questionnaires in this study were distributed to the target users over the same period and that the target users were all user groups in the open innovation community. The research employed Harman’s one-way analysis of variance to determine whether the questionnaire data had the issue of homogeneous variance to ensure the validity of the data source. The data from the study could be regarded as being free of issues with homogenous variance because the fraction of the extracted first principal component, which was 50.83%, did not surpass 60% when principal component factor analysis was conducted using SPSS 22.0 software.

4.4. Reliability and Validity

4.4.1. Reliability

In terms of the reliability of the measurement items, Cronbach’s Alpha (CR) was used to test the reliability of each construct. The study was tested using SPSS 22.0 and the results indicated (listed in

Table 2. Constructs, measurement items and sources.

Constructs	Items	Factor Load	CR	AVE
SOB	SOB1	0.924	0.968	0.882
	SOB2	0.920
	SOB3	0.927
	SOB4	0.985
ECB	ECB1	0.984	0.970	0.889
	ECB2	0.929
	ECB3	0.933
	ECB4	0.924
FUB	FUB1	0.977	0.962	0.865
	FUB2	0.904
	FUB3	0.912
	FUB4	0.926
SFB	SFB1	0.982	0.958	0.884
	SFB2	0.922
	SFB3	0.916
SVN	SVN1	0.982	0.982	0.872
	SVN2	0.935
	SVN3	0.918
	SVN4	0.925
	SVN5	0.921
	SVN6	0.925
	SVN7	0.929
	SVN8	0.935
FWS	FWS1	0.923	0.862	0.612
	FWS2	0.742
	FWS3	0.747
	FWS4	0.697
CCB	CCB1	0.978	0.979	0.869
	CCB2	0.931
	CCB3	0.926
	CCB4	0.934
	CCB5	0.917
	CCB6	0.922
	CCB7	0.917

Note: CR = composite reliability, AVE = average variance extracted.

) that the Cronbach’s Alpha scores for each variable dimension were greater than 0.7, which exceeds the threshold recommended by Hair et al. [83]. Moreover, we calculated the corrected item–total correlation (CITC), and the results were all greater than 0.5, indicating that there was a good correlation between the items analyzed and that the questionnaire had a high level of reliability. In conclusion, the results of the questionnaire in this study pass the reliability test.

4.4.2. Construct Validity

Construct validity, which includes convergent and discriminant validity, refers to the extent to which items accurately measure theoretical constructs. Average variance extracted (AVE) was used to evaluate the convergent validity between the variables, and an AVE greater than 0.5 means that the model converged well. The findings of the study’s specific tests are displayed in Table 2, in which each variable’s AVE value is greater than 0.612, showing that the measurement questions for the latent variables may effectively converge and satisfy the criteria for convergent validity.

For discriminant validity, we tested whether the square root of the extracted average variance (AVE) (i.e., the diagonal values given in

Table 3. Correlational matrix.

Construct	FWS	CCB	SVN	SFB	FUB	ECB	SOB
FWS	0.782
CCB	0.475	0.932
SVN	0.379	0.511	0.934
SFB	0.505	0.555	0.560	0.940
FUB	0.458	0.523	0.509	0.537	0.930
ECB	0.474	0.511	0.530	0.502	0.532	0.943
SOB	0.466	0.551	0.478	0.531	0.533	0.557	0.939

Note: The lower triangular data in the table is the correlation coefficient matrix between the latent variables, the diagonal boldface is the square root of each latent variable AVE, and the lower triangular facet is the Pearson correlation.

) was greater than the correlation between the constructs (i.e., the off-diagonal values given in Table 3). The results, as shown in Table 3, showed that the variance of each construct concerning its measure was greater than that of the other constructs. Thus, discriminant validity was ensured.

5. Results

This study was analyzed using Amos 22.0 software and a structural equation model to test the relationship between user benefits, self-verification, and continuous contribution behavior and to test Hypotheses H1, H2, and H3. The specific path coefficient tests are shown in

Table 4. Results of main path coefficients of structural equation model.

Path	Non-Standardized Coefficient	Standardized Coefficient	Standard Deviation	t-Value	Significance	Hypothesis Testing
SOB → SVN	0.100	0.118	0.035	2.864	**	PASS
ECB → SVN	0.227	0.271	0.035	6.504	***	PASS
FUB → SVN	0.180	0.202	0.037	4.858	***	PASS
SFB → SVN	0.304	0.342	0.037	8.140	***	PASS
SVN → CCB	0.134	0.145	0.044	3.066	**	PASS
SOB → CCB	0.201	0.257	0.032	6.226	***	PASS
ECB → CCB	0.116	0.150	0.033	3.485	***	PASS
FUB → CCB	0.148	0.180	0.035	4.254	***	PASS
SFB → CCB	0.208	0.252	0.036	5.694	***	PASS

Note: ** means the significance level is 0.01, and *** means the significance level is 0.001.

.

As shown in Table 4, social benefits, economic benefits, functional benefits, and self-fulfillment benefits of user benefits show a significant positive influence relationship on continuous contribution behavior and self-verification. Hypotheses H1a, H1b, H1c, H1d, H2a, H2b, H2c, and H2d pass the test; there is also a significant positive influence relationship of self-verification on continuous contribution behavior, and so Hypothesis H3 passes the test.

On this basis, we applied Hayes’ Bootstrap mediation effect analysis to further test the mediating role of self-verification. The results are shown in

Table 5. Results of the mediation role of self-verification.

Path	Point Estimate	Product of Coefficients		Bias-Corrected 95% CI		Percentile 95% CI
		S.E.	Z	Lower	Upper	Lower	Upper
SOB → SVN → CCB	0.013	0.008	1.625	0.002	0.036	0.000	0.033
ECB → SVN → CCB	0.031	0.012	2.583	0.011	0.059	0.009	0.056
FUB → SVN → CCB	0.024	0.011	2.182	0.007	0.053	0.006	0.050
SFB → SVN → CCB	0.041	0.016	2.563	0.013	0.078	0.012	0.076

. Through the test of Bootstrap = 5000, it was found that the confidence intervals between users’ economic benefits, functional benefits, self-fulfillment benefits, and continuous contribution behavior do not contain 0. Therefore, the study suggests that self-verification mediates the relationship between economic benefits, functional benefits, self-fulfillment benefits, and continuous contribution behavior. Hypotheses H4b, H4c, and H4d are valid.

The confidence interval of bias-corrected 95% CI and percentile 95% CI between social benefits and continuous contribution behavior are [0.002, 0.036] and [0.00, 0.033]. This suggests that self-verification has no mediating effect between social benefits and continuous contribution behavior. This result implies that when social benefits are judged to meet psychological expectations through self-verification in an open innovation community, additional social benefits create a sense of burden for users to continue to produce continuous contribution behaviors. Similarly, when users do not meet the psychological expectation of social benefits, they will feel frustrated and will not be able to produce continuous contribution behavior. Therefore, Hypothesis H4a was not tested.

In this study, the research hypothesis of the moderating effect was statistically tested by using SPSS 22.0 data analysis software through the hierarchical regression analysis method.

(1)

The moderating role of future work self-salience on the relationship between social benefits and self-verification

The regression results of Model 1 in

Table 6. Hierarchical regression test results for social benefits.

Variable	Self-Verification
	Model 1	Model 2	Model 3
Independent variable
SOB	0.467 ***	0.379 ***	0.379 ***
Moderating variable
FWS		0.200 ***	0.201 ***
Independent variable × Moderating variable
SOB × FWS			0.013
R2	0.218	0.250	0.251
Adjusted R2	0.216	0.247	0.246
F	130.285 ***	77.872 ***	51.850 ***

Note: *** means the significance level is 0.001.

show that social benefits have a significant positive effect on self-validation (β = 0.467, p < 0.001), which is consistent with the results of the AMOS 22.0 software path test. Also, this study used hierarchical regression analysis to test the moderating effect of future work self-salience.

Model 3 in the table shows that the p-values of the interaction terms between social benefits and future work self-salience are all above 0.05. This indicates that the moderating effect of future work self-salience between social benefits and self-verification is not significant, and Hypothesis H5a does not hold. This is because users will continue to engage in contributing behaviors once they experience the social rewards of doing so. However, the degree of future work self-salience is too high for self-knowledge and easy to imagine, which tends to weaken the degree of stimulation of social benefits to users and is not conducive to the enhancement of the self-verification process.

(2)

The moderating role of future work self-salience on the relationship between economic benefits and self-verification

The regression results of Model 1 in

Table 7. Hierarchical regression test results for economic benefits.

Variable	Self-Verification
	Model 1	Model 2	Model 3
Independent variable
ECB	0.523 ***	0.449 ***	0.464 ***
Moderating variable
FWS		0.169 ***	0.171 ***
Independent variable × Moderating variable
ECB × FWS			0.091 *
R2	0.274	0.297	0.305
Adjusted R2	0.272	0.294	0.300
F	176.040 ***	98.254 ***	67.875 ***

Note: * means the significance level is 0.05, and *** means the significance level is 0.001.

show that economic benefits have a significant positive effect on self-verification (β = 0.523, p < 0.001), which is consistent with the results of the AMOS 22.0 software path test. Also, this study used hierarchical regression analysis to test the moderating effect of future work self-salience.

From Model 3 in the table, it can be seen that economic benefits and future work self-salience have a significant marginal effect on self-verification (β = 0.091, p < 0.05). In other words, future work self-salience significantly and positively moderates the relationship between economic benefits and self-verification, and Hypothesis H5b holds.

In order to more intuitively reflect the moderating effect of future work self-salience, the study plotted the moderating effect schematically (Figure 2). When future work self-salience is low, the effect of economic benefits on self-verification is relatively slow. As future work self-salience increases, the slope of the dashed line in the graph increases significantly, confirming the positive moderating effect of future work self-salience.

(3)

The moderating role of future work self-salience on the relationship between functional benefits and self-verification

The regression results of Model 1 in

Table 8. Hierarchical regression test results for functional benefits.

Variable	Self-Verification
	Model 1	Model 2	Model 3
Independent variable
FUB	0.491 ***	0.409 ***	0.421 ***
Moderating variable
FWS		0.185 ***	0.193 ***
Independent variable × Moderating variable
FUB × FWS			0.111 **
R2	0.242	0.269	0.281
Adjusted R2	0.240	0.266	0.277
F	148.865 ***	85.853 ***	60.692 ***

Note: ** means the significance level is 0.01, and *** means the significance level is 0.001.

show that functional benefits have a significant positive effect on self-verification (β = 0.491, p < 0.001), which is consistent with the results of the AMOS 22.0 software path test. Also, this study used hierarchical regression analysis to test the moderating effect of future work self-salience.

From Model 3 in the table, it can be seen that functional benefits and future work self-salience have a significant marginal effect on self-verification (β = 0.111, p < 0.001). That is, future work self-salience significantly and positively moderates the relationship between functional benefits and self-verification, and Hypothesis H5c holds.

The moderating effect of future work self-salience is more intuitively captured in Figure 3. When future work self-salience is low, the effect of functional benefits on self-verification is relatively small. On the contrary, when future work self-salience is high, the slope of the dotted line in the figure increases, confirming the positive moderating effect of future work self-salience.

(4)

The moderating role of future work self-salience on the relationship between self-fulfillment benefits and self-verification

The regression results of Model 1 in

Table 9. Hierarchical regression test results for self-fulfillment benefits.

Variable	Self-Verification
	Model 1	Model 2	Model 3
Independent variable
SFB	0.544 ***	0.477 ***	0.486 ***
Moderating variable
FWS		0.143 ***	0.149 ***
Independent variable × Moderating variable
SFB × FWS			0.080 *
R2	0.296	0.312	0.319
Adjusted R2	0.295	0.309	0.314
F	196.750 ***	105.861 ***	72.458 ***

Note: * means the significance level is 0.05, and *** means the significance level is 0.001.

show that self-fulfillment benefits have a significant positive effect on self-verification (β = 0.544, p < 0.001), which is consistent with the results of the AMOS 22.0 software path test. Also, this study used hierarchical regression analysis to test the moderating effect of future work self-salience.

Model 3 in the table shows that self-fulfillment benefits and future work self-salience have a significant marginal effect on self-verification (β = 0.080, p < 0.05). That is, future work self-salience significantly and positively moderates the relationship between self-fulfillment benefits and self-verification, and Hypothesis H5d holds.

Figure 4 visualizes the moderating effect of future work self-salience even more. When future work self-salience is low, the effect of self-fulfillment benefits on self-verification is relatively small, but when future work self-salience is high, the slope of the dotted line in the figure increases, confirming the positive moderating effect of future work self-salience.

6. Discussion

In this study, we used a structural equation modeling approach to analyze the relationship between user benefits, self-verification, and continuous contribution behavior based on the Stimulus–Organism–Response (S-O-R) theory. Furthermore, we explored the moderating role of future work self-salience on the relationship between user benefits and self-verification. First, we classified user benefits into social [32], economic [31], functional [30], and self-fulfillment [35] benefits based on existing research. We employed empirical methods to discuss the impact of user benefits on continuous contribution behavior in open innovation communities. The statistical results support Hypothesis H1, that user benefits have a direct and significant positive impact on continuous contribution behavior, which is in line with our expectations. This result is also similar to the findings of Gerben et al. [84], who concluded that users’ self-benefits, creative challenges, and sense of achievement motivate them to participate in the community. Verhagen et al.’s study [85] also supports the view that users’ enjoyment of the community, benefits of interactions, and increased status in the community can all contribute to users’ contributing behaviors. Their arguments all support our view.

Second, the statistical results also support Hypotheses H2 and H3, and partially support Hypothesis H4, that self-verification partially mediates the relationship between user benefits and continuous contribution behavior. These findings are partially consistent with those of Wang et al. [17], Swann et al. [59], and Zhang et al. [79]. The difference is that our study is based on the Stimulus–Organism–Response (S-O-R) theory, which takes self-verification as a part of the organism (O), and explores the pathways through which user benefits (S) influence continuous contribution behavior (R) in an open innovation community. When users receive economic benefit, functional benefit, and self-fulfillment benefit stimuli, they can all promote continuous contribution behavior through self-verification. The main reason why social benefits do not promote continuous contribution behavior through self-verification may be that social benefits tend to make users feel burdened or frustrated after performing self-verification, which affects the output of continuous contribution behavior.

Finally, the statistical results of Hypothesis H5 also provide a complementary aspect to the application of future work self-salience in different contexts. Previous research has validated the positive role that future work self-salience plays on communication and innovation within team organizations [53,86]. Our study explores the moderating role of future work self-salience in an open innovation community. The findings suggest that future work self-salience positively moderates the effects of economic benefits, functional benefits, and self-fulfillment benefits on self-verification. These results are partially consistent with Gao et al. [87] and Zhang et al. [20], who found that higher future work self-salience facilitates individuals’ predictive and control abilities and enhances self-consistency. We initially thought that future work self-salience also plays a positive moderating role between social benefits and self-verification. However, high levels of self-clarity tended to diminish the degree of stimulation that social benefits provide to users, which is not conducive to the enhancement of the self-verification process, and therefore does not have a positive moderating effect.

6.1. Theoretical Implications

This paper contributes to the understanding of users’ continuous contribution behavior in open innovation communities based on the S-O-R theory. First, the S-O-R theory is extended by creatively including self-verification as a part of an organism (O) and analyzing the impact of user benefits on continuous contribution behavior. According to the results, both the degree of self-verification and continuous contribution behavior of users will significantly increase when they gain perceived benefits in terms of economic [42], social [67], functional [72], and self-fulfillment [73] aspects in an open innovation community. Self-verification also contributes positively to continuous contribution behavior [59]. This study encourages firms to establish a sound benefits incentive mechanism to provide lessons for improving the sustainability of open innovation communities.

Second, this paper also presents a more specific analysis to understand which type of user benefits can promote continuous contribution behavior through the level of self-verification of users. Prior research has mostly looked at user characteristics [88] and platform environments [89] to explain the psychological mechanisms that promote continuous contribution behavior. Studies have also explored the fact that within group organizations, individual performance is better for members who pass self-verification [61,62]. Our results confirm that self-verification likewise plays a positive partial mediating role in open innovation communities. The findings expand the boundaries of self-verification theory and provide a theoretical basis for how to improve users’ continuous contribution behavior.

Third, through this study, the mechanism of influence by which future work self-salience impacts on self-verification and further promotes continuous contribution behavior is revealed. Existing research has focused on the role of future work self-salience as an individual motivational resource to enhance personal innovation behavior [53,55]. This study extends the research context to open innovation communities and finds that future work self-salience has a partial positive moderating effect between user benefits and self-verification. This finding enhances the explanatory power of future work self-salience in different contexts and provides a theoretical basis for promoting users’ continuous contribution behaviors and the sustainability of open innovation communities.

6.2. Managerial Implications

We offer several management implications. First, companies and community stakeholders should focus on users’ perceived benefits in terms of economic, social, functional, and self-fulfillment benefits. Community administrators can further improve users’ continuous contribution behaviors by establishing a sound user revenue incentive policy to increase user stickiness. For example, the community can build a recommendation mechanism based on user profiles to match users with community members who share the same interests. This is not only conducive to the establishment of personal social networks, but also improves the access and quality of knowledge dissemination. In addition, for the creative ideas proposed by users, the community can give financial rewards or increase users’ earnings through public praise.

Second, focusing on users’ self-verification is of great relevance to increasing continuous contribution behavior. Communities can track user behavioral data to find out how they behave in response to revenue stimuli. This action can identify users with high self-verification ability. Based on this, community administrators can motivate such users to authentically express their ideas and creativity to facilitate the design and development of new products. In addition, user behavior data can help the community identify users who are not satisfied with the benefits after self-verification. The community can make timely strategic adjustments to ensure the sustainability of the community.

Finally, future work self-salience also significantly affects continuous contribution behavior within the community by moderating the relationship between user benefits and self-verification. In this regard, the community should establish a user identification mechanism to correctly identify groups with different levels of future work self-salience. The community can prioritize users with a high level of clarity about their future work to stimulate their earnings, so that they can generate continuous contribution behavior through self-verification. In addition, a differentiated management approach should be implemented to guide various types of users to engage in continuous contribution behavior in order to ensure the business sustainability of the enterprise.

7. Conclusions

Taking the Stimulus–Organism-Response (S-O-R) theory as the basic framework and combining the viewpoints of the self-verification theory, this study constructs a mechanism for the influence of user benefits on continuous contribution behaviors in an open innovation community. First, using Huawei Pollen Club community users and Xiaomi online community users as samples, this study explores the influence of community user benefits on continuous contribution behavior. The results show that user benefits have a direct and significant positive impact on continuous contribution behavior. On this basis, the study examined the mediating role of self-verification in this context. The results showed that economic benefits, functional benefits, and self-fulfillment benefits significantly influenced continuous contribution behaviors through self-verification. However, the mediating effect of self-verification between social benefits and continuous contribution behaviors was not significant. The main reason for this is that fewer social benefits tend to frustrate users, and additional social benefits create a sense of burden for users. Both situations are detrimental to the emergence of continuous contribution behavior. In addition, future work self-salience, an important psychological and motivational resource, was able to influence the degree of self-verification after users gained benefits. Therefore, the study also examined the moderating role of future work self-salience between user benefits and self-verification. The results showed that future work self-salience had a positive moderating effect on the relationship between economic benefits, functional benefits, and self-fulfillment benefits and self-verification. However, the moderating effect of future work self-salience between social benefits and self-verification was not significant, mainly because it weakened the degree of stimulation of social benefits to users and did not promote self-verification.

This study has many limitations that provide direction for future research. Firstly, although the sample and the community are representative, the data coverage is not comprehensive enough. Future research could collect data from more communities and conduct cross-community comparative studies. In addition, the sample was still limited to China, and future studies should collect data from different countries to explore universally applicable theories. Second, this study only examined the effect of user benefits on contribution behavior without considering the role of user costs. For example, implementation costs, psychological costs, and social costs. Subsequent studies need to explore this and analyze the effect of high or low user benefits on their contribution behavior based on user costs. Third, we only explored the moderating role of future work self-salience. Future research could examine the impact of other moderating factors on continuous contribution behavior, such as community culture, incentives, and brand influence, among others.