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Article

Building Sustainable Virtual Communities of Practice: A Study of the Antecedents of Intention to Continue Participating

by
Baltasar González-Anta
1,*,
Isabel Pérez de la Fuente
2,
Ana Zornoza
1 and
Virginia Orengo
1
1
Research Institute of Human Resources Psychology, Organizational Development, and Quality of Working Life (IDOCAL), University of Valencia, 46010 València, Spain
2
Department of Social Psychology, Faculty of Psychology, University of the Basque Country, 20018 Donostia-San Sebastián, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(21), 15657; https://doi.org/10.3390/su152115657
Submission received: 15 September 2023 / Revised: 26 October 2023 / Accepted: 1 November 2023 / Published: 6 November 2023
(This article belongs to the Special Issue New Trends in Organizational Psychology—2nd Edition)

Abstract

:
Virtual communities are essential in contemporary social and organizational domains. Their sustainability is largely propelled by members’ contributions, and yet the mechanisms for achieving significant participation remain ambiguous. Grounded in the Technology Acceptance Model, our primary objective is to identify the factors that may predict the intention to participate in a virtual community of practice; secondly, we aim to detect the most influential predictor(s) and the best model. In this paper, we conduct a cross-sectional study with a sample of 114 virtual community participants. Our multiple and weighted regression analyses reveal that technological, personal, and motivational factors sway participation intentions. Nevertheless, a combination of specific factors, interactivity, self-efficacy, and identification, are the most closely related to participation intention. This research offers valuable insights for organizations and community promoters, enhancing member retention and interaction stimulation and thereby constructing sustainable virtual environments through effective community design and management.

1. Introduction

Virtual communities (VCs) have revolutionized human relations through platforms like Twitter, LinkedIn, Reddit, or Instagram, with an ever-increasing use that has reached around 75% in developed countries [1]. VCs have transitioned our interactions into the digital sphere, becoming an essential component of our everyday routines, particularly with the widespread adoption of the internet and mobile devices and a growing emphasis after the COVID-19 pandemic [2,3,4]. They have also made their way into the job market, impacting organizational structures and the way people work and connect within organizations [4,5]. Moreover, amidst the challenges posed by the pandemic, virtual communities have emerged as indispensable and highly valuable platforms for facilitating and enhancing professional development opportunities [4,6]. The rise of the knowledge economy, driven by globalization and unprecedented challenges, has further necessitated virtual tools that enable communication, access to information, and adaptation to the needs of well-informed clients and other stakeholders [3,7].
This article focuses on Virtual Communities of Practice (VCoPs). VCoPs are collectives of professionals who gather on virtual platforms to exchange experiences, ideas, and solutions to work-related problems, ultimately enhancing their competencies [8,9,10]. VCoPs foster coordination, innovation, and knowledge exchange within organizations and among professionals [11]. They are based on face-to-face organizational communities of practice that have emerged as informal structures for sharing knowledge efficiently, circumventing the complexities of formal organizational hierarchies [12]. By breaking down geographical and time barriers, VCoPs serve as a tool for exchanging knowledge within multinational companies with diverse branches in different locations [13]. Therefore, VCoPs can play a pivotal role in revitalizing the lost office culture within an online setting, while also addressing some of the challenges introduced by new modes of work [14], such as virtual teamwork [15] or telework [14,16]. In VCoPs, participants will benefit from interactions with colleagues by acquiring unique knowledge, having the opportunity to socialize, and developing new skills [17].
However, despite their popularity, VCoPs face limitations in terms of their effectiveness, due to inactive participation from some members or poor quality interactions, which undermines their sustainability [18] and leads matrix organizations to experience great financial losses [19]. VCoPs rely on technological systems that optimize communication among members, making the success of the community contingent on members’ continued adoption and utilization of these systems [20,21]. Moreover, although personal interests drive member engagement, members also share a common purpose linked to broader organizational goals that require widespread and active participation.
In order to address this need, the current literature explores the factors that contribute to the success and longevity of VCoPs [22,23]. To do so, different theoretical approaches have been considered (e.g., Theory of Planned Behavior; UTAUT ([24,25]). In this study, we build on the Technology Acceptance Model (TAM) [26] to delve into a set of technological (navigation and interactivity), personal (self-efficacy and perceived individual benefits), and motivational (reciprocity and identification) factors that we hypothesize are related to participation intention in VCoPs. Moreover, we test the relative importance of each set of factors in participation intention. We aim to contribute to and broaden the corpus of recent research that has attempted to further understand the underpinnings, challenges, and inefficacies related to VCoP participation [4,6,27]. In the following sections, we briefly review each set of factors and further describe the hypothesized relationships. Then, we test the proposed hypotheses in a VCoP with participants from different NGOs using regression analysis. We finally discuss the results from the perspective of the TAM theory and prior research.

1.1. Sustainable VCoPs: A Complex Recipe

Creating a virtual platform in an organization is an easy task; some resources and basic technological interventions can easily prepare the groundwork. However, digitalizing organizational goals, relationships, and dynamics is a complex goal. For example, Ford and P&G ran into problems when pushing a digital transformation without proper management and awareness of the intertwined relationships between technology, people, and business nuances [28]. Therefore, if we want to obtain real benefits from the VCoP, we need a community that stands the test of time, nourishes its members and the broader context, and is not only effective but also leads to the wellbeing of different stakeholders. In sum, creating a sustainable VCoP requires more effort because there is little consensus about the best way to design and implement VCoPs in different fields [4].
The sustainability of VCs has been operationalized using different criteria, for example, through objective and subjective indicators such as the number of interactions or knowledge sharing. In this study, given the need for active members who participate in the VCoP frequently and effectively, sustainability is measured through intention to continue participating or participation intention. The term intention refers to the predisposition to perform a particular behavior [29]. This behavioral orientation estimates the probability that a given VCoP member will continue to interact with the community. TAM already highlights usage intention as a key predictor of actual use, and the current literature has continued to support the possibility of studying intention as a determinant of technology adoption and community participation [30,31]. Participation intention has been previously used as an indicator of the maintenance and success of VCs [32,33]. This follows along the lines of the growing literature that highlights that organizational—or, in this case, community—sustainability is related to members’ long-term orientation and participation, which are closely linked to their wellbeing in the group, their engagement and involvement, and their compliance beyond formal standards [34,35].
Particularly, VCoPs depend on the voluntary contributions of their members in order to be useful and thrive, given that members create content, resolve other members’ doubts, and indicate which topics should be addressed by the community [9,27,36]. Consequently, each individual decides whether or not to continue to be a member of the community and participate through his/her personal contributions. In summary, it is essential to study the factors that predict participation intention due to its critical role in VCoPs’ sustainability. In this vein, the TAM is an information systems theory that helps us to understand and evaluate behavioral intention to participate in a virtual community and propose the nomological network of the factors under study [19]. As Luo and colleagues (2019) stated, when “studying continuance intention, it is inevitable to consider the Technology Acceptance Model” [37] (p. 120). Therefore, this theory can help to develop effective and sustainable online communities. TAM includes a set of core factors (technological characteristics and perceived value for the user) and peripheral factors related to personal characteristics and motivations that vary as the model evolves [38]. Among the different conceptualizations of the model, relational factors such as subjective norms or social influence are also considered, for example, in TAM Model 3 [19,25]. This recent approach describes potential determinants of the behavioral intention to contribute [31]. Based on TAM [19,26], we posit that participation intention is a complex function of personal characteristics, motivations, and social influences, as well as system characteristics.
These broad factors need to be specified as variables. However, several particular variables have been related to participation intention in VCs. Various studies within the literature have also explored this in different communities, such as virtual learning communities, communities of interest, or virtual brand communities, among others [2,39,40]. In this vein, initial research on participation in VCs highlighted technological resources and “online friendship” as the main variables [41,42]. Later, individual determinants and social identities were analyzed, also mentioning the relevance of technological and motivational factors such as system quality and trust [41]. Previous research also considered information quality and accuracy [43]. Similarly, Zhang and colleagues (2017) found that the quality of the information and interactions affected the usefulness, which, in turn, determined users’ participation intention [44]. In communities of interest, system quality factors and personal factors such as perceived individual benefits are components that increase participants’ intention to continue [33].
The prior literature presented here tends to coalesce into sets of factors that could positively affect participation intention in VCoPs. The design of a technology system—technical or technological factors—may be a necessary condition for usability and ease-of-use and, thus, the development of the community based on participation. Once technological factors are met, psychological factors—both personal and motivational factors—are related to VCs’ flourishing and success. Although previous studies have highlighted the importance of contextual factors [27], and TAM reformulations have suggested them [19], in this study, their influence is considered to be indirect and diffuse because there is no full shared context (a depiction of the broad conceptual rationale explained can be seen in Figure 1).
Therefore, we attend to technological, motivational, and personal factors as the core elements to analyze for understanding participation. They are relevant factors to take into consideration in increasing the continuance intention of the members. However, previous research has addressed specific isolated factors (e.g., [33,45]), and there have been a variety of factors studied and methods used, but the possible symbiotic effect that may exist has not been analyzed [2,27]. Thus, there is still a research gap in the area of the joint effect of different sets of factors and, more importantly, their relative relevance in fostering participation intention in a VC in a professional context, a VCoP.

1.2. Technological Factors

Several studies have investigated how technology—the quality of the system—influences different results, such as perceived individual benefits, satisfaction, and participation intention in VCs [33,46], due to the infrastructural role played by technology in community development [4]. Based on TAM, the perceived ease-of-use of the VCoP (i.e., ability to effortlessly navigate, use the platform tools, and interact with other members) will lead to an increased willingness to take part in the community. Supporting this, Zheng and colleagues (2013) argued that technological factors are directly related to participation intention in VCs [33]. As Jones, Ravid, and Rafaeli (2004) mentioned, system quality factors such as navigation are important for facilitating users’ search process and reducing processing costs [47]. Recent case studies in educational settings also highlight the challenges that technological factors can produce in online settings [48,49]. In sum, an effective technical infrastructure promotes user participation and is a key element of the information exchange process in VCs [50].
Considering the context of this study, it is important to note that VCoPs are, overall, virtual spaces for working collaboratively. In them, information is shared and learned from others, and there is a need for reciprocity and debate. To do so, a prerequisite is that the technology has to work properly and fluently, providing resources for interaction and communication (e.g., direct inboxes, chatrooms, document sharing tools). These technological resources affect the way users evaluate their interactions with the information technology of the system [51]. In the end, a positive perception of the technological factors will facilitate members’ usage and maintain their continuance intention in VCoPs [46]. Throughout this study, technological factors are composed of navigation and interactivity.
Navigation is defined as the extent to which a user easily goes back and forth within the VC [33]. It implies that understanding the common functions and general characteristics of a technological tool is easy (user interface and experience). Proper navigation makes it possible to find information easily and take advantage of other resources, facilitating the willingness to continue to participate in the community [52].
Interactivity is understood as the ability of the technology to facilitate the establishment of more personal relationships among members [33]. In VCoPs, members are expected to exchange knowledge and engage in shared conversation. Therefore, interactivity is relevant because it is the element that manages the storage, control, processing, and transmission of information among users. Moreover, interactivity involves the interpersonal communication process by providing members with tools for online dialogue with each other [33,53]. In sum, interactivity is a key factor that promotes easy, enjoyable, and appreciated participation and satisfaction among users [33,52].
Therefore, based on previous results and the relevance of technological factors in promoting and encouraging VC members’ participation, we expect that the perception of technological factors (navigation and interactivity) will play a relevant role in VCoP’ members’ intention to continue.
To this end, we posit:
Hypothesis 1. 
The set of technological factors (navigation and interactivity) will have a positive relationship with the intention to continue.

1.3. Personal Factors

People’s intention to participate in VCoPs also involves different soft individual variables, usually called personal factors [27]. We focus on self-efficacy and individual benefits as personal factors. Personal factors have been suggested as an important avenue for future research on online knowledge sharing [30], and, particularly, technology self-efficacy and potential individual benefits (i.e., usefulness) are key components of the TAM model that can influence community participation [19].
First, self-efficacy is defined as the perceived probability of reaching a goal. It is a self-evaluation based on the perception of one’s own skills in order to share knowledge and, consequently, cooperate and achieve greater goals [54,55]. Prior research has mainly analyzed self-efficacy in face-to-face environments [56], and it has also been considered a key factor in online communities [57,58]. Thus, participants who perceive themselves to have a high probability of accomplishing their goals will cooperate more in VCoPs. Similarly, these participants will feel that their competences match the demands of the task and perceive themselves as having the skills to carry out community tasks. Therefore, they are also more likely to actively engage in community activities to achieve their goals [2]. Van Acker and colleagues (2014) also reported that self-efficacy is positively related to the intention to share in electronic systems [54]. In sum, users with high self-efficacy seem to be more motivated towards the community and more likely to collaborate [55,59].
In addition, participation in VCoPs is driven by a set of benefits or advantages that individuals obtain, e.g., learning, showing off, feeling part of something, or developing specific competences. There are different types of benefits, however; individual benefits play a key role in continuance intentions in VCs [29]. Perceived individual benefits are defined as the extent to which a member perceives personal gains for participating in a VC based on his/her experiences [10,33]. In other words, a member who obtains something s/he needs is more likely to continue to participate [33]. Prior studies within the literature have identified the importance of outcome expectations and benefits from the community as key drivers of participation [27]. In this context, participants’ intention to contribute increases if their needs are met and there are opportunities and resources in the community [2]. Likewise, considering the information system post-adoption research (e.g., [60]), personal factors—such as perceived individual benefits and self-efficacy—will profoundly determine continuance intention. Lin and colleagues (2009) [46] reported that self-efficacy and perceived individual benefits are positively linked to knowledge-sharing behavior, and that the latter is linked to community loyalty, which could increase participation intention in a VC. Considering the aforementioned rationale and research, we expect that personal factors (self-efficacy and perceived individual benefits) may be components related to the intention to continue in VCoPs.
Therefore, we hypothesize that:
Hypothesis 2. 
The set of personal factors (self-efficacy and perceived individual benefits) will have a positive relationship with the intention to continue.

1.4. Motivational Factors

Technological and personal factors are important in predicting the continuance intention in a VC, but motivational factors are also involved in this process [33]. Motivational factors in this study are reciprocity and identification. Based on the Social Exchange Theory [61] and TAM Model 2 and Model 3 [19,62], reciprocity and social influence (identification) will have a direct impact on participants’ eagerness to become involved in the VCoP.
Reciprocity, closely linked to the expected benefits and returns, is defined as peoples’ main beliefs that current participation (for example, in knowledge sharing or social interaction) in a VC leads to the future participation of other members [63]. Reciprocity is, therefore, understood as a social norm that involves members’ helping process, with the knowledge exchange being mutual, perceived, and fair, based on the mutual exchange among team members [61]. The existence of reciprocity, in turn, will foster interest in the VCoP and its members [64]. Empirical results have linked reciprocity to participation in different forms. For example, Wasko and Faraj (2000) found that members who shared knowledge in VCs believed in reciprocity [65]. Zheng et al. (2013) [33] proposed that motivational factors such as reciprocity might be involved in the intention to continue to participate. Norms of reciprocity have also been related to information-sharing behavior [66] and the development of VCs [67].
Another key motivational factor in fostering the intention to continue is identification with the community [57,68]. Identification is defined as individual recognition and feelings of belonging to a particular VC. Social identification, understood as considering oneself as part of the virtual community, is a core variable in virtual communities [69] that helps to cultivate participants’ engagement and behavioral intentions [68]. In this regard, multiple prior studies also found that identification is positively related to knowledge sharing [70,71]. Additionally, prior research reported that identification has a positive effect on the quality and quantity of the knowledge contributed and permanence in VCs [29,71]. Thus, previous studies within the literature have shown the importance of fulfilling this social need as a means of nurturing members’ participation [57]. We argue that reciprocity and identification are motivational factors that will affect the intention to continue in VCoPs.
Therefore, we expect that:
Hypothesis 3. 
The set of motivational factors (reciprocity and identification) will have a positive relationship with the intention to continue.
Finally, throughout the Introduction, we have built on theory and research to propose the relationship between these three sets of factors (technological, personal, and motivational) and participation intention in VCoPs. In other words, as Lin and Lee (2006) proposed, technology is the structure that provides information and a basis for social exchange in a virtual environment, but technological factors cannot work on their own and do not ensure the success of VCs [72]. They also pointed out that other kinds of factors, such as reciprocal and individual benefits, knowledge self-efficacy, and enjoyment in helping others, are related to knowledge-sharing behavior. Therefore, it is necessary to understand members’ interests, motivations, and preferences in order to boost the use of a virtual system [73] and promote contents within the VCoP that facilitate users’ participation [74].
Moreover, based on the literature, it is clear that each set of factors influences participation intention in its own way, and all three sets of factors should be important in fostering participation in VCoPs in the long term. Despite this, as mentioned above, a myriad of studies have analyzed individual specific factors, but as far as we know, no study has empirically investigated which dimension is the most important, which set of factors best predict intention to continue, or any synergistic effects that may occur. Consequently, exploratory research is required to examine this potential difference among factors or groups of factors because this field of inquiry is in the early stages [75]. Therefore, we propose an exploratory research question:
ERQ1. Is there a weight difference in the types of factors that predict intention to continue in VCoPs?
The resulting research model is presented in Figure 2, which summarizes the relationship paths we seek to test.

2. Materials and Methods

2.1. Sample and Procedure

Data were collected from 114 healthcare professionals from a Spanish NGO confederacy. This confederacy is a network of organizations that ensures compliance with the rights of people with intellectual and developmental disabilities in Spain. Their work focuses on improving the quality of life and social inclusion of their patients. To do so, they participate in an internal VCoP that gathers workers from different specific NGOs throughout Spain. Their participation was voluntary and linked to a research project that aimed to explore the impact of VC use on NGO workers.
Participants in the VCoP could create folders for document storage and sharing and manage a calendar, publish news, and initiate discussion threads. Furthermore, the platform enabled them to build a comprehensive profile, including a picture and personal and professional information. They were invited to participate and completed a confidential online survey posted on the online platform for two weeks. Fourteen responses were eliminated from the final analyses due to incorrect or incomplete answers.
Table 1 presents the demographic profile of the final respondents as well as the reasons for participating in the VC. As the table shows, the participants are mainly women (71.9%), young and middle aged (46.3% range from 21 to 40 years old), and have medium and mainly higher education (74.6% had at least a bachelor’s degree). The reasons for their participation are varied and represent the many possibilities VCoPs can offer.

2.2. Measures

Technological factors. The set of technological factors is composed of two variables, navigation and interactivity. Five of the survey items measured navigation. An example is “XXX provides tools for me to easily locate information (e.g., table of contents, use of categories, and index)”. Interactivity was evaluated with three items (e.g., “XXX allows me to interact with other users through various methods (e.g., discussion board, email, blog)”) developed by Zheng et al. (2013) [33]. Both measures used a 6-point Likert-scale ranging from one (“Strongly disagree”) to six (“Strongly agree”). Cronbach’s alphas were 0.94 for navigation and 0.88 for interactivity.
Personal factors in VCs. The set of personal factors is composed of two variables, perceived individual benefit and self-efficacy. Perceived individual benefit was measured using three items from a scale of perceived relative advantage. A sample item is “Sharing knowledge with members in this virtual community will increase my problem-solving capability”. Self-efficacy was measured with three items. A sample item is “I have confidence in my ability to provide knowledge that other members in this virtual community consider valuable”. Both measures derive from Lin et al. (2009) and present a 6-point Likert-scale ranging from one (“Strongly disagree”) to six (“Strongly agree”) [46]. Cronbach’s alphas were 0.93 for self-efficacy and 0.92 for individual benefits.
Motivational factors in VCs. The set of motivational factors is composed of two variables, reciprocity and identification. Reciprocity was measured using three items from Lin et al. (2009) [46]. The items were measured on a 6-point Likert-scale ranging from one (“Strongly disagree”) to six (“Strongly agree”). An example of these items is “I know that other members will help me, and so it’s obligatory and fair to help other members in this virtual community”. Identification was evaluated with three items by Chang and Chuang (2011) [70]. A sample item is “I have a feeling of togetherness or closeness in this virtual community”. Both scales were measured on a 6-point Likert-scale ranging from one (“Strongly disagree”) to six (“Strongly agree”). Reciprocity had a Cronbach’s alpha of 0.84, whereas Cronbach’s alpha for identification was 0.92.
Intention to continue. Intention to continue participating was measured using six items from the Zhao et al. (2013) scale [29]. An example of these items is “I intend to continue browsing information in XXX”. The items were measured on a 6-point Likert-scale ranging from one (“Strongly disagree”) to six (“Strongly agree”). Cronbach’s alpha was 0.94.

2.3. Analyses

In order to test Hypothesis 1 (navigation and interactivity will have a positive relationship with the intention to continue), Hypothesis 2 (self-efficacy and perceived individual benefits) will have a positive relationship with the intention to continue), and Hypothesis 3 (reciprocity and identification will have a positive relationship with the intention to continue), a multiple linear regression analysis was conducted. Moreover, to answer our exploratory research question (is there a weight difference in the types of factors that predict intention to continue in VCoPs?), we conducted a stepwise regression analysis to evaluate which set of predictor variables was more relevant in the intention to continue.
Analyses were conducted with SPSS statistics. First, we ran a preliminary analysis to check the validity of our scales. Then, we checked descriptive statistics and correlations. In addition, we also checked for assumptions of regression normality, linearity, multicollinearity, and homoscedasticity.
To examine the study hypotheses and exploratory research question, four models were proposed. First, three individual multiple linear regression analyses were performed for each group of factors; because of their different natures, there was one for technological factors, one for personal factors, and one for motivational factors. After these three individual models, a joint model of the six variables was carried out through stepwise regression analysis with forward selection in order to find out which variables had greater predictive values in the intention to continue.

3. Results

Table 2 presents descriptive statistics with the means and SDs, along with correlations between the studied variables. As Table 2 shows, the correlations among navigation, interactivity, self-efficacy, individual benefits, reciprocity, identification, and intention to continue were positive and statistically significant, except for the correlation between reciprocity and interactivity. Cronbach’s alpha coefficients were also above the 0.7 threshold.
The results of the multiple regression analysis are summarized in Table 3. Technological factors accounted for 13 percent of the variance explained by the model (R2 = 0.13, F2, 111 = 8.55, p < 0.001). The results for personal factors revealed that 20 percent of the variance was explained by the model (R2 = 0.20, F2, 111 = 14.14, p < 0.001). The motivational factor results showed that 20 percent of the variance was explained by the model (R2 = 0.20, F2, 111 = 14.03, p < 0.001).
Regarding Hypothesis 1, the regression analyses showed that navigation was not significant (β = 0.13, p = 0.27), but interactivity had a significant effect on the intention to continue (β = 0.27, p = 0.02). Thus, there was a positive relationship between technological factors and the criterion variable. Based on these results, H1 was partially supported.
The results for Hypothesis 2 show that both variables—self-efficacy (β = 0.30, p < 0.001) and individual benefits (β = 0.26, p = 0.004)—were significantly related to the intention to continue.
We also found support for Hypothesis 3. Both reciprocity (β = 0.21, p = 0.043) and identification (β = 0.30, p = 0.004) were significantly related to the intention to continue.
Moreover, to test the exploratory research question, we carried out a stepwise regression analysis. The results (Table 4) show that, of the six predictor variables, only three variables were left in the final regression model (identification, interactivity, and self-efficacy). As the table shows, Model 3 yields identification, interactivity, and self-efficacy, which explain 27 percent of the variance. In addition, according to these results, the adjusted R2 is higher in Model 3 than in Models 2 and 1.
To sum up, the best model to explain the intention to continue includes three variables that belong to the three groups of factors, identification (β = 0.25) from motivational factors, interactivity (β = 0.23) from technological factors, and self-efficacy (β = 0.22) from personal factors.

4. Discussion

The aim of the present article was to contribute to discovering the role of different factors that influence the sustainability of VCoPs. Using the Technology Acceptance Model as the theoretical basis for our research, we examined general factors and specific variables that shape participation intention in VcoPs.
Several findings and significant contributions are derived from this research. First, regarding the technology that acts as a platform for the VCoP, the results revealed that technological factors have a positive effect on the intention to continue in a VC. When a user perceives that there is easy information identification and sharing among members, s/he will be more likely to continue to contribute to the VC. Our technological factor results support Markus’ (2005) findings [50]. The technical infrastructure, in the specific area of interactivity and technological mechanisms, promotes members’ participation. This finding is of primary relevance because technology directly shapes users’ experience, and ease-of-use based on proper platform interaction may have a great influence on continued participation. Reliable and user-friendly infrastructure facilitates seamless interaction and access to community resources and knowledge, thus encouraging active and meaningful participation.
However, only interactivity had a significant effect on the intention to continue. Interactivity plays a central role because it makes users’ participation and the exchange among members easy and enjoyable [53]. In contrast, it is possible that proper navigation is considered a minimum standard nowadays and does not directly influence participation intention. These results do not agree with Zheng et al. (2013), whose results highlight navigation as the main variable in VC sustainability [33]. Despite this, these differences could be due to the type of VC analyzed. Whereas our study examined a VCoP, with workers evaluating their own community, their study was conducted in a community of interest, namely a travel forum [36]. Considering the sample characteristics, it is possible that workers prioritize interaction with their colleagues in order to reach the organization’s goals, whereas users of a community of interest consider usability to be an important predictor, given that they will feel less attached to the VC. Our results confirm Tseng’s (2015) findings, which showed that the interactivity function was related to the intention to continue participating in a virtual system [52]. In sum, the design of the technology and its functions are important factors in users’ continuance intention, especially the possibility of properly interacting with other members. These results align with recent studies within the literature highlighting the importance of technical factors in community development [6,27].
Moreover, the hypothesized positive effect of personal factors—self-efficacy and individual benefits—on continuance intention in VCoPs is supported. This finding is in line with most of the empirical studies developed in this area (e.g., [31,33,57]). Feeling able to use the technology successfully to achieve goals and obtain individual rewards leads to overall personal improvement from participating in the VCoP, thus encouraging members’ continued participation in the community and potentially increasing their wellbeing due to personal fulfilment, both from perceived competence and objective gains.
In addition, the study also confirms that motivational factors are important in predicting participation intention in VCoPs. The results of this study suggest that a mutual, perceived, and fair exchange and identification among members will favor continued participation in the VC. As proposed by TAM, identification and reciprocity—through social influence—are key influence mechanisms in the adoption of an information system [19]. This finding is consistent with Chan and Li’s (2010) argument that the reciprocity factor contributes to the development of VCs [67], and with Zhao and colleagues (2013)’s argument that identification greatly fosters the continuance intention in VC members [21]. Self-efficacy and social identification with the group are crucial for members of VCoPs because the positive relationship established, the collective identity, and the sense of achievement group members experience can make participation an enjoyable activity that motivates them rather than a duty.
Addressing a variety of perspectives regarding the predictive elements of participation in VCs [76], this article also tries to disentangle which set of factors better predict the intention to continue in VCoPs. Our exploratory analysis of the weight of each factor revealed that the model that best predicts the intention to continue is composed of identification, interactivity, and self-efficacy. Prior research has examined individual factors or bundles of factors with the same characteristics [33], but our results suggest that in order to foster continued participation in VCoPs, a synergistic combination of factors of different natures is necessary.
In sum, users of online virtual communities need to be able to connect, collaborate, and socialize properly, namely by means of proper interactivity. They need to perceive competence in their use of the community in a way that allows them to accomplish goals, namely self-efficacy. Finally, they need to create something beyond their work ties, and they need to truly become and feel part of the community, that is, build social identification. Our findings highlight the importance of personal identity and social links in the virtual community as drivers of community success.

4.1. Theoretical Contributions

This study contributes to the literature by integrating past studies [21,33] focused on only one group of factors that influence the intention to continue to participate in VCs. We also address the call to integrate and further develop the predicting factors of participation in VCoPs [6,27]. Hence, this study goes further by empirically exploring different groups of factors in the same study and sample. The studied relationships provide a more fine-grained view of the TAM Model 3 theoretical framework [19]. TAM has been considered a very useful model from an organizational and community development point of view because it addresses the components that may drive actual members’ use of a technology from a broad perspective. Despite this, the existing literature has hardly applied this theory in the context of organizational virtual communities, and the results have sometimes been contradictory [20]. Our results support the broad formulation of TAM [19,77], providing more evidence about the relevance of perceived ease-of-use (interactivity in the online community), self-efficacy, and usefulness of the community.
Moreover, we focus on a VCoP, a VC used in the job context to enhance communication and knowledge transfer among professionals in a company or profession [4]. This study context (participation in VCoPs) has been largely neglected compared to other contexts such as communities of interest, virtual brand communities, or social networking sites (e.g., [2,78]). Our findings show which factors are essential in a model that predicts participation intention in VCoPs, highlighting that there are differences, and that not only one element, but rather a combination of them, will truly help to develop VCoPs.
Despite this, the results also highlight identification—a motivational factor—as the most relevant element in fostering participation in VCoPs. In summary, this article theoretically contributes to the overall understanding of users’ intention to continue in VCoPs. We propose a model based on interactivity, self-efficacy, and identification as the three main factors that could influence participation, although individual benefits and reciprocity also show significant results and, thus, should be considered.

4.2. Practical Implications

This study offers practical implications for VC design, maintenance, and management in several different areas such as educational settings, business development, or governmental participative activities. First, community moderators and company managers who try to build up a VC need to attend to the software’s functions and characteristics, especially by providing tools for information exchange among members, i.e., “friends” lists, private in-boxes, etc., in order to foster users’ interaction. These technological features will offer the basis for further psychosocial development. This finding is of primary relevance in public entities that experienced struggles related to technology when suddenly faced with digitalization during the COVID-19 pandemic, for example, in the Italian education system [48,79]. Similarly, the case study conducted by Kamal et al. (2020) emphasized that, in virtual work settings, inadequate technological infrastructure poses a risk to outcomes, whereas factors such as interactivity and self-efficacy can mitigate the adverse effects of technical challenges [49].
Based on our findings, once the technology has been established and accepted by the user, the emphasis should be on personal and motivational factors. In this regard, organizational and industrial psychologists in companies will play an important role in working to maintain participation intention in VCs. Psychologists need to work as community coordinators so that, first, important goals are addressed and members feel confident about participating, and second, the community extends beyond the formal structure of the company. In other words, it is important for people who first attend the community to perceive high self-efficacy and identify with the community in order to ensure the sustainability of the VCoP. Community managers should, therefore, periodically check that interactions flow adequately and in a positive way, and that people can accomplish their personal goals without huge investments in terms of time or effort.
The outcomes of our research are essential in the post-pandemic context with the prevalence of virtual teamwork and new forms of work, such as platform work [15,80]. For example, collaborative platform work and the gig economy in general are highly dependent on virtual communities and online settings that may hamper psychosocial processes that spontaneously occur in face-to-face job contexts. Therefore, sustainable and effective VCoPs and other collaborative environments can be an organizational tool for emulating classical office environments, helping organizations in the long term. Consequently, the aim of these suggestions is to make it possible to design an intranet with specific characteristics that can guarantee the sustainability of the VCoP in the long term. Moreover, by fostering VCoPs, we aim to improve the return on investment of companies that innovate by implementing VCs. Companies that create a VCoP that functions well will obtain greater learning and professional development among their workers that will positively affect their day-to-day jobs. They will increase their wellbeing and engagement, not only with the community, but also with the organization and the broader context. At the same time, companies will reduce costs—e.g., coordination meetings, traveling costs—and obtain the benefit of knowledge value created by workers from different locations with different cultures and backgrounds. Workers from different regions and time zones will maintain their participation in the community and share, work, learn, socialize, and discuss different relevant topics for their jobs, ultimately benefiting the organization and the community’s sustainability through this spillover effect.

4.3. Limitations and Future Research

As in any empirical research, this study has some limitations that should be noted. The sample size was adequate because it was representative and close in number to the population (i.e., the total users of the VCoP). Despite this, the results are only generalizable to this particular type of VC, and future studies need to address whether the factors studied are able to predict participation in other types of VCs, comparing communities such as learning communities or virtual brand communities [2,36]. In addition, our sample was composed of members who work in non-profit organizations. Therefore, replicating this study in traditional business companies will offer a better idea about how users’ intention to continue operates in VCoPs.
We considered intention to continue participating as our dependent variable, which is a well-established approach in the IS adoption literature (e.g., [60]). However, as we mentioned in the Introduction, other variables have also been studied as proxies for sustainability [34,35]. In relation to this, we present a correlational design, asking about users’ intentions, and we do not evaluate the relational components that may be affecting their intentions, such as the role of team managers or the subsequent social links that may arise [6,27]. Future longitudinal studies could replicate this research, add interpersonal factors as predictors, and track users’ behaviors over time, thus measuring maintained participation in a VC. This may also help to consider contextual factors whose influence on the studied variables is complex, given that the organizational context of each participant was different. Future studies should analyze a VCoP operating in a single organization, which will make it possible to study variables such as the organizational culture and leadership structure.
Lastly, future research could explore the studied factors to find out how VCoP users interpret identification, interactivity, and self-efficacy in terms of behaviors. Therefore, we suggest carrying out qualitative studies in order to better understand and explore the factors that contribute to VCs’ sustainability.

5. Conclusions

There is a growing need to properly understand the sustainability of VCoPs. VCoPs have become a key tool in organizations, and their use is increasing around the world [4,27], but their utility and spread does not go hand-in-hand with their sustainability, in some cases backfiring in the companies that promote them. Further research needs to continue to disentangle the elements that can best assist in the process of maintaining and developing VCs in organizations and in for-profit companies, considering other sustainability variables. All in all, this study provides a broad view of the main factors that can influence members’ participation intention as an indicator of the sustainability of the community. Based on the TAM theory, we addressed the technological, personal, and motivational factors that have been individually considered, and we integrated them in a multifaceted model fueled mainly by identification, interactivity, and self-efficacy.

Author Contributions

Conceptualization, V.O., A.Z., I.P.d.l.F. and B.G.-A.; methodology, B.G.-A. and I.P.d.l.F.; validation, B.G.-A. and I.P.d.l.F.; formal analysis, I.P.d.l.F.; investigation, A.Z., V.O. and B.G.-A.; resources, V.O. and A.Z.; data curation, V.O., A.Z. and B.G.-A.; writing—original draft preparation, I.P.d.l.F., A.Z. and V.O.; writing—review and editing, B.G.-A., A.Z. and V.O.; visualization, B.G.-A., A.Z. and V.O.; supervision, V.O. and A.Z.; project administration, V.O. and A.Z.; funding acquisition, V.O. and A.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Spanish Agency of Economy and Competitiveness [grant numbers PSI2013-48509-P, PSI2016-79351-P].

Institutional Review Board Statement

Ethical committee review and approval is not compulsory for this study due to the non-experimental and non-health-related goal of the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data used during the study are available from the corresponding author upon request.

Acknowledgments

We would like to acknowledge “FEAPS-Plena inclusion” and associated centers that participated in the study.

Conflicts of Interest

The authors declare no known conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Auxier, B.; Anderson, M. Social Media Use in 2021. Available online: https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/ (accessed on 7 October 2023).
  2. Qu, L.; Liu, C.; Yin, J. Effects of Person–Environment Fit on Users’ Willingness to Contribute Knowledge in Virtual Brand Communities. Sustainability 2023, 15, 13476. [Google Scholar] [CrossRef]
  3. Kaya, T. The Changes in the Effects of Social Media Use of Cypriots Due to COVID-19 Pandemic. Technol. Soc. 2020, 63, 101380. [Google Scholar] [CrossRef] [PubMed]
  4. Shaw, L.; Jazayeri, D.; Kiegaldie, D.; Morris, M.E. Implementation of Virtual Communities of Practice in Healthcare to Improve Capability and Capacity: A 10-Year Scoping Review. Int. J. Environ. Res. Public. Health 2022, 19, 7994. [Google Scholar] [CrossRef] [PubMed]
  5. Cascio, W.F.; Montealegre, R. How Technology Is Changing Work and Organizations. Annu. Rev. Organ. Psychol. Organ. Behav. 2016, 3, 349–375. [Google Scholar] [CrossRef]
  6. Ghamrawi, N. Teachers’ Virtual Communities of Practice: A Strong Response in Times of Crisis or Just Another Fad? Educ. Inf. Technol. 2022, 27, 5889–5915. [Google Scholar] [CrossRef]
  7. Dulebohn, J.H.; Hoch, J.E. Virtual Teams in Organizations. Hum. Resour. Manag. Rev. 2017, 27, 569–574. [Google Scholar] [CrossRef]
  8. Gannon-Leary, P.M.; Fontainha, E. Communities of Practice and Virtual Learning Communities: Benefits, Barriers and Success Factors. ELearning Pap. 2007, 5, 1–14. [Google Scholar]
  9. Martínez-López, F.J.; Anaya-Sánchez, R.; Aguilar-Illescas, R.; Molinillo, S. Types of Virtual Communities and Virtual Brand Communities. In Online Brand Communities: Using the Social Web for Branding and Marketing; Springer: Berlin/Heidelberg, Germany, 2016; pp. 125–140. ISBN 978-3-319-24826-4. [Google Scholar]
  10. Wasko, M.M.; Faraj, S. Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice. MIS Q. 2005, 29, 35. [Google Scholar] [CrossRef]
  11. Blanchard, A.L.; Askay, D.; Frear, K. Sense of Community in Professional Virtual Communities. In Communication, Relationships and Practices in Virtual Work; Long, S., Ed.; Advances in Human Resources Management and Organizational Development; IGI Global: Hershey, PA, USA, 2010; pp. 161–176. ISBN 978-1-61520-979-8. [Google Scholar]
  12. Kirkman, B.L.; Cordery, J.L.; Mathieu, J.; Rosen, B.; Kukenberger, M. Global Organizational Communities of Practice: The Effects of Nationality Diversity, Psychological Safety, and Media Richness on Community Performance. Hum. Relat. 2013, 66, 333–362. [Google Scholar] [CrossRef]
  13. Carter, D. Living in Virtual Communities: An Ethnography of Human Relationships in Cyberspace. Inf. Commun. Soc. 2005, 8, 148–167. [Google Scholar] [CrossRef]
  14. Urien, B. Teleworkability, Preferences for Telework, and Well-Being: A Systematic Review. Sustainability 2023, 15, 10631. [Google Scholar] [CrossRef]
  15. González-Anta, B.; Orengo, V.; Zornoza, A.; Gamero, N.; Peñarroja, V. Collaboration and Performance in Virtual Teams with Faultlines: An Emotional Management Intervention. Rev. Psicol. Organ. Trab. Organ. Work J. 2020, 20, 1237–1246. [Google Scholar] [CrossRef]
  16. Peiró, J.M.; Todolí, A.; González-Anta, B.; Riera, I.; Salvador, A. El Teletrabajo En La Comunitat Valenciana 2022; University of Valencia-LABORA: Valencia, Spain, 2022; ISBN 978-84-09-48401-0. Available online: https://links.uv.es/UQjW0ja (accessed on 14 September 2023).
  17. Tseng, F.-C.; Kuo, F.-Y. A Study of Social Participation and Knowledge Sharing in the Teachers’ Online Professional Community of Practice. Comput. Educ. 2014, 72, 37–47. [Google Scholar] [CrossRef]
  18. Ma, M.; Agarwal, R. Through a Glass Darkly: Information Technology Design, Identity Verification, and Knowledge Contribution in Online Communities. Inf. Syst. Res. 2007, 18, 42–67. [Google Scholar] [CrossRef]
  19. Venkatesh, V.; Bala, H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis. Sci. 2008, 39, 273–315. [Google Scholar] [CrossRef]
  20. Nistor, N.; Baltes, B.; Dascălu, M.; Mihăilă, D.; Smeaton, G.; Trăuşan-Matu, Ş. Participation in Virtual Academic Communities of Practice under the Influence of Technology Acceptance and Community Factors. A Learning Analytics Application. Comput. Hum. Behav. 2014, 34, 339–344. [Google Scholar] [CrossRef]
  21. Zhao, L.; Lu, Y.; Wang, B.; Chau, P.Y.K.; Zhang, L. Cultivating the Sense of Belonging and Motivating User Participation in Virtual Communities: A Social Capital Perspective. Int. J. Inf. Manag. 2012, 32, 574–588. [Google Scholar] [CrossRef]
  22. Fang, C.; Zhang, J. Users’ Continued Participation Behavior in Social Q&A Communities: A Motivation Perspective. Comput. Hum. Behav. 2019, 92, 87–109. [Google Scholar] [CrossRef]
  23. McLoughlin, C.; Patel, K.D.; O’Callaghan, T.; Reeves, S. The Use of Virtual Communities of Practice to Improve Interprofessional Collaboration and Education: Findings from an Integrated Review. J. Interprof. Care 2018, 32, 136–142. [Google Scholar] [CrossRef]
  24. Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  25. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425. [Google Scholar] [CrossRef]
  26. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319. [Google Scholar] [CrossRef]
  27. Hernández Soto, R.; Gutiérrez Ortega, M.; Rubia Avi, B. Key Factors in Knowledge Sharing Behavior in Virtual Communities of Practice: A Systematic Review. Educ. Knowl. Soc. EKS 2021, 22, e22715. [Google Scholar] [CrossRef]
  28. Davenport, T.H.; Westerman, G. Why So Many High-Profile Digital Transformations Fail. Available online: https://hbr.org/2018/03/why-so-many-high-profile-digital-transformations-fail (accessed on 6 October 2023).
  29. Zhao, K.; Stylianou, A.C.; Zheng, Y. Predicting Users’ Continuance Intention in Virtual Communities: The Dual Intention-Formation Processes. Decis. Support Syst. 2013, 55, 903–910. [Google Scholar] [CrossRef]
  30. Alyouzbaky, B.A.; Al-Sabaawi, M.Y.M.; Tawfeeq, A.Z. Factors Affecting Online Knowledge Sharing and Its Effect on Academic Performance. VINE J. Inf. Knowl. Manag. Syst. 2022. ahead-of-print. [Google Scholar] [CrossRef]
  31. Faqih, K.M.S.; Jaradat, M.-I.R.M. Assessing the Moderating Effect of Gender Differences and Individualism-Collectivism at Individual-Level on the Adoption of Mobile Commerce Technology: TAM3 Perspective. J. Retail. Consum. Serv. 2015, 22, 37–52. [Google Scholar] [CrossRef]
  32. Sun, Y.; Fang, Y.; Lim, K.H. Understanding Sustained Participation in Transactional Virtual Communities. Decis. Support Syst. 2012, 53, 12–22. [Google Scholar] [CrossRef]
  33. Zheng, Y.; Zhao, K.; Stylianou, A. The Impacts of Information Quality and System Quality on Users’ Continuance Intention in Information-Exchange Virtual Communities: An Empirical Investigation. Decis. Support Syst. 2013, 56, 513–524. [Google Scholar] [CrossRef]
  34. Peiró, J.M.; Montesa, D.; Soriano, A.; Kozusznik, M.W.; Villajos, E.; Magdaleno, J.; Djourova, N.P.; Ayala, Y. Revisiting the Happy-Productive Worker Thesis from a Eudaimonic Perspective: A Systematic Review. Sustainability 2021, 13, 3174. [Google Scholar] [CrossRef]
  35. Stankevičiūtė, Ž.; Savanevičienė, A. Designing Sustainable HRM: The Core Characteristics of Emerging Field. Sustainability 2018, 10, 4798. [Google Scholar] [CrossRef]
  36. Henri, F.; Pudelko, B. Understanding and Analysing Activity and Learning in Virtual Communities. J. Comput. Assist. Learn. 2003, 19, 474–487. [Google Scholar] [CrossRef]
  37. Luo, N.; Zhang, Y.; Zhang, M. Retaining Learners by Establishing Harmonious Relationships in E-Learning Environment. Interact. Learn. Environ. 2019, 27, 118–131. [Google Scholar] [CrossRef]
  38. Scherer, R.; Siddiq, F.; Tondeur, J. The Technology Acceptance Model (TAM): A Meta-Analytic Structural Equation Modeling Approach to Explaining Teachers’ Adoption of Digital Technology in Education. Comput. Educ. 2019, 128, 13–35. [Google Scholar] [CrossRef]
  39. Lin, K.-Y.; Lu, H.-P. Intention to Continue Using Facebook Fan Pages from the Perspective of Social Capital Theory. Cyberpsychol. Behav. Soc. Netw. 2011, 14, 565–570. [Google Scholar] [CrossRef]
  40. Zhang, Y.; Fang, Y.; Wei, K.K.; Chen, H. Exploring the Role of Psychological Safety in Promoting the Intention to Continue Sharing Knowledge in Virtual Communities. Int. J. Inf. Manag. 2010, 30, 425–436. [Google Scholar] [CrossRef]
  41. Ridings, C.M.; Gefen, D.; Arinze, B. Some Antecedents and Effects of Trust in Virtual Communities. J. Strateg. Inf. Syst. 2002, 11, 271–295. [Google Scholar] [CrossRef]
  42. Chidambaram, L.; Jones, B. Impact of Communication Medium and Computer Support on Group Perceptions and Performance: A Comparison of Face-to-Face and Dispersed Meetings. MIS Q. 1993, 17, 465. [Google Scholar] [CrossRef]
  43. Wu, B.; Zhang, C. Empirical Study on Continuance Intentions towards E-Learning 2.0 Systems. Behav. Inf. Technol. 2014, 33, 1027–1038. [Google Scholar] [CrossRef]
  44. Zhang, M.; Liu, Y.; Yan, W.; Zhang, Y. Users’ Continuance Intention of Virtual Learning Community Services: The Moderating Role of Usage Experience. Interact. Learn. Environ. 2017, 25, 685–703. [Google Scholar] [CrossRef]
  45. Zhang, Z. Feeling the Sense of Community in Social Networking Usage. IEEE Trans. Eng. Manag. 2010, 57, 225–239. [Google Scholar] [CrossRef]
  46. Lin, M.-J.; Hung, S.-W.; Chen, C.-J. Fostering the Determinants of Knowledge Sharing in Professional Virtual Communities. Comput. Hum. Behav. 2009, 25, 929–939. [Google Scholar] [CrossRef]
  47. Jones, Q.; Ravid, G.; Rafaeli, S. Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration. Inf. Syst. Res. 2004, 15, 194–210. [Google Scholar] [CrossRef]
  48. Ferri, F.; Grifoni, P.; Guzzo, T. Online Learning and Emergency Remote Teaching: Opportunities and Challenges in Emergency Situations. Societies 2020, 10, 86. [Google Scholar] [CrossRef]
  49. Kamal, A.A.; Mohd, N.; Truna, L.; Sabri, M.; Junaini, S.N. Transitioning to Online Learning during COVID-19 Pandemic: Case Study of a Pre-University Centre in Malaysia. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 217–223. [Google Scholar] [CrossRef]
  50. Markus, M.L. Technology-Shaping Effects of e-Collaboration Technologies: Bugs and Features. Int. J. E-Collab. IJeC 2005, 1, 1–23. [Google Scholar] [CrossRef]
  51. Nelson, R.R.; Todd, P.A.; Wixom, B.H. Antecedents of Information and System Quality: An Empirical Examination within the Context of Data Warehousing. J. Manag. Inf. Syst. 2005, 21, 199–235. [Google Scholar] [CrossRef]
  52. Tseng, S.-M. Exploring the Intention to Continue Using Web-Based Self-Service. J. Retail. Consum. Serv. 2015, 24, 85–93. [Google Scholar] [CrossRef]
  53. Frey, K.; Lüthje, C. Antecedents and Consequences of Interaction Quality in Virtual End-User Communities. Creat. Innov. Manag. 2011, 20, 22–35. [Google Scholar] [CrossRef]
  54. Van Acker, F.; Vermeulen, M.; Kreijns, K.; Lutgerink, J.; van Buuren, H. The Role of Knowledge Sharing Self-Efficacy in Sharing Open Educational Resources. Comput. Hum. Behav. 2014, 39, 136–144. [Google Scholar] [CrossRef]
  55. Wang, D.; Xu, L.; Chan, H.C. Understanding Users’ Continuance of Facebook: The Role of General and Specific Computer Self-Efficacy. ICIS 2008 Proc. 2008, 168, 1–17. [Google Scholar]
  56. Alghamdi, A.; Karpinski, A.C.; Lepp, A.; Barkley, J. Online and Face-to-Face Classroom Multitasking and Academic Performance: Moderated Mediation with Self-Efficacy for Self-Regulated Learning and Gender. Comput. Hum. Behav. 2020, 102, 214–222. [Google Scholar] [CrossRef]
  57. Abedini, A.; Abedin, B.; Zowghi, D. A Framework of Environmental, Personal, and Behavioral Factors of Adult Learning in Online Communities of Practice. Inf. Syst. Front. 2023. [Google Scholar] [CrossRef]
  58. Cai, Y.; Shi, W. The Influence of the Community Climate on Users’ Knowledge-Sharing Intention: The Social Cognitive Theory Perspective. Behav. Inf. Technol. 2022, 41, 307–323. [Google Scholar] [CrossRef]
  59. Hsu, M.-H.; Ju, T.L.; Yen, C.-H.; Chang, C.-M. Knowledge Sharing Behavior in Virtual Communities: The Relationship between Trust, Self-Efficacy, and Outcome Expectations. Int. J. Hum.-Comput. Stud. 2007, 65, 153–169. [Google Scholar] [CrossRef]
  60. Jia, Q.; Guo, Y.; Barnes, S.J. Enterprise 2.0 Post-Adoption: Extending the Information System Continuance Model Based on the Technology-Organization-Environment Framework. Comput. Hum. Behav. 2017, 67, 95–105. [Google Scholar] [CrossRef]
  61. Cropanzano, R.; Mitchell, M.S. Social Exchange Theory: An Interdisciplinary Review. J. Manag. 2005, 31, 874–900. [Google Scholar] [CrossRef]
  62. Venkatesh, V.; Davis, F.D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef]
  63. Davenport, T.H.; Prusak, L. Working Knowledge: How Organizations Manage What They Know; Harvard Business Press: New York, NY, USA, 1998; ISBN 0-87584-655-6. [Google Scholar]
  64. Ardichvili, A. Learning and Knowledge Sharing in Virtual Communities of Practice: Motivators, Barriers, and Enablers. Adv. Dev. Hum. Resour. 2008, 10, 541–554. [Google Scholar] [CrossRef]
  65. Wasko, M.M.; Faraj, S. “It Is What One Does”: Why People Participate and Help Others in Electronic Communities of Practice. J. Strateg. Inf. Syst. 2000, 9, 155–173. [Google Scholar] [CrossRef]
  66. Pai, P.; Tsai, H.-T. Reciprocity Norms and Information-Sharing Behavior in Online Consumption Communities: An Empirical Investigation of Antecedents and Moderators. Inf. Manag. 2016, 53, 38–52. [Google Scholar] [CrossRef]
  67. Chan, K.W.; Li, S.Y. Understanding Consumer-to-Consumer Interactions in Virtual Communities: The Salience of Reciprocity. Adv. Internet Consum. Behav. Mark. Strategy 2010, 63, 1033–1040. [Google Scholar] [CrossRef]
  68. Huang, G.I.; Chen, Y.V.; Wong, I.A. Hotel Guests’ Social Commerce Intention: The Role of Social Support, Social Capital and Social Identification. Int. J. Contemp. Hosp. Manag. 2020, 32, 706–729. [Google Scholar] [CrossRef]
  69. Ferreira, J.J.; Fernandes, C.; Veiga, P.M.; Rammal, H.G. Ethics and the Dark Side of Online Communities: Mapping the Field and a Research Agenda. Inf. Syst. E-Bus. Manag. 2023. [Google Scholar] [CrossRef]
  70. Chang, H.H.; Chuang, S.-S. Social Capital and Individual Motivations on Knowledge Sharing: Participant Involvement as a Moderator. Inf. Manag. 2011, 48, 9–18. [Google Scholar] [CrossRef]
  71. Chiu, C.-M.; Hsu, M.-H.; Wang, E.T.G. Understanding Knowledge Sharing in Virtual Communities: An Integration of Social Capital and Social Cognitive Theories. Decis. Support Syst. 2006, 42, 1872–1888. [Google Scholar] [CrossRef]
  72. Lin, H.-F.; Lee, G.-G. Determinants of Success for Online Communities: An Empirical Study. Behav. Inf. Technol. 2006, 25, 479–488. [Google Scholar] [CrossRef]
  73. Hew, K.F. Determinants of Success for Online Communities: An Analysis of Three Communities in Terms of Members’ Perceived Professional Development. Behav. Inf. Technol. 2009, 28, 433–445. [Google Scholar] [CrossRef]
  74. Alvarez, H.; Ríos, S.A.; Aguilera, F.; Merlo, E.; Guerrero, L. Enhancing Social Network Analysis with a Concept-Based Text Mining Approach to Discover Key Members on a Virtual Community of Practice. In Knowledge-Based and Intelligent Information and Engineering Systems; Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 591–600. [Google Scholar]
  75. Casula, M.; Rangarajan, N.; Shields, P. The Potential of Working Hypotheses for Deductive Exploratory Research. Qual. Quant. 2021, 55, 1703–1725. [Google Scholar] [CrossRef]
  76. Dholakia, U.M.; Bagozzi, R.P.; Pearo, L.K. A Social Influence Model of Consumer Participation in Network- and Small-Group-Based Virtual Communities. Int. J. Res. Mark. 2004, 21, 241–263. [Google Scholar] [CrossRef]
  77. McAran, D.; Manwani, S. The Five Forces of Technology Adoption. In HCI in Business, Government, and Organizations: eCommerce and Innovation; Nah, F.F.-H., Tan, C.-H., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2016; volume 9751, pp. 545–555. ISBN 978-3-319-39395-7. [Google Scholar]
  78. Boulianne, S. Social Media Use and Participation: A Meta-Analysis of Current Research. Inf. Commun. Soc. 2015, 18, 524–538. [Google Scholar] [CrossRef]
  79. Guzzo, T.; Caschera, M.C.; Ferri, F.; Grifoni, P. Analysis of the Digital Educational Scenario in Italian High Schools during the Pandemic: Challenges and Emerging Tools. Sustainability 2023, 15, 1426. [Google Scholar] [CrossRef]
  80. Todolí, A.; Peiró, J.M.; González, B.; Riera, I.; Salvador, A.; Villajos, E. El trabajo en Plataformas en la Comunitat Valenciana; Cátedra en Economía Colaborativa y Transformación Digital; University of Valencia-LABORA: Valencia, Spain, 2021; Available online: https://links.uv.es/EcK32hK (accessed on 14 September 2023).
Figure 1. Conceptual rationale.
Figure 1. Conceptual rationale.
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Figure 2. Research model.
Figure 2. Research model.
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Table 1. Demographic profile (total respondents = 114).
Table 1. Demographic profile (total respondents = 114).
MeasuresItemsFrequencyPercentage
GenderMale3228.1
Female8271.9
Age21–30 years76.1
31–40 years4741.2
41/503934.2
more than 50 years2118.4
Education
level
High school21.8
Vocational training2723.7
Bachelor8473.7
PhD10.9
Reason for participating in the VCUpload required documents2421.1
Interact with other users2118.4
Create relationships with other users76.1
Look for solutions for workplace issues3429.8
Missing2824.6
Table 2. Descriptive statistics and Pearson’s correlations.
Table 2. Descriptive statistics and Pearson’s correlations.
MSD1234567
1. Navigation3.821.01[0.91]
2. Interactivity4.161.030.65 **[0.88]
3. Self-efficacy4.540.920.38 **0.21 *[0.93]
4. Individual benefits4.730.840.31 **0.33 **0.3 **[0.92]
5. Reciprocity4.810.860.31 **0.130.38 **0.42 **[0.84]
6. Identification4.321.160.36 **0.3 **0.43 **0.45 **0.55 **[0.92]
7. Intention to continue4.970.90.30 **0.35 **0.38 **0.35 **0.37 **0.41 **[0.94]
* p < 0.05, ** p < 0.01. Cronbach’s alpha coefficients for each measure appear between brackets.
Table 3. Multiple linear regressions per set of factors predicting intention to continue.
Table 3. Multiple linear regressions per set of factors predicting intention to continue.
BSEβR2F
Technological factors model 0.138.55 ***
Navigation0.120.100.13
Interactivity0.230.100.27 *
Personal factors model 0.2014.14 ***
Self-efficacy0.290.090.30 ***
Individual benefit0.280.100.26 **
Motivational factors model 0.2014.03 ***
Reciprocity0.220.110.21 *
Identification0.230.080.30 **
* p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4. Collective effect and stepwise regression analysis predicting intention to continue (3 models).
Table 4. Collective effect and stepwise regression analysis predicting intention to continue (3 models).
BSEβR2ΔR2F
Model 1. 0.17 ***0.1723.20 ***
Identification0.320.070.41 ***
Model 2. 0.23 **0.0616.52 ***
Identification0.260.070.34 ***
Interactivity0.220.080.25 **
Model 3. 0.27 *0.0413.44 ***
Identification0.200.070.25 **
Interactivity0.200.080.23 **
Self-efficacy0.210.090.22 *
* p < 0.05, ** p < 0.01, *** p < 0.001.
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González-Anta, B.; Pérez de la Fuente, I.; Zornoza, A.; Orengo, V. Building Sustainable Virtual Communities of Practice: A Study of the Antecedents of Intention to Continue Participating. Sustainability 2023, 15, 15657. https://doi.org/10.3390/su152115657

AMA Style

González-Anta B, Pérez de la Fuente I, Zornoza A, Orengo V. Building Sustainable Virtual Communities of Practice: A Study of the Antecedents of Intention to Continue Participating. Sustainability. 2023; 15(21):15657. https://doi.org/10.3390/su152115657

Chicago/Turabian Style

González-Anta, Baltasar, Isabel Pérez de la Fuente, Ana Zornoza, and Virginia Orengo. 2023. "Building Sustainable Virtual Communities of Practice: A Study of the Antecedents of Intention to Continue Participating" Sustainability 15, no. 21: 15657. https://doi.org/10.3390/su152115657

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