The relationship between online knowledge sharing and employee innovation: the role of task crafting and departmental support for innovation

Employee innovation refers to “the process, outcomes, and products of attempts to develop and introduce new and improved ways of doing things” (Che et al., 2019, p. 221). Employee innovation is recognized as a crucial factor in enhancing organizational efficiency, growth, and sustainable success (Anderson et al., 2014; Ekmekcioglu and Öner, 2024; Escribá-Carda et al., 2023). Consequently, scholars have explored and tested numerous antecedents of employee innovation, which can be classified into individual factors, task contexts, and social contexts (Anderson et al., 2014). Among these, prior studies emphasized the importance of knowledge in promoting employee innovation (Anderson et al., 2014; Le et al., 2024). Nonetheless, employees are unable to acquire and produce all the knowledge required to perform their everyday tasks (Che et al., 2019). As a result, they participate in knowledge-sharing events to stay updated and gain new insights (Thuan, 2020; Wang and Noe, 2010). According to Nguyen and Nham (2019), the rapid development of information technology has led to an increase in sharing knowledge online. Employees followed this trend because online knowledge sharing provides many benefits, including the ability to communicate a variety of knowledge more quickly and efficiently (Schau and Gilly, 2003). Recent studies focused on investigating online knowledge sharing as an intervening mechanism linking online platform use and organizational innovative climate (Pham et al., 2023), as well as leadership styles (Nguyen et al., 2024), with employee innovative performance.

Online knowledge sharing fosters a continuous learning and innovative culture (Ahuja and Thatcher, 2005), which enables workers to actively shape and optimize their tasks to suit their strengths and interests better (Henttonen et al., 2016). According to Li et al. (2020), the innovative process requires proactive behaviors in which employees proactively seek out useful knowledge to generate and apply new ideas and solutions. Although Le et al. (2024) investigated the impact of online knowledge sharing on employee innovation both directly and indirectly through a motivational mechanism, this mediator does not account for employees’ perceived job characteristics and overlooks proactive behaviors that foster employee innovation. Addressing the limitations in the innovation literature, we aim to explore and test the role of proactive behavioral mechanisms (e.g. task crafting) to gain a thorough understanding of the connection between online knowledge sharing and employee innovation, thereby responding to the call of previous research (Le et al., 2024). Furthermore, social and contextual influences may also impact the effect of online knowledge sharing on employee innovation (Woodman et al., 1993). However, previous studies only examined the moderating role of contextual influences (e.g. innovative climate) (Le et al., 2024). The moderating role of social influences, such as departmental support for innovation, has remained unexplored.

This article comprises two stages: a pilot qualitative study and a main quantitative study. Through this approach, this work makes two new contributions. First, this study provides one of the earliest insights into how task crafting bridges online knowledge sharing and employee innovation in response to the call of previous studies (Le et al., 2024). Instead of investigating the effect of online knowledge sharing on employee innovation from a motivational view (Le et al., 2024), this article explores how online knowledge sharing might foster employee innovation via proactive behaviors (task crafting). Second, this research is among the first articles to examine how departmental support for innovation influences the effect of online knowledge sharing on employee innovation, thereby responding to the call of previous studies (Le et al., 2024). Prior studies proved that contextual influences (e.g. innovative climate) moderated the nexus between online knowledge sharing and employee innovation (Le et al., 2024). This study focuses on investigating the role of social influences (e.g. departmental support for innovation) to gain a deeper understanding of the effect of online knowledge sharing on employee innovation.

The AMO framework is widely used in human resource management to explain the interplay between various factors that can influence employee performance (Boxall and Purcell, 2003). According to this theory, three main factors - ability, motivation, and opportunity - play crucial roles in determining employee performance (Blumberg and Pringle, 1982). Ability refers to the skills, knowledge, and capabilities that employees possess to perform their tasks effectively (Jiang et al., 2012). Motivation relates to the desire and commitment of employees to complete their tasks (Chang et al., 2012). Opportunity includes external factors such as the work environment, tools, and organizational support that help employees effectively leverage their abilities and motivation (Den Hartog et al., 2013). The AMO model emphasizes that for high performance to be achieved, all three factors must be present and mutually supportive (Boxall and Purcell, 2003). Moreover, subsequent studies affirmed that the interaction between ability, motivation, and opportunity positively impacts employee output (Appelbaum et al., 2000). Previous research applied the AMO framework to study employee engagement and job satisfaction (Alfes et al., 2013), employee innovation (Shipton et al., 2005), and knowledge sharing (Reinholt et al., 2011).

Online knowledge sharing involves the exchange of information, skills, and expertise through digital platforms, enhancing communication and learning within organizations (Cabrera and Cabrera, 2005). This process fosters collaboration among employees in problem-solving and idea development (Nursyirwan et al., 2023). Technologies such as forums, social media, and collaborative software facilitate this process, providing real-time updates that are crucial for maintaining an informed workforce and creating a collaborative culture that leads to innovation (Majchrzak et al., 2000; Panahi et al., 2013; Vuori and Okkonen, 2012a). The implementation of technological innovations has significantly changed how workers communicate and handle tasks, enabling quicker and more effective information sharing (Li et al., 2017). Online knowledge sharing enhances employee innovation by facilitating the exchange of knowledge (Liu et al., 2016). When employees share knowledge online, they not only contribute their insights but also acquire knowledge from others (Chiu et al., 2006; Nonaka and Takeuchi, 1995). This process of acquiring knowledge is crucial because it allows employees to incorporate various viewpoints and solutions. It promotes ongoing learning among staff, driving creativity and innovation (Davenport and Prusak, 1998; Henttonen et al., 2016). Knowledge acquired through online communications with others will help employees understand the root of issues and combine the necessary insights to create and apply solutions in the workplace (Le et al., 2024; Liu et al., 2016). Empirical research indicated that online knowledge sharing fosters collective intelligence and spurs innovative thinking (Hansen et al., 1999). Organizations that prioritize knowledge sharing exhibit higher creativity and innovation, as employees feel empowered to experiment and collaborate (McInerney and Koenig, 2011). Furthermore, sharing knowledge online enables employees to build trust with others (Kurniawanti et al., 2023; Nursyirwan et al., 2023; Rhee, 2017), which is a major driving force of individual innovation (Anderson et al., 2014). Therefore, online knowledge sharing may boost employee innovation as posited by the AMO framework (Blumberg and Pringle, 1982).

Task crafting is defined as the proactive behavior where employees reshape their job tasks to better align with their skills, interests, and values (Wrzesniewski and Dutton, 2001). This research describes task crafting as a fundamental process connecting online knowledge sharing and employee innovation. Employees who regularly participate in online knowledge exchanges are continually exposed to best practices and innovative ideas from their colleagues across the organization (Vuori and Okkonen, 2012b) and gain new insights that enhance their understanding of job roles and potential areas for improvement (Cabrera and Cabrera, 2005). In an environment that encourages online knowledge sharing, employees are provided with opportunities to gain new skills and information and apply the shared knowledge to their specific job responsibilities, regardless of their role or seniority within the company (Hau et al., 2013). The ease of access to information significantly enhances employees’ performance by reducing the time and effort needed to find solutions to work-related challenges, thereby increasing efficiency in task crafting (Wasko and Faraj, 2005). Moreover, online knowledge sharing fosters a culture of continuous learning and innovation (Ahuja and Thatcher, 2005), which enables employees to actively shape and optimize their tasks to better align with their strengths and interests (Henttonen et al., 2016).

Task crafting subsequently influences employees’ innovation (Berg et al., 2013). By proactively tailoring tasks to fit their strengths and passions, employees become more engaged and motivated (Petrou et al., 2012). This increased engagement motivates employees to understand the underlying workplace issues, develop effective strategies to address those problems, and also embrace risks to innovate new approaches (Tims et al., 2012). In other words, task crafting influences employee capacity to generate innovative solutions (Wrzesniewski and Dutton, 2001). Additionally, task crafting encourages continuous learning and skill development (Bipp and Demerouti, 2015), which enhances individual capabilities in innovation (Bakker et al., 2012). Also, this learning process leads to a better job-person fit, resulting in higher levels of job satisfaction and performance, which are essential for sustaining innovative behaviors (Rudolph et al., 2017). Possessing significant task-crafting skills enables employees to understand the underlying issues and apply their knowledge to develop and execute innovative ideas and strategies (Lyons, 2008). Therefore, this study predicts hypothesis 2.

Departmental support for innovation refers to the extent to which departments within an organization provide resources, encouragement, and leadership to facilitate and promote innovative behaviors among employees (Amabile, 1996). According to the AMO framework, employee performance may be enhanced through the interaction between opportunity and ability (Blumberg and Pringle, 1982). Opportunity is described as “the particular configuration of the field of forces surrounding a person and his or her task that enables or constrains that person’s task performance and that is beyond the person’s direct control” (Blumberg and Pringle, 1982, p. 565). Ability refers to “the knowledge, skills, and experience needed to perform a task” (Chang et al., 2012, p. 928). In the context of this study, the interaction between departmental support for innovation and online knowledge sharing may enhance employees’ innovative performance. This is because departmental support for innovation denotes a specific type of opportunity, while online knowledge sharing improves employee skills and abilities (Blumberg and Pringle, 1982; Su et al., 2022; Thuan and Thanh, 2020).

Moreover, a high level of departmental support is characterized by the provision of sufficient resources, encouragement, and a supportive environment that cares about employees’ well-being and values their contributions (Eisenberger et al., 1986). A highly supportive workplace helps employees enhance their perceptions of support, develop their capabilities, and increase their potential for creative work (Aldabbas et al., 2023). In the innovative process, employees in an environment with high departmental support for innovation receive comprehensive facilitation and support, which enables them to exchange knowledge online, transform it into valuable ideas as well as proactively seek new approaches, and effectively implement them at work (Chen et al., 2019; Hansen et al., 1999). Conversely, in settings with minimal departmental support for innovation, the lack of encouragement and resources may inhibit employees’ motivation to participate in online knowledge sharing, ultimately constraining their capacity to develop and implement innovative ideas (Nonaka, 1994; Ryan and Deci, 2000; Zhou and George, 2001).

Empirical studies demonstrated that support for innovation from leaders and coworkers may strengthen the effect of knowledge sharing on employee innovation. Mura et al. (2013) concluded that interaction, encouragement, and support from coworkers strengthen the association between knowledge sharing and innovative work behavior. Islam et al. (2024) concluded that leadership focusing on vision, innovation, and risk-taking moderates the relationship between knowledge sharing and innovative work behavior. When people in departments facilitate and promote innovative behaviors, it creates an environment where individuals share and collect knowledge that may help them be more innovative (Islam et al., 2024). Therefore, this study predicts hypothesis 3.

Figure 1 shows the conceptual model of this research.

Vietnam’s economy has transitioned from a centrally planned system to a market-driven one, necessitating that employees develop and implement more innovative and valuable products and services to remain competitive in an increasingly challenging environment (Le et al., 2024). Moreover, the innovative culture in Vietnamese organizations is high, which has facilitated employees to generate and implement novel and useful ideas at work (Tho and Trang, 2015). Additionally, companies in Vietnam have leveraged information technology to enable employees to share information, opinions, knowledge, ideas, and solutions online, facilitating their adoption of digital trends and enhancing innovative outcomes among employees (Le et al., 2024). According to Bledow et al. (2013, p. 437), “a heterogeneous sample of full-time employees in professional jobs to allow for generalization across jobs and industries.” Building on previous studies in top journals (Bledow et al., 2013; Gong et al., 2013), this study collected data from workers across various industries, including information technology, tourism, education, banking, manufacturing, and telecommunications, to facilitate generalization across different jobs and industries. These industries were chosen because employees working in these organizations need to generate and implement more innovative ideas at work to improve their goods and services, thereby competing with other businesses (Bledow et al., 2013; Gong et al., 2013).

This study includes two stages: a pilot qualitative study and a main quantitative study. First, the authors conducted a pilot qualitative study in Ho Chi Minh City, Vietnam. In-depth interviews were used to collect information from experts. Theoretical sampling, a method employed in qualitative research (Coyne, 1997), was used with a saturation point of 14 experts. Experts were invited to provide feedback on any indicators they found unclear or difficult to understand. Based on the experts’ comments, we revised the Vietnamese questionnaire to ensure that the meaning of the indicators aligns with the context in Vietnam. Second, the main quantitative study was undertaken in Vietnam through Google Forms. The convenience sampling method was employed to collect data as it is the most commonly used approach for data collection in social science research (Etikan et al., 2016). Moreover, ethical dilemmas are rarely present with convenience sampling because it is easy to use, cost-effective, time-efficient, and more convenient for recruiting participants (AlMulhim and Mohammed, 2023). To distribute the Vietnamese questionnaire to full-time employees, we contacted friends, coworkers, human resource managers, chief executives, and other managers, explaining the purpose of this research to obtain their support. Each survey included a cover letter that explained the purpose of the research. Participants were assured that their information would remain private and anonymous. Furthermore, they were informed that there were no right or wrong answers and that participation was entirely optional. After two months, we received 363 rated questionnaires. Thirty-eight rated questionnaires were removed due to incomplete information. Finally, the final sample size of this article was 325 questionnaires. To mitigate biases, this article collected data from several industries information technology (16.31%), tourism (17.54%), education (9.23%), banking (18.46%), manufacturing (30.77%), and telecommunication industries (7.69%) and gathered several demographic variables such as age, gender, education, and organizational tenure. This study selected these variables based on previous studies (e.g. Le et al., 2024; Thuan and Thanh, 2020).

All measurements of constructs in the conceptual model were adapted from prior studies in top journals to ensure reliability and validity. First, all items of the constructs were prepared in English. Then, the back-translation method was applied to ensure accuracy in the translation process (Brislin, 1986). More specifically, one of the authors translated all scales into a Vietnamese version. Then another author translated it back into another English version. After that, based on the two English versions, the Vietnamese version was modified to ensure the meaning of each indicator. Finally, we conducted a pilot qualitative study to refine the Vietnamese questionnaire, ensuring that the meaning of each item aligns with the language used by employees in Vietnam. All items related to online knowledge sharing, departmental support for innovation, and employee innovation used a Likert-type scale with a range from 1 (“strongly disagree”) to 7 (“strongly agree”). Items of task crafting used a Likert-type scale with the range from 1 “hardly ever” to 7 “very often”.

Online knowledge sharing: This study utilized four items adapted from the paper of Nguyen and Nham (2019) to measure online knowledge sharing. Items include the following: “I frequently share knowledge online with my colleagues”; “I try to share knowledge online with my colleagues”; “I always make an effort to share knowledge online with my colleagues”; and “I share knowledge online with colleagues who ask”.

Departmental support for innovation: This study used four items adapted from the study of Birdi et al. (2016) to measure departmental support for innovation. Items include the following: “This department is always moving towards the development of new answers”; “People in my department are always searching for fresh, new ways of looking at problems”; “People in my department co-operate to help develop and apply new ideas”; and “Members of this department provide and share resources to help in the application of new ideas”. Employee innovation: This study used four items adapted from Welbourne et al. (1998) to measure employee innovation. Items were as follows: “I come up with new ideas”; “I work to implement new ideas”; “I find improved ways to do things”; and “I create better processes and routines”.

Task crafting: This study used five items adapted from the study by Slemp and Vella-Brodrick (2014) to measure task crafting. Items include the following: “I introduce new approaches to improve your work”; “I change the scope or types of tasks that you complete at work”; “I introduce new work tasks that you think better suit your skills or interests”; “I choose to take on additional tasks at work”; and “I give preference to work tasks that suit your skills or interests”.

The main survey consisted of 161 males (49.5%) and 164 females (50.5%). Of them, 16 (4.9%) had a high school diploma, 208 (64.0%) had a bachelor’s degree, and 101 (31.1%) had a postgraduate degree. The average age was 28.06 years (standard deviation = 7.310). The average organizational tenure was 7.32 years (standard deviation = 7.195).

SPSS version 25 and AMOS version 24 were utilized to analyze the data. The normal distribution of indicators was examined to determine the method that is most suitable for analyzing confirmatory factor analysis (CFA) and structural equation modeling (SEM). The kurtosis values of 17 indicators ranged from −0.603 to +0.075, and the skewness values of 17 indicators ranged from −0.211 to +0.274. These results revealed that items presented slight deviations from normality. Luckily, the values of kurtosis and skewness were still within the range from −1 to +1. Thus, the maximum likelihood (ML) method is suitable when analyzing the CFA and SEM (Muthén and Kaplan, 1985).

Since the data were collected from a single source, this study followed the suggestions of Podsakoff et al. (2003) to reduce the common method variance. First, the cover letter made it very clear that there were no right or wrong responses and requested that participants rate the questionnaire honestly. Second, we conducted a pilot qualitative study to ensure that the meaning of the indicators aligns with the context in Vietnam, thereby eliminating ambiguity among participants. Third, every item on the survey was taken from scales that had already been validated and published in reputable journals. Fourth, we ran Harman’s single-factor analysis to examine the method bias in the data. The results demonstrated that the single factor explained 28.93% of the variance, suggesting that CMB is not problematic (Podsakoff et al., 2003).

Firstly, Cronbach’s alphas and exploratory factor analysis (EFA) were conducted on all scales of constructs for a preliminary assessment. Cronbach’s alphas of the scales measuring online knowledge sharing, task crafting, departmental support for innovation, and employee innovation were 0.853, 0.861, 0.870, and 0.801, respectively. EFA (principal axis factoring with Promax rotation) extracted four factors with 67.48% of variance extracted at an eigenvalue of 1.831. Moreover, the KMO value was 0.842. The findings showed that each item loaded mostly on its designated component and the factor structure completely matched the design.

Next, a CFA with the ML method was conducted using four latent variables from the conceptual model. The CFA fit the collected data well: chi-square = 146.048; df = 113; chi-square/df = 1.292 GFI = 0.950; NFI = 0.941; TLI = 0.983; CFI = 0.986; RMSEA = 0.030. All standardized factor loadings of the 17 items of the constructs were significant (p < 0.001). The standardized factor loadings ranged from 0.727 to 0.805 for online knowledge sharing, from 0.673 to 0.746 for task crafting, from 0.774 to 0.814 for departmental support for innovation, and from 0.704 to 0.802 for employee innovation. Moreover, the average variance extracted (AVE) of latent variables ranged from 0.523 to 0.627, and the composite reliability scores of latent variables ranged from 0.827 to 0.870. Thus, these results show that the convergent validity of constructs was satisfactory (Steenkamp and Van Trijp, 1991). Furthermore, the square root of the AVE of each latent variable was greater than the latent variables’ intercorrelations in Table 1. Therefore, the discriminant validity of constructs was satisfactory (Fornell and Larcker, 1981). Table 1 illustrates the CFA results.

This article used SEM to test hypotheses because SEM enhances the capacity to test structural model links of multi-item constructs (Hair et al., 2017). According to Lee et al. (2011), SEM enhances the theoretical comprehension of the study model since it thoroughly examines any mediating variables as part of the analysis. Moreover, the variance explained in the dependent variables is bigger because the SEM method considers both direct and indirect effects (Lee et al., 2011). In the conceptual model, departmental support for innovation serves as a moderator that strengthens the relationship between online knowledge sharing and employee innovation. In line with the recommendation of Cortina et al. (2001), this study performed a single-step analysis to evaluate all the hypotheses. The formula of Ping (1995) was used to calculate the interaction between online knowledge sharing and departmental support for innovation. Mean-deviated variables were used to calculate the interaction, thereby avoiding multicollinearity (Cronbach, 1987). Furthermore, this study used 1,000 bootstraps with a 95% confidence interval when analyzing data to test the mediating role of task crafting in linking the association between online knowledge sharing and employee innovation.

The SEM with ML method was analyzed with all latent variables in the conceptual model. The results showed that the conceptual model fits the gathered data well: chi-square = 156.282; df = 128; chi-square/df = 1.221; GFI = 0.949; NFI = 0.938; TLI = 0.986; CFI = 0.988; RMSEA = 0.026. The results of the analyses are listed in Tables 2 and 3. Hypothesis 1 predicted that online knowledge sharing has a positive influence on employee innovation. This association was positive and significant (β = 0.168; p < 0.001). Thus, Hypothesis 1 was supported. Hypothesis 2 proposed that task crafting will mediate the nexus between online knowledge sharing and employee innovation. This association was positive and significant (β = 0.034; p < 0.01), with a confidence interval ranging from 0.014 to 0.064. The β value of this indirect effect was small (β = 0.034) because this study used the formula of Zhao et al. (2010) by multiplying the β value of the direct of effect online knowledge sharing on task crafting (0.213) and the β value of the direct effect of task crafting on employee innovation (0.161). Therefore, Hypothesis 2 was supported. Hypothesis 3 proposed that departmental support for innovation positively moderates the nexus between online knowledge sharing and employee innovation. In other words, the interaction between online knowledge sharing and departmental support for innovation (online knowledge sharing x departmental support for innovation) has a positive effect on employee innovation. This association was positive and significant (β = 0.332; p < 0.001). Hence, Hypothesis 3 was supported. Figure 2 shows the estimated model. Figure 3 illustrates the interplay between online knowledge sharing and departmental support for employee innovation.

This study examines and tests the intervening behavioral mechanisms and social influences that underlie the relationship between online knowledge sharing and employee innovation. This research makes two new contributions. First, in response to the call of earlier studies (Le et al., 2024), this study provides one of the early insights into how task crafting connects online knowledge sharing and employee innovation. According to Li et al. (2020), proactive behaviors are essential for the innovative process, whereby staff members actively seek relevant knowledge to produce and implement new ideas and solutions. Although prior research attempted to explain the relationship between online knowledge sharing and employee innovation both directly and indirectly by investigating the motivational view (Le et al., 2024), this view fails to capture employees’ perceptions of their jobs and ignores the role of proactive behaviors in the innovative process. When online knowledge sharing is encouraged, employees are given opportunities to acquire new skills and information and apply the shared knowledge to their specific job responsibilities (Hau et al., 2013). This reduces the time and effort required to perform their tasks (Wasko and Faraj, 2005). By proactively modifying their tasks to fit their strengths and passions, employees become more engaged and motivated (Petrou et al., 2012), which enables them to develop and apply creative strategies, ideas, and solutions (Anderson et al., 2014; Tims et al., 2012).

Second, in response to the recommendations of earlier studies (Le et al., 2024), this research advances existing knowledge on employee innovation by providing new evidence to understand employee innovation, specifically the interaction between employee ability and social influences. Previous research has provided evidence that contextual influences, such as innovative climate, moderated the relationship between online knowledge sharing and employee innovation (Le et al., 2024). However, this relationship depends not only on contextual influences but also on social influences (Woodman et al., 1993). Specifically, the moderating role of social influences in this relationship, such as departmental innovation, was limited. This finding aligns with the AMO model, which suggests that the interplay between opportunity and ability may foster innovative employee outcomes (Blumberg and Pringle, 1982). During the innovative process, employees in an environment where innovation is highly supported often share knowledge online, convert it into practical ideas, and successfully apply these ideas to achieve more innovative outcomes (Chen et al., 2019; Hansen et al., 1999). On the other hand, in environments with little support for innovation, staff members may be less likely to share knowledge online due to limited resources and lack of encouragement, which ultimately restricts their ability to develop and implement new ideas (Nonaka, 1994; Ryan and Deci, 2000; Zhou and George, 2001).

The results indicate that online knowledge sharing has a positive influence on employee innovation. In the short term, organizations should provide training sessions for online knowledge sharing, including how to facilitate knowledge exchange among team members online. Additionally, organizations recognize and reward employees who actively participate in online knowledge-sharing activities. Moreover, organizations encourage online communication across various teams or departments to promote the exchange of ideas and viewpoints. In the long term, organizations can implement knowledge-sharing platforms or applications to facilitate online knowledge sharing, project collaboration, and access to online resources. Furthermore, organizations can foster a culture of online knowledge sharing by promoting online communication and establishing a secure platform where staff members can freely share their expertise and experiences.

The findings show that task crafting links the effect of online knowledge sharing on employee innovation. To increase the level of task crafting of employees, companies should provide employees opportunities to modify their tasks that fit their abilities or interests. Conferences, workshops, and training courses should be conducted to teach staff members how to divide their jobs into smaller and more manageable chunks. After that, employees may find it easier to efficiently manage and adjust their tasks. Additionally, when hiring new staff, companies should assess the extent to which candidates proactively adapt their tasks to match their abilities, expertise, and interests, particularly when hiring for jobs that frequently require creating and implementing new ideas and solutions.

The results also show that departmental support for innovation reinforces the relationship between online knowledge sharing and employee innovation. Therefore, companies should encourage departments to provide resources, encouragement, and leadership that facilitate and promote innovative behaviors among their employees. Several activities can be implemented to enhance innovative support within departments, including promoting open communication among employees, providing resources to all staff, recognizing and rewarding innovation, cultivating a growth mindset within departments, and facilitating cross-functional collaboration among employees. Moreover, when recruiting new employees, companies should ask them about their experiences in supporting coworkers in creating and implementing new ideas and solutions at previous companies. As a result, companies can select applicants who support their coworkers, thereby increasing innovative outcomes in the future.

Although this study provides several new contributions, it has some limitations. First, this study employed a convenience sampling method to collect data from full-time workers in various industries across Vietnam. Future research should use additional methods to gather data and test the conceptual model, potentially enhancing the research’s value. Second, this paper focuses on task crafting, linking the association between online knowledge sharing and employee innovation. However, the literature review shows that motivational elements, organizational elements, identification-based elements, and social-relational elements may act as mediators to link this association (Anderson et al., 2014). Future research can explore and test such mediators to gain a deeper understanding of the impact of online knowledge sharing on employee innovation. Third, this study focuses on departmental support for innovation, acting as a moderator to reinforce the effect of online knowledge sharing on employee innovation. Nevertheless, the literature review indicates that creative self-efficacy, proactive personality, learning orientation, customer orientation, growth need strength, and mastery orientation can serve as moderators, enhancing the impact of online knowledge sharing and employee innovation. Future research can examine such moderators to gain a deeper understanding of this impact. Finally, this study collected data from several industries, so the research results are valid and applicable to multiple industries. However, the culture in Vietnamese organizations is often collectivist; future studies can be conducted in individualist cultures to investigate the effects of online knowledge sharing, task crafting, and departmental support for innovation on employee innovation.

This research is funded by University of Economics Ho Chi Minh City, Vietnam (UEH).