Introduction

With the rise of the fourth scientific and technological revolution, the digital revolution represented by digital technology has changed the production and management mode of traditional organizations (Liu et al., 2022a). In the era of digital economy, digital project teams (hereinafter referred to as “teams”) have flourished and become the representative of the agile structure of organizations responding to rapid changes. At the same time, the topics of organizational management, member behavior, and the development of digital project teams are gradually attracting considerable attention (Guinan et al., 2019). These teams are task oriented to use digital technology to complete the project. To improve the efficiency and quality of the project, digital project teams need to accelerate their learning of digital technology knowledge. They gradually enhance their knowledge of digital technology to guide project activities such as service practice (Kozarkiewicz, 2020). To achieve this, the teams need a mix of employees with different digital expertise. Teams rely on the division of labor to integrate cross-disciplinary individual knowledge into team knowledge, ultimately achieving project development and innovation (Guinan et al., 2019; Moura et al., 2021). However, within a short period of time, a large amount of heterogeneous knowledge intersects in teams, intensifying the heterogeneous knowledge conflict in teams and making the knowledge management process more complicated, which brings negative consequences to team development. In this paper, we focus on the relationship between knowledge heterogeneity and team performance.

There are paradoxical explanations for the impact of knowledge heterogeneity on organizational performance. Opposing scholars argue that heterogeneous knowledge adversely affects the organizational knowledge management process owing to characteristics such as differences in background, division of expertise, local inertia of knowledge acquisition, and path dependence of knowledge development (Amabile, 1996). Moreover, it is not conducive to the improvement of enterprise performance (Kanawattanachai & Yoo, 2007). Some scholars point out that heterogeneous knowledge and innovation activities seem to have an inverted U-shaped nonlinear relationship. High heterogeneity is not only difficult to absorb and transform but also easily causes internal cognitive conflict and even increases the management cost of knowledge transaction (Andersen & Munksgaard, 2009). In this case, teams and enterprises with high knowledge heterogeneity cannot carry out breakthrough innovation activities (Leeuw et al., 2014; Oerlemans et al., 2013), because these will eventually bring negative consequences to enterprise performance (Hirunyawipada et al., 2010). By contrast, some scholars believe that for organizations to create value, it is very important to manage heterogeneous knowledge bases to promote knowledge sharing within organizations and enhance the competitiveness of enterprises (Goffin et al., 2010; Rodan, 2002). Heterogeneous knowledge can have a positive impact on business activities and performance (Fu-Sheng, 2018; Laursen & Salter, 2014; West & Bogers, 2014). In this regard, the present paper argues that the paradox of the impact of knowledge heterogeneity on organizational performance stems from the fact that the mechanism of the organizational knowledge management process is not yet clear. The reason for the negative effect of knowledge heterogeneity may also be that existing literature has not found appropriate means to effectively manage heterogeneous knowledge. Therefore, besides focusing on the relationship between knowledge heterogeneity and team performance, we focus our work on develo** an appropriate model of how knowledge heterogeneity affects team performance. This model may be closely related to digital capability and knowledge management.

Digital project teams are different from the general organization form. Their formation and development are closely related to the change of digital technology. Specifically, the teams recruit employees with different knowledge, especially those with different digital technology backgrounds, who subsequently promote learning across the teams (Zhan & **e, 2022). This strategy gradually develops teams’ ability to master digital technology applications and digital information acquisition, among other skills, and thus promotes project development and construction (Chaudhuri et al., 2022). Thus, the completion of team projects relies on the digital capability that the teams have gradually acquired through continuous learning of digital technology. For example, the teams recruit people with knowledge of cloud computing to develop their digital capability, thus accelerating the process of a certain project (Guinan et al., 2019). Therefore, it is of key importance for the project development that the teams have staff with different digital knowledge to exchange and learn and then gradually develop their digital capability (Zhao & Yang, 2022). In addition, in the context of digital economy, the emergence of digital project teams also promotes the rethinking of the heterogeneous knowledge management process. The characteristics of enterprise knowledge mean enterprises face two tasks simultaneously. The first is to create new valuable expertise through the integration and innovation of personal knowledge within enterprises. The second is that teams need to acquire the required knowledge effectively from outside (Kogut & Zander, 1992). From the perspective of knowledge creation theory, knowledge conversion is an inter-conversion process of explicit and tacit knowledge within an organization (Mishra & Bhaskar, 2011; Nonaka, 1994; Zahra & George, 2002). Absorptive capacity theory holds that knowledge conversion is an effective way for organizations to identify valuable knowledge outside the system, and then, teams will digest, absorb, and apply it to achieve organizational goals. In practice, the two kinds of knowledge conversion processes exist in the enterprise. While the enterprise nurtures and constructs new knowledge internally, it also absorbs valuable knowledge from outside in a timely manner. Therefore, combined with knowledge creation theory and absorptive capacity theory, knowledge conversion is divided into internal knowledge conversion and external knowledge conversion (Zahra & George, 2002). In this regard, combined with the knowledge management process, the teams use external knowledge filtering, storage, and edge applications (Choi et al., 2018; Karadsheh et al., 2009) and interact with internal knowledge to better play the service value of knowledge for practice. However, in theoretical analysis, it is still insufficient to analyze the heterogeneous knowledge management process of digital project teams based on the background of a digital economy. In particular, existing literature has not reached a consensus on whether the heterogeneous knowledge management process can be related to digital capability and knowledge conversion.

Based on the background of a digital economy, we take digital project teams as the research object, introduce digital capability and internal and external knowledge conversion, and attempt to analyze the influence mechanism and boundary conditions of knowledge heterogeneity within teams on team performance. The marginal contribution of our work is as follows. First, it enriches the expanded application of the knowledge base view in the digital economy era and points out that knowledge heterogeneity in the context of the digital economy has a positive impact on team performance. To some extent, this reflects the paradoxical interpretation of the impact of heterogeneous knowledge on organizations. Second, it enriches the research on heterogeneous knowledge management process in the era of the digital economy. We likewise propose the mediating effect of digital capability and the regulatory effect of internal and external knowledge conversion. Finally, we further enrich the research of digital project teams and provide some practical reference for the teams to actively exert heterogeneous knowledge value and correctly deal with the knowledge management process.

Theory and Hypothesis

Team Knowledge Heterogeneity and Team Performance

Knowledge heterogeneity was first proposed by Amabile et al., (1996) on the basis of learning from the concept of team knowledge heterogeneity. The differences among team members are believe to come from unavoidable “fragmented” knowledge and experience. As an attribute describing the collective knowledge structure, knowledge heterogeneity refers to the degree to which a member’s knowledge is different from that of other members (Rodan & Gahunic, 2004). The knowledge base view clearly shows that knowledge as a heterogeneous resource can deepen the competitive advantage of companies, which rely on knowledge resources to successfully create value through innovation (Sánchez-González et al., 2009). It has been argued that heterogeneous knowledge exists in a collective oriented toward a particular knowledge activity or process (Reagans & Zuckerman, 2001). In this process, organizations can rely on knowledge heterogeneity resources to successfully create value through innovation. This idea also implies that knowledge heterogeneity may constitute a real “motivating factor” for collective knowledge activities (Quigley et al., 2007). In addition, as knowledge diversification deepens, a variety of innovative combinations are explored with acquired external heterogeneous knowledge, effectively expanding teams’ knowledge base (Liu et al., 2022b). Individuals with different knowledge structures can also form new knowledge coupling patterns through knowledge interaction. This increases their willingness to express or manage knowledge publicly and helps enterprises obtain innovation opportunities and resources faster (Yang & Yang, 2014), break innovation thinking bottlenecks, and so on (Laursen & Salter, 2014; West & Bogers, 2014). In this regard, the current paper argues that heterogeneous knowledge with digital technology can create greater value for organizations in the context of the digital economy. Developed or acquired knowledge for organizational innovation is often transferred from different units of the organization, or even between different organizations. It is also this acquisition and conversion of knowledge that accelerates digital innovation in enterprises (Cui et al., 2019; Vaio et al., 2021). More critically, teams have a great deal of heterogeneous knowledge of digital technology, which is interwoven with traditional technical knowledge. This plays an important role in forming a digital mindset, accelerating the digital capability of teams to apply new technologies, cracking dilemmas for teams, and enhancing the capability of digital project teams, among other functions. Accordingly, this study argues that heterogeneous knowledge is richer in digital project teams and that knowledge from different fields collides with others to accelerate innovation and promote project progress (Cohen & Caner, 2016). Ultimately, it has a positive impact on team performance, resulting in the following hypothesis.

  • H1: Team knowledge heterogeneity positively affects team performance.

The Mediating Role of Digital Capability

With the development of digital technology, digital capability has become closely related to the use of computers, the power to understand images and sounds, and the ability to transform digital media (Bejakovié & Mrnjavac, 2020). Calle et al., (2020) focus on professionals working in the ICT industry, most of whom already have specific digital capability to perform specific tasks. Similarly, Annarelli et al., (2021) classify digital capability into three concepts, namely, generic, professional, and complementary competencies. However, these concepts are all based on the study of digital capability at the individual level. For organizations, digital capability is the key to creating disruption and, accordingly, creating competitive advantage in the digital environment (Chaudhuri et al., 2022). Currently, some scholars believe that enterprise digital capability refers, on the one hand, to the ability of enterprises to use digital technology to integrate with the physical components of products for promoting technological innovation in new products and processes (Fichman et al., 2014; Yoo et al., 2010a). On the other hand, it also refers to a company’s ability to collect technical and market information from stakeholders through the Internet, cell phones, communication and multimedia technologies, and so forth. The teams facilitate the integration of R&D and production through the efficient use of core information such as big data (Lyytinen et al., 2016; Yoo et al., 2010b). Combined with the characteristics of digital project teams, the present paper considers that digital capability in teams mainly refers to teams’ ability to integrate digital information and use digital technology. It accelerates the learning and mastery of digital technology by leveraging the value of heterogeneous knowledge and gradually cultivates the digital capability of the teams. Therefore, digital capability in this paper refers specifically to the ability of teams to manage heterogeneous knowledge through the integration and, therefore, mastery of digital technology. Certainly, it also includes the application of digital technology capability, among other functions.

To date, few research has been conducted on knowledge heterogeneity and the digital capability of companies. Some researches discuss the relationship between knowledge heterogeneity and innovation in the context of digital economy (Liu, 2019; Song, 2019). This paper combines the digital environment with the digital technology capability characteristics of the enterprises and argues that under the continuous change of digital technology, the turnover of members in teams will increase. The heterogeneity of team knowledge becomes more evident as the knowledge possessed by different members becomes richer. In such cases, members with different knowledge can collide with one another to activate the innovative thinking of team members. This outcome accelerates the exploration and understanding of the team regarding the application of digital technology, which promotes both teams’ digital technology application ability and digital capability. In this regard, the following hypothesis is proposed in this paper.

H2: Team knowledge heterogeneity positively affects digital capability.

There is considerable literature on the positive correlation between organizations having digital capability or having the ability to apply digital technology and business performance, innovation capability, and so on (Tang et al., 2017). External knowledge absorption can help companies understand the frontiers of digital technology and digital knowledge in the industry. Therefore, external knowledge acquisition and integration become particularly important for building digital project teams in the digital age (Liu et al., 2021). External knowledge conversion has also been shown to be an important part of the knowledge management process (Choi et al., 2018). Therefore, this paper argues that the combination of external and internal knowledge conversion has a positive impact on the optimization of the heterogeneous knowledge management process of digital project teams and the improvement of enterprises’ digital capability. Subsequently, the filtered and processed heterogeneous knowledge is introduced into teams and interacts with the internal knowledge of the teams (Zoo et al., 2017). Thus, teams incorporate external knowledge into the internal heterogeneous knowledge conversion and management process. Through the conversion of external heterogeneous knowledge and the introduction of internal knowledge management process, teams accelerate the integration with internal heterogeneous knowledge and optimize the process of team heterogeneous knowledge management (Pateli & Lioukas, 2019). This approach indirectly enhances the positive effect of team knowledge heterogeneity on improving enterprise digital capability. In this regard, we argue that the conversion of external knowledge management process, especially external knowledge, can enhance the internal knowledge conversion and internal team knowledge management process. Finally, it indirectly heightens the positive impact of team knowledge heterogeneity on enhancing team digital capability. On the basis of the above discussion, we argue that external knowledge conversion can effectively regulate the internal knowledge conversion process. In this regard, the following hypothesis is proposed.

H6: External knowledge conversion enhances the moderating effect of internal knowledge conversion on team knowledge heterogeneity on digital capability.

In summary, the model is shown in Fig. 1.

Fig. 1
figure 1

The theoretical model

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Research Method

Sample and Data

Due to the COVID-19 epidemic, it is difficult to obtain team samples through field research, so we have adopted a combination of online and offline research for large-scale data collection. Our research object is digital project teams in China, and the questionnaire is mainly filled by the leaders or senior managers of these teams. We focus on teams that were highly related to digital innovation, such as intelligent manufacturing, intelligent medical care, chip development, information systems, and other business areas. More specifically, these samples mainly involve the information industry, game development industry, and medical industry. For offline research, we mainly investigate digital project teams in South China (Guangdong, Fujian, etc.), East China (Jiangsu, Zhejiang, and Shanghai), and Central China (Hubei and Hunan). Before the research, the research team has contacted the executives of these digital project teams to ask for their consent to the survey and asked the participants to complete the questionnaire separately during the research. We send e-mail questionnaires and instructions to these offline participants, and finally, the participants return the completed questionnaires by e-mail. For online research, we collect team data nationwide through the online survey platform “Credamo.”

The data is collected through a questionnaire survey, so there may be potential interviewee bias, which requires us to take measures to reduce bias (Brace, 2018). We take the following measures. First of all, to encourage respondents to answer these questions as truthfully as possible, we set up diversified salaries. Specifically, whether it is an online questionnaire or a field survey, we give 30 yuan rewards to respondents who complete the questionnaire. Secondly, we believe that these managers were more interested in how to conduct daily business management and operation than material incentives. Team members keep close contact with enterprises and teams in the business field for a long time, especially high-tech and digital enterprises (teams). During this period, we accumulate rich practical experience. Continuous efforts in academic research also provide us with a solid theoretical foundation and knowledge. Therefore, we provide them with free and open online courses (strategic management and human resource management), ho** to guide them to answer the questions in the questionnaire truthfully. Finally, following the ethical rules of investigation, we clearly state in the questionnaire, “We guarantee that the research will only be used for academic purposes, and we will keep your information confidential and not disclose irrelevant people and organizations,” which is emphasized again in the on-site investigation.

To obtain reliable research conclusions and avoid the common method bias, we collect two waves of data (Brace, 2018). The first survey starts in June 2020 and lasted for 3 months. It mainly asks participants to complete the scale of team knowledge heterogeneity, digital capability, and knowledge conversion. In this wave, we distribute questionnaires to 400 teams, and 389 of them were collected, and the valid questionnaires are 364 (excluding 8 questionnaires with consistent answers and too long and too short filling time). Earlier, we conduct a long-term follow-up survey of some digital project teams and find that most digital project teams need about 1 year from implementing digital strategies to producing results. In cases such as failure of digital strategies is excluded, it will take longer to produce project results. Therefore, we start the second wave of data collection in October 2021, lasting for 2 months, mainly for participants to complete the team project performance scale. To accurately match the data of the two surveys, we ask participants to fill in the last four digits of their mobile phone numbers in the questionnaire. According to the valid samples of the first wave, in the second wave, we distribute questionnaires to 364 teams, and 358 are collected, of which 322 are valid. We find that the information industry accounted for 20.5%, game development accounted for 27.6%, and the medical industry accounted for 32.0%. Teams with less than 10 people account for 15.5%, and more than 30 people accounted for 22.0%; 3 to 5 years are accounted for 34.2%, and more than 11 years are accounted for 9.0%. Teams in East China account for 11.5%, those in Central China account for 26.4%, and those in South China accounted for 34.2%. On the whole, the sample of this research covers digital project teams in different industries, team sizes, ages, and geographical locations (Table 1), which shows that the sample of this research is representative and convincing.

Table 1 Basic characteristics of samples (N = 322)
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Measurement

The scale mainly draws lessons from foreign mature scales and is designed in the form of a Likert 7-point scale, with scores ranging from 1 (totally disagree) to 7 (totally agree). It is worth noting that although there are mature measurement methods for variables such as knowledge heterogeneity, digital capability, and knowledge conversion, these tools are rarely or even not tested in the Chinese context. Therefore, we need to revise these scales according to the research situation, to conform to the research topic and situation of this paper. We mainly follow the following steps (Harkness et al., 2003): first, we ask professors from English majors and management majors to translate in a forward-backward way. Then, the translate scale and the original foreign scale are discussed and revise by the other two professors, and finally, a formal Chinese scale is formed, further using expert consultation (1 professor in knowledge management and 1 professor in digital innovation) and structured interview (4 digital project team managers in an entrepreneurial park in ** software products. Therefore, we have referred to this research and made the items in the scale close to digital products. In addition, we integrate team learning and speed-to-users and use these two dimensions to measure project performance. Generally, the project performance scale include six items, such as “overall, our team does an outstanding job uncovering digital product problem areas with which users/customers are divorced” and “the digital projects in our teams are completed in less time than what is considered normal and custom for our industry.” Cronbach’s α was 0.812 in this research.

Knowledge Conversion

In this research, we comprehensively consider the moderating effects of internal knowledge conversion and external knowledge conversion. Internal knowledge conversion is a process of mutual conversion of knowledge within a team, such as socialization, externalization, integration, and internalization of knowledge. For internal knowledge conversion, we mainly refer to the research of Tiwana & McLean, (2005) and Nonaka, (1994). In this research, the scale consists of four items, such as “team members synthesis and integrate team members’ expert at the project level.” In this research, Cronbach’s α of internal knowledge conversion was 0.862. External knowledge conversion refers to the process in which the team effectively identifies valuable new knowledge outside the team and digests, absorbs, and applies it to achieve the teams’ goals. For external knowledge conversion, we mainly refer to Simonin, (1999) and Zahra & George, (2002). In this research, the scale consisted of three items such as “our team has learned a great deal about the technology/process know-how held by your partner.” In this research, Cronbach’s α of external knowledge conversion is 0.752.

Control Variables

As the teams’ project performance and digital capability may be affected by the team size, age, project field, and geographical location (Akgün, 2020), we regard these variables as control variables. The project field is divided into four categories: 1 for the information industry, 2 for medical, 3 for manufacturing, and 4 for others. The team size is divided into four categories: 1 means less than 10 people, 2 means 10~20 people, 3 means 20~30 people, and 4 means more than 30 people. Team age is divided according to the time from the establishment of the team to the first survey: 1 means 3–5 years, 2 means 6–8 years, 3 means 9–11 years, and 4 means more than 11 years. The location is divided into four categories according to the location of the research team: 1 for East China, 2 for Central China, 3 for South China, and 4 for others. In addition, according to the research of Calantone et al., (2003), we also consider the influence of market turbulence and technical turbulence on the deviation of model results. For the evaluation of market and technology turbulence, we refer to the scale developed by Jaworski & Kohli, (1993). There are six items in the market turbulence, such as “in our kind of business, customers’ digital product preferences change quite a bit over time.” Market turbulence in this research Cronbach’s α was 0.900. Technical turbulence includes five items, such as “the technology in our industry is changing rapidly.” The Cronbach’s α is 0.814 in this research.

Results

Reliability and Validity Analysis

We test the reliability of the data and find that Cornbrach’s α value, factor load, CR value, and AVE value of all factors meet the requirements (Table 2). Secondly, by using Harman’s single factor test method, it is found that seven factors are extracted by principal component analysis, accounting for 64.97% of the total variance. The first principal component obtained without rotation accounted for 16.38%, and it does not exceed the critical value of 40%. Therefore, the common method bias in this research is well controlled (Hair et al., 2019).

Table 2 Quality criteria of constructs
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Finally, to examine the validity of discrimination among variables, we make a confirmatory factor analysis on seven variables (including control variables) involved in the research. The results show that the index of the single-factor model was the worst, while the index of the seven-factor model (χ2/df = 1.443, CFI = 0.956, TLI = 0.951, SRMR = 0.045, RMSEA = 0.037) meets the requirements (Hair et al., 2019), and it is superior to other competitive models, which indicates that the seven factors in this research have good discrimination validity (Table 3).

Table 3 Results of confirmatory factor analysis
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Descriptive Statistics and Correlation Analysis

According to the results in Table 4, the average values of the main variables (knowledge heterogeneity, digital capability, project performance, internal knowledge conversion, and external knowledge conversion) in this research are between 4.63 and 5.32, which indicates that the knowledge heterogeneity of the teams in the sample is generally at the upper-middle level, and the digital practice, knowledge management practice of the teams, and the project performance of the teams are pretty good. Secondly, the standard deviation of these variables is between 0.64 and 1.02, indicating that the sample fluctuation is not obvious and accords with the normal distribution, so it is suitable for the next analysis. Through correlation analysis, we find that there is a significant positive relationship between team knowledge heterogeneity and project performance (r = 0.122, p < 0.05), and there is also a significant positive relationship between team knowledge heterogeneity and digital capability (r = 0.178, p < 0.01), and there is also a significant positive relationship between team digital capability and project performance (r = 0.354, p < 0.001). The verification of the correlation between variables provides preliminary evidence for the research hypothesis.

Table 4 Mean value, standard deviation, and correlation analysis of variables
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Hypothesis Test

Direct Effect Test

Based on control, we use SPSS 24 software to test the hypothesis. Through regression analysis (Hair et al., 2019), we find (Table 5) that the direct effect of knowledge heterogeneity on team project performance is significantly positive (β = 0.138, p < 0.001, M2), indicating that knowledge heterogeneity can improve team project performance, and H1 is confirmed. In addition, the direct effect of knowledge heterogeneity on team digital capability is significantly positive (β = 0.122, p < 0.01, M5), indicating that knowledge heterogeneity can improve team digital capability, and H2 is confirmed. Finally, the direct effect of team digital capability on project performance is significantly positive (β = 0.287, p < 0.001, M3), which indicates that team digital capability can improve project performance, and H3 is confirmed. The above direct effect analysis lays the foundation for the subsequent intermediary effect test.

Table 5 Regression analysis results
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Mediating Effect Test

First, we preliminarily test the mediating effect of digital capability through hierarchical regression (Baron & Kenny, 1986) and find that (Table 5) the team knowledge heterogeneity still has a significant positive impact on project performance (β = 0.103, p < 0.01, M3), and H1 was verified again. As mentioned, digital capability has a significant positive impact on project performance (β = 0.287, p < 0.001, M3). Importantly, we find that compared with M2, the regression coefficient of team knowledge heterogeneity to project performance is significant but decreased (from 0.138 to 0.103), which indicates that team knowledge heterogeneity has a positive impact on project performance through digital capability, and H4 is confirmed.

To clarify the specific mediation effect, we use the PROCESS macro in SPSS 24 software and the Bootstrap method to estimate the mediation effect (Preacher & Hayes, 2008). The Bootstrap sampling number is set to 5,000 times, and the confidence level of the confidence interval was set to 95%. We find (Table 6) that the total effect of team knowledge heterogeneity on project performance is 0.138 (95% CI (0.073, 0.203)), and the indirect effect of team knowledge heterogeneity on project performance through digital capability is 0.035 (95% CI (0.010, 0.065)). The interval does not include 0, indicating that digital capability plays an intermediary role between team knowledge heterogeneity and project performance, and H4 is verified again.

Table 6 Mediating effect test of Bootstrap method in PROCESS macro
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Moderating Effect Test

Before the test, independent variables (team knowledge heterogeneity) and moderating variables (internal knowledge conversion and external knowledge conversion) are centralized, and the interaction items are calculated (Hair et al., 2019). Through regression analysis (Table 5), it is found that team knowledge heterogeneity has a significant positive impact on digital capability (β = 0.092, p < 0.05, M6), while the interaction between team knowledge heterogeneity and internal knowledge conversion has a significant positive impact on digital capability (β = 0.224, p < 0.001, M6). The above results show that internal knowledge conversion has played a positive moderating role in the relationship between team knowledge heterogeneity and digital capability. That is, internal knowledge conversion strengthens the positive influence of team knowledge heterogeneity on digital capability, and H5 is confirmed. In addition, the interaction of team knowledge heterogeneity, internal knowledge conversion, and external knowledge conversion has a significant positive impact on digital capability (β = 0.152, p < 0.001, M7), indicating that external knowledge conversion enhances the moderating role of internal knowledge conversion, and H6 is confirmed. Specifically, we believe that under the high external knowledge conversion, internal knowledge conversion further strengthens the positive influence of team knowledge heterogeneity on digital capability. The specific moderating effects are shown in Figs. 2 and 3.

Fig. 2
figure 2

The moderating role of internal knowledge conversion

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Fig. 3
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The moderating role of external knowledge conversion

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Discussion

This study constructs a theoretical model of knowledge heterogeneity within digital project teams affecting team performance from the knowledge base view. Furthermore, we combine the characteristics of the digital economy era by drawing the following research conclusions through questionnaire survey and empirical analysis. (1) First, in the digital economy, knowledge heterogeneity within digital project teams positively affects team performance. This partly responds to the paradoxical controversy of research on the impact of knowledge heterogeneity on organizational performance (Kanawattanachai & Yoo, 2007; Rodan, 2002). Digital project teams can also maximize the value of knowledge through heterogeneous knowledge management, which can positively impact team performance. (2) Second, this paper introduces digital capability and demonstrates the positive impact of knowledge heterogeneity on digital capability. The partial mediating effect that digital capability has in the model of the effect of knowledge heterogeneity on team performance is also demonstrated in this paper. That is, team knowledge heterogeneity can positively affect team performance by positively influencing digital capability. Specifically, employees with different knowledge have a positive effect on teams’ ability to improve digital technology learning and digital technology application. The enhanced digital capability also accelerates teams’ project progress and improves its performance. This result further adds to the literature on knowledge heterogeneity, digital capability, and organizational performance. (3) Third, this paper introduces the knowledge conversion condition to empirically demonstrate the moderating effect of knowledge conversion. That is, it can effectively moderate the positive effect of team knowledge heterogeneity on digital capability. The conflict of knowledge heterogeneity can be effectively mitigated and the value of knowledge maximized through internal and external knowledge conversion (Choi et al., 2018). Internal knowledge conversion enhances the positive impact of team knowledge heterogeneity on team performance in the digital economy, while external knowledge conversion can indirectly enhance the positive impact of team knowledge heterogeneity on team performance by directly and positively influencing internal knowledge conversion.

Theoretical Implications

First, distinct from previous studies of knowledge heterogeneity (Cui et al., 2019; Fu-Sheng, 2018; Rodan, 2002), this paper puts the research of knowledge heterogeneity under the background of the digital economy. Taking the teams as the main research object, this paper constructs a model of the impact of team knowledge heterogeneity on team performance based on the background of the digital economy. In this regard, it further enriches the content of the knowledge base view, especially the analytical study of the knowledge base view in the context of the digital economy (Shrivastava & Gregory, 2009). Besides, based on the context of the digital economy, this paper proposes that knowledge heterogeneity in digital project teams positively affects team performance, thus responding to some extent to the paradoxical study of the impact of knowledge heterogeneity on organizational performance (Leeuw et al., 2014). Meanwhile, this paper proposes that through the moderating role of internal and external conversion of knowledge and the mediating role of digital capability, it further deepens how to enhance team heterogeneous knowledge management in the digital environment. That is, the team enhances internal and external knowledge conversion regulation by strengthening digital capability, thus weakening the negative impact of knowledge heterogeneity on organizational performance, among other outcomes (Leeuw et al., 2014; Oerlemans et al., 2013). Based on this, this study enhances the analysis of organizational knowledge management processes in the digital environment. We likewise provide some references for future scholars to analyze in depth the knowledge base view in the digital environment and enhance the process of managing teams’ knowledge heterogeneity.

Second, this paper introduces the mediating variable of digital ability. Most existing studies focus on the relationship between team knowledge heterogeneity and innovation, learning ability, and absorptive capacity (Nieto & Quevedo, 2005; Wang & Sun, 2020; Zhang & Guo, 2019). However, in the digital economy, the improvement of digital project team performance is inextricably linked to digital capability, and in this process, knowledge heterogeneity can effectively reinforce the improvement of the teams’ digital capability. Therefore, knowledge heterogeneity in digital project teams has a positive impact on digital capability. Moreover, digital capability has a mediating effect between team knowledge heterogeneity and project performance.

Third, this paper further extends the study of the process of managing knowledge heterogeneity in the digital context. This in turn accelerates the process of project progress and ultimately improves team performance.

Finally, this paper introduces the moderating variable of knowledge conversion and divides it into internal and external knowledge conversion according to its scope of action (Mishra & Bhaskar, 2011). It further analyzes how to enhance the management of heterogeneous knowledge through internal and external knowledge conversion in the digital environment so as to maximize the value of heterogeneous knowledge. Moreover, this paper further enriches the study of heterogeneous knowledge management processes in organizations. It clarifies how to strengthen heterogeneous knowledge management and weaken the negative influence of heterogeneous knowledge through internal and external knowledge conversion. Thus, it maximizes the positive effect of heterogeneous knowledge on team performance.

Based on this, through the introduction of digital capability and internal and external knowledge conversion, this paper deepens the analysis on improving project performance by knowledge heterogeneity within teams in the digital environment. It also provides a basis for further research on the heterogeneous knowledge management process within the teams and some theoretical enlightenment to weaken the negative impact of knowledge heterogeneity.

Practical Implications

This study also has important implications for guiding the practice of digital project teams in the digital economy. First, teams must focus on the knowledge management process, especially on the management of heterogeneous knowledge. That is, it serves the teams effectively through heterogeneous knowledge value and enhances team performance. Therefore, such management is of key importance for team managers to select heterogeneous employees, and maintaining the knowledge heterogeneity of the digital project team should be at a reasonable level. When constructing the digital project team, we should improve the knowledge difference system of the team members, select knowledge reserve personnel with different educational and professional backgrounds, and reasonably match the members with different expertise (Fu-Sheng, 2018). In particular, there should be a focus on recruiting people with knowledge of digital technology, such as AI, cloud computing, and Python (Bejakovic & Mrnjavac, 2020), to improve teams’ ability to acquire digital information resources. In addition, managers should fully understand the knowledge distribution of team members and accurately assess the knowledge capability of different members. On the basis of an accurate assessment, the team can reasonably arrange the division of labor to avoid the conflict and contradiction of different knowledge and thus maximize the value of heterogeneous knowledge.

Second, the teams strengthen the digital capability, including the ability to analyze and process the application of digital technology as well as the ability to obtain, filter, and transform digital information (Khin & Ho, 2018). In this regard, enhancing organizational learning is the key to improving the digital capability of teams (Aboobaker & Zakkariya, 2021). On the one hand, teams need to focus on external knowledge to master the current market environment development trends (Arfi et al., 2018); for example, the progress of projects of the same type and the changes in consumer demand so that teams can respond to changes in the external environment in a timely manner. On the other hand, the team should also pay attention to the introduction of digital hardware equipment and strengthen both the digital soft power and hard power of the teams (Lyytinen et al., 2016). Teams thus become more effective in facing the complex and changing digital environment to accelerate team project progress.

Finally, in the digital environment, teams focus on optimizing the knowledge conversion management process so as to leverage the value of heterogeneous knowledge to guide project practice. Therefore, for teams, the value of knowledge depends not only on the amount of its stock resources but also on the amount of its effectiveness in being transformed and internalized. When members within the team share knowledge and information exchange, they want to maintain the advantage of knowledge maintenance and activation as well as transform and internalize the acquired knowledge as much as possible. Thus, it provides strong power support for enterprise product innovation (Herschel et al., 2001). First, digital project teams must develop the ability of employees to translate and internalize knowledge, especially to stimulate innovative thinking and ideas in a digital environment. By doing so, they improve digital capability as an important prerequisite for project completion and accelerates the project process (Chaudhuri et al., 2022). In addition, the efficiency and rate of knowledge conversion are constrained by the hierarchy of organizational structure and the channels of information transfer. A gradual hierarchical structure and poor communication within the team will limit the efficiency of knowledge transfer. In this regard, the team communication channel should be opened up in time to create conditions between teams to improve the willingness of team members to transform knowledge and the effect of knowledge conversion. For example, teams should improve information exchange tools, organize knowledge-sharing activities, and create conditions for knowledge conversion. These tasks improve the efficiency of knowledge conversion by strengthening the knowledge communication ability of team members and enhancing communication skills as well as promote the digital capability and innovation performance of teams (Deja & Rak, 2019).

Being in the digital age, teams certainly have to consider some special contexts during their practice. For example, teams should improve information exchange tools and organize knowledge-sharing activities to create conditions for knowledge conversion. In addition, how to quickly and effectively filter useful value information in the process of external knowledge conversion and other knowledge management processes is worth noting.

Limitations and Future Directions

This paper explores the impact of knowledge heterogeneity in digital project teams on team performance based on the digital economy environment. First, this paper selects digital capability and internal and external knowledge conversion factors and empirically demonstrates their mediating moderating effects separately, while whether there are other variable factors affecting knowledge heterogeneity and team performance in the context of the digital economy, they still need to be studied in the context of theory and practice. Second, this paper analyzes the digital project team as the research object. Are there other types of digital teams that exist in the context of the digital economy? For example is digital start-up teams and etc. In this regard, it remains to be seen whether the conclusions of this paper are still applicable, and whether they can be generalized to digital teams in general. Third, at this stage, no unified conclusion has been reached regarding the impact of knowledge heterogeneity on organizational performance. This paper argues that knowledge heterogeneity can positively affect organizational performance in the digital economy. Organizations are in the volatility, uncertainty, complexity, and ambiguity (VUCA) era, with increasing uncertainty and dynamism in the environment and the iterative nature of digital technology, whether heterogeneous knowledge always positively affects organizational performance. However, the existing literature is still inadequate in exploring this process. For this, this paper hopes to continue to explore the process of knowledge management within organizations in the context of the digital economy in the future. And we further increase the sample size, enhance the reliability and validity, and present more generalized conclusions.

Conclusions

Consistent with the knowledge-based view, team knowledge heterogeneity is an important source of gaining performance, which has been firmly confirmed in this study. A more significant discovery and contribution are that we have found the important mechanism of digital capability in the era of interaction between the knowledge economy and the digital economy. It can explain why the knowledge heterogeneity of digital project teams can predict team performance. In addition, we also provide effective measures for knowledge management for digital project teams in emerging economies. More specifically, we emphasize the importance of internal knowledge conversion and external knowledge conversion in managing heterogeneous knowledge.