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Article

Does Innovation Climate Help to Effectiveness of Green Finance Product R&D Team? The Mediating Role of Knowledge Sharing and Moderating Effect of Knowledge Heterogeneity

by
Xue Ding
1,
Wei Li
2,*,
Dujuan Huang
3 and
Xinghong Qin
4
1
School of Financial Technology, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China
2
School of Economics and Management, Tongji University, Shanghai 200092, China
3
College of Business Administration, Anhui University of Finance and Economics, Bengbu 233030, China
4
School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing 400067, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(7), 3926; https://doi.org/10.3390/su14073926
Submission received: 14 February 2022 / Revised: 19 March 2022 / Accepted: 24 March 2022 / Published: 26 March 2022
(This article belongs to the Special Issue Sustainability in People Management)
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Versions Notes

Abstract

:
Green finance innovation has received emerging attention from the finance industry in recent years; however, few studies have explored the internal mechanisms that link innovation climate to a green finance R&D team’s effectiveness. Using data from 65 teams that belong to green finance industries, collected via the questionnaire survey, we explore how innovation climate positively affects knowledge sharing, and both innovation climate and knowledge sharing can improve the effectiveness of the green finance R&D team. We also find that knowledge sharing mediates the relationship between innovation climate and a green finance R&D team’s effectiveness and that knowledge heterogeneity moderates the relationship between knowledge sharing and team effectiveness. Based on these findings, this study contributes to providing useful recommendations for professional managers and policymakers to effectively promote the development of the green finance industry.

1. Introduction

In recent decades, environmental issues have increasingly gained attention because of continuous global warming and higher energy consumption and production [1,2]. The balance between resources, the environment, and economic prosperity should be carefully considered in social development. Currently, China aims to promote domestic green economic transformation by achieving the national strategic goal of carbon emissions reaching a peak by 2030 and carbon neutrality before 2060 [3,4]. In this context, heavily polluting manufacturing enterprises need to shoulder the responsibility to protect the environment. Therefore, scholars and practitioners suggest addressing the environmental protection requirements of businesses and developing green technology production [5,6]. Therefore, an increasing number of companies and organizations across a wide range of industries are beginning to focus on green technology innovation in China. New energy sectors such as clean energy, energy-saving, green buildings, and green transport are growing spectacularly in China [7,8]. Besides government support, more funding from social capital needs to be involved in the development of green industries [9,10]. Accordingly, green finance products are being developed to meet the requirements of sustainability. Green finance products refer to financial services, such as project investment, financing, project operation, and risk management, that support production activities that protect the environment, support efficient energy conservation, and promote sustainable development [3,11].
In certain respects, the development of new green finance products has been deemed essential in the innovation and development of financial enterprises. R&D teams are usually considered the basic carrier of new product research and development [12]. Here, diversified outstanding talents with different professional skills are integrated to construct an R&D team for green Finance Products. It is beneficial to form a heterogeneous knowledge pool in a team [13]. Meanwhile, the success of new product development depends greatly on the richness of collective knowledge and innovative technology resources within teams [14]. How can we accelerate the innovative actions in green finance products’ R&D teams? Is there an effective and cost-saving approach to enhance innovation teams’ problem-solving abilities?
The cumulative evidence has proven a positive role of the innovation climate in an organization’s or team’s effectiveness in certain conditions [15,16,17,18]. According to Vegt et al. [19] and Kuenzi and Schminke [20], the team innovation climate is defined as the shared cognition of the team processes, management behaviors, and other environmental factors that promote the generation and the implementation of new ideas. By shaping and cultivating a work climate of innovation, team members’ internal creative motivation can be stimulated [21], and all members would bravely strive for the ultimate collective goal [22]. Researchers have classified the measurement of innovation climates into two types: the cognitive schemata approach [23] and the shared perception approach. In the financial technology innovation management field, previous research has mostly focused on various product themes’ development, the progress of information technology applications, and legal/regulatory alternation [24]. The corresponding theoretical underpinnings of green finance products’ R&D teams’ management are limited, which may result in different conclusions being reached. Accordingly, the internal mechanism in which innovation climates positively influence finance products’ R&D teams’ effectiveness requires further investigation.
According to on organizational learning theory, project-based teams benefit by sharing and integrating explicit and tacit knowledge among team members [25]. Knowledge sharing, defined as the process of understanding, absorbing, and utilizing different types of knowledge [26], aims to put shared knowledge into project activities [27]. It has been proved to improve team effectiveness in the contexts of various projects [28,29,30]. However, the lack of expertise may lead to project failure [31]. The development process of team knowledge is formed by manifesting private knowledge into the team’s knowledge pool. Additionally, integrated knowledge is available to all team members. Therefore, knowledge heterogeneity refers to the degree to which members’ knowledge is different from that of other members [32], which potentially moderates the positive link between knowledge sharing and team effectiveness.
Accordingly, the objective of this research is to clarify the underlying mechanisms of innovation climates for team effectiveness for the green finance products of R&D teams within a comprehensive theoretical framework. Furthermore, the roles of knowledge sharing and knowledge heterogeneity are studied in this paper. The main contributions of this paper are as follows: First, this study enriches the research results regarding team management (e.g., Zhao et al. [33], Gao et al. [34], and Newman et al. [35]), especially for the green finance industry. It extends our current understanding of how to improve team effectiveness using a cost-saving but effective approach. Second, from the special perspective of knowledge integration, this study incorporates the mediating effect of knowledge sharing and the moderating effects of knowledge heterogeneity into a framework to explore the intrinsic connection between innovation climates and team effectiveness for green finance products’ R&D teams. Finally, this study provides multiple managerial implications for enterprises and governments to drive the development of the green finance innovation industry.
This objective is achieved as follows: Section 2 introduces a literature review and presents hypotheses with a conceptual model. Section 3 and Section 4 present the methodology and empirical results, respectively. Section 5 offers discussion and implications.

2. Literature Review and Hypotheses

2.1. Innovation Climate and Knowledge Sharing

An innovation climate is the subjective experience of employees in terms of whether the organization has an innovative environment or not [36]. Based on Amabile [36], Liu and Shi [37] defined an organizational innovation climate as the subjective perceptions that individuals generate about organizational policies, management behaviors, organizational processes, and other elements that support innovation in the organizational environment. According to these definitions, an innovation climate is essentially the sense of social support that employees receive in their work environment, including support from colleagues, support from supervisors, and support in terms of organizational philosophy [38].
For a project-based team, knowledge sharing is a process of existing knowledge exchange and new knowledge creation within individuals, including both knowledge contribution and knowledge acquisition [39]. Knowledge contribution refers to individuals actively communicating their intellectual capital with other colleagues, and knowledge acquisition refers to individuals actively consulting with other colleagues to share the intellectual capital owned by others [40]. Knowledge contribution and knowledge acquisition reflect a two-way process of knowledge sharing.
According to the research of Taylor and Wright [41], an innovative organizational culture is a vital driver of knowledge sharing, and therefore, a team innovation climate promotes knowledge sharing among team members. According to the theory of reasonable action (TRA), the subjective norms of actors become important variables in predicting their specific behaviors [42]. Innovation reflects an organization’s encouragement of creativity and new approaches, and organizational innovation climate is a subjective perception and description of the innovative nature of an organization’s environment that influences individual attitudes, beliefs, motivations, values, and innovative behavior [43]. Lin [44] suggested that organizational support has a significant positive relationship with employees’ willingness to share knowledge. Moreover, organizational climate is positively related to knowledge-sharing behavior [45,46]. Janz and Prasarnphanich [47] showed that the support and responsibility climate in the organization has a positive effect on the cooperative learning behavior (knowledge sharing) in the organization.
When employees are in a work environment with a supportive climate, they are stimulated to develop their potential as well as generate new ideas. They will actively communicate with other employees and will share their knowledge [48]. An organizational innovation climate is perceived by organizational members as benefitting the whole team and encourages employees to share their views and ideas [49,50]. Management support and culture are both contributing factors to knowledge sharing [51].
Based on the above information, the following hypothesis is proposed:
Hypothesis 1 (H1).
Innovation climate is positively related to knowledge sharing.

2.2. Knowledge Sharing and Team Effectiveness

Team efficacy is an extension of the concept of self-efficacy. Based on self-efficacy theory and social learning theory, individual behavior is not only motivated by human behavior but also incorporates the role of cognition [52]. In the process of the formation of human behavior, people’s cognition involves expectations of their behavioral abilities and outcomes, a process known as self-efficacy [53]. According to self-efficacy, team effectiveness, which is not an actual ability possessed by an individual, is not an actual ability or skill possessed by the team but rather a subjective perception and evaluation of that ability or skill by team members [54,55]. In addition, a very important part of the human learning process is observational learning [56]. According to Bandura’s triadic reciprocal determinism, team efficacy is an outcome of the interaction of cognition, behavior, and the environment. Bandura [57] defined team efficacy as the team’s shared beliefs about the organization and the joint ability to implement the behavioral processes needed to produce a certain level of achievement. Team effectiveness refers to a team member’s belief in the ability to achieve a specific level of performance in a given situation in combination with their team [58]. That is, team effectiveness refers to team members’ collective evaluation and shared perceptions of the team’s capabilities and levels, which are gradually developed in the process of accomplishing the team’s tasks and goals.
Knowledge sharing is a communication process between knowledge sharers and receivers, in which knowledge sharers externalize their knowledge by giving lectures, teaching, and writing, and knowledge receivers internalize the received knowledge by listening, imitating, and reading [59]. Knowledge sharing behavior in teams is a team-based communication behavior, emphasizing cooperation and coordinated communication [60].
As knowledge sharing is a communication process, communication helps members of the organization to quickly understand and grasp each other’s information, experience, and skills, to stimulate thinking and promote the generation of new knowledge within the organization. Marijn Poortvliet et al. [61] concluded that sharing valuable knowledge and skills with other colleagues inevitably leads to one’s future access to valuable information from other colleagues as well. By sharing knowledge, team members acquire the knowledge of other team members and improve the team’s skills, and previous research has suggested that improving team skills increases the team’s belief in their ability to accomplishing tasks and their team effectiveness [62,63]. Based on social network theory [64], knowledge sharing gives sharers access to diverse knowledge and information. By sharing with others, team members can not only expand the boundaries of their knowledge acquisition but also put themselves at key nodes in the knowledge transfer network, which can improve their self-efficacy [65].
Taylor and Wright [41] revealed that the culture and climate of knowledge sharing is conducive to innovative performance in organizations and that adequate knowledge sharing can facilitate the generation of new knowledge. Knowledge sharing allows for the flow of knowledge through individuals and fosters the generation of novel ideas, thus helping to stimulate innovative behavior in employees [66]. In the process of knowledge sharing, disagreement can not only further develop one’s ideas but also facilitate the formation of new ideas [67]. Janssen and Yperen [68] also argued that innovation performance involves the development and application of new knowledge and strategies learned. Thompson and Heron [69] found that knowledge sharing has a significant impact on employees’ innovation performance.
Since green finance team members have different knowledge structures, knowledge sharing can make full use of each other’s knowledge resources and capabilities, preventing the waste of resources caused by the repeated production of knowledge and reducing the acquisition cost and innovation cost of knowledge, thus contributing to the generation of innovative behaviors. The process of knowledge sharing is used to break the barriers between knowledge owners and aims to achieve the free knowledge flow within a certain range, which plays a vital role in organizational learning, knowledge creation within the organization, and organizational performance improvement [70].
Based on the above analysis, it can be found that knowledge sharing can enhance the ability of oneself and one’s team members, improve self-efficacy, and also promote innovative behavior and innovative performance, all of which can affect team effectiveness. Therefore, the following hypothesis is proposed:
Hypothesis 2 (H2).
Knowledge sharing is positively related to team effectiveness.

2.3. Innovation Climate and Team Effectiveness

Team effectiveness is an objective assessment of team output [71] and a psychological expectation of team members [72]. As an objective assessment of team output, team effectiveness is an objective evaluation of team performance. When team performance improves, team effectiveness, which is an objective evaluation of team performance, also improves. Innovation climate positively promotes the innovative behavior of employees [73]. Since an innovation climate can improve team performance [20], it can also improve team effectiveness. Support from leaders can enhance the innovative outcomes of employees [74]. When team members perceive support in the team’s innovation climate from the team, supervisors, and organization, they can perceive the team’s capabilities and therefore have certain psychological expectations of the team’s capabilities and beliefs that they can achieve a certain level of achievement. As team effectiveness is a psychological expectation of team members, the more support is received, the more team effectiveness is perceived.
Innovation is correlated with team effectiveness when mediated by the clarity of purpose and commitment, and the higher the clarity of purpose and commitment, the stronger the positive effect of innovation on team effectiveness [15]. It has been shown that the innovation climate is correlated with team effectiveness. For example, innovation is correlated with team effectiveness when mediated by the clarity of purpose and commitment, and the higher the clarity of purpose and commitment, the stronger the positive effect of innovation on team effectiveness [15]. Therefore, a team innovation climate can enhance team effectiveness.
Based on the above analysis, the following hypothesis is proposed for green finance products’ R&D teams:
Hypothesis 3 (H3).
Innovation climate is positively related to team effectiveness.

2.4. Mediating Effect of Knowledge Sharing

As H1 and H2 suggest, direct relationships between an innovation climate and knowledge sharing and between knowledge sharing and team effectiveness have been established. This demonstrates that knowledge sharing mediates the impact of an innovation climate on team effectiveness. Knowledge sharing resulting from an innovation climate can help improve work performance [48] and thus increase team effectiveness.
Knowledge sharing often plays a critical role on the research of team innovation or effectiveness improvement. Research has found that through a collaborative culture, knowledge sharing can improve team members’ ability to innovate and increase innovation output [75]. The relationship between transformational leadership and employee effectiveness is significantly positive and there is an even stronger mediating relationship with knowledge sharing [76,77]. In addition, knowledge sharing mechanisms can stimulate employee creativity, and new knowledge can supplement individual knowledge gaps and maximize organizational knowledge benefits by improving individual and organizational performance [78].
Therefore, this study argues that knowledge sharing, enhanced through an innovation climate, mediates the relationship between an innovation climate and team effectiveness. The following hypothesis is proposed:
Hypothesis 4 (H4).
The impact of innovation climate on team effectiveness is mediated by knowledge sharing.

2.5. Moderating Effect of Knowledge Heterogeneity

Knowledge heterogeneity has been defined as the variety of knowledge, know-what, know-how, and expertise [79,80]. Some scholars believed that heterogeneous knowledge makes up for the lack of knowledge required for enterprise innovation and can provide more novel and diverse problem solutions, which is conducive to the improvement of enterprise innovation performance [81,82]; other scholars emphasized the additional costs incurred by enterprises in acquiring and utilizing external heterogeneous knowledge resources, as well as the negative effects on enterprise innovation performance, and they proposed that the relationship between heterogeneous knowledge and enterprise innovation performance should be non-linear [83,84,85]. Ye, Hao, and Patel [80] divided knowledge heterogeneity into external knowledge heterogeneity and internal knowledge heterogeneity and then clarified that the relationship between external knowledge heterogeneity and innovation performance is inverted-U-shaped and the relationship between internal knowledge heterogeneity and innovation performance is linear.
Because the knowledge sharing caused by an innovation climate within a team is internal team knowledge sharing, the knowledge heterogeneity of team members leads to different perspectives, knowledge, insights, and levels of different members’ views on problems, which can make the whole team consider problems more comprehensively. Based on the perspective of cognitive resource theory, knowledge heterogeneity can bring diverse knowledge to the team, prompting individuals to analyze problems from multiple perspectives and develop a comprehensive understanding of them [86]. Therefore, this knowledge heterogeneity moderates the impact of knowledge sharing on team performance, which in turn affects team effectiveness. Therefore, we proposed the following hypothesis:
Hypothesis 5 (H5).
Knowledge heterogeneity moderates the effect of knowledge sharing on team effectiveness.
The conceptual model is shown in Figure 1.

3. Methodology

3.1. Sample and Data Collection

This research collected data from the green finance industry in China. In order to reduce the common method bias, data were collected from two sources including field surveys and online questionnaires [87,88]. The survey was carried out from July to December 2021. In the first stage, we conducted a field questionnaire on a team-by-team basis. To avoid homologation problems, the questionnaires were collected from both team leaders and members [89]. Respondents were asked to answer the questionnaires based on their position only, without leaving their names. This survey considered the green finance product’s R&D team of the five experts in the pilot study as the initial respondents, and the snowball approach is used to distribute the questionnaire. The sample was from five Chinese cities, including Shanghai, Beijing, Hangzhou, Shenzhen, and Guangzhou. The number of people interviewed per team was between 4 and 10. Referring to previous research [89], team leaders mainly evaluated project information, innovation climate, and team performance, and other team members mainly evaluated the degree of knowledge sharing and knowledge heterogeneity, team members’ work engagement, and satisfaction. In the first stage, the survey results of 52 teams were collected, and the data from three teams were deleted due to incompleteness. In the next stage, the survey link on the Questionnaire Star platform was sent randomly to 500 individuals from a green finance professional association by email. The item Project Title in the questionnaire was used to associate the different questionnaires from one team. A total of 102 responses were returned, and 22 teams participated in our survey. Finally, six invalid responses were deleted.
Next, a t-test was used to compare the responses of the two collection methods in terms of innovation climate, knowledge sharing, knowledge heterogeneity, and team effectiveness [88]. The results showed that differences were insignificant. Further, Harman’s single-factor method was employed to eliminate potential common method bias with single-factor analysis. All indicators were loaded on a single factor. The highest variance for all team member reported variables was 25.913%. The result was under half of the total explained variance, indicating no serious concerns [87,90]. Finally, 65 valid team responses, including 65 team leader questionnaires and 375 team member questionnaires, were used in the final analysis, with a response rate of 48.67 percent. The descriptive information of the sample respondents and projects is shown in Table 1.
The production types of the sampled teams were Credit (32.30 percent), Bonds (27.69 percent), Insurance (24.61 percent), and others (15.4 percent). Regarding product R&D duration, 13.85% of the samples lasted 3 months or less, 32.31% lasted 3~12 months, 35.38% lasted 13–24 months, and 18.46% lasted more than 24 months. Furthermore, the size of 13 teams was less than or equal to 5 persons, the size of 13 teams was more than 5 persons but less than 10 persons, the size of 24 projects was more than 11 persons but less than 20 persons, and the size of 15 teams was more than 20 persons, which corresponds to rates of 20.00%, 20.00%, 36.92%, and 23.08%, respectively. Furthermore, most respondents were male (64.32%), and the majority of these respondents (79.10%) held an undergraduate degree or higher.

3.2. Measures

In this research, measurement scales were used that originated from the literature review so as to ensure the validity of the scales. The translation committee approach was used because respondents were from China [88]. Two professors majoring in financial technology were invited to translate the English scale into Chinese. Then, the translation committee corrected the differences between the two Chinese versions to achieve a consensus. Next, the Chinese scale version was translated back into English by three Ph.D. candidates in financial technology. Moreover, to ensure the validity and reliability of the scale, a pilot study was implemented involving 18 respondents who were five experts from green finance enterprises in Shanghai, five financial technology researchers, and eight Ph.D. candidates from three renowned Chinese universities. The final version of the questionnaire items is detailed in Appendix A.
According to Amabile [36], Liu and Shi [37], and Popa, Soto-Acosta, and Martinez-Conesa [91], we propose an operational definition of innovation climate as the extent to which team members’ subjective perceptions of innovation support are generated in the organizational environment. We measured the innovation climate using four items from Popa, Soto-Acosta, and Martinez-Conesa [91], aiming to create a team climate to integrate different experts, facilitate knowledge sharing, and generate innovative ideas. We define the operational definition of knowledge sharing as the extent of activities that transfer knowledge, exchange knowledge, and create knowledge among team members [39,92]. The scale for knowledge sharing was adopted from the study of Chuang, Jackson, and Jiang [92]. An operational definition of knowledge heterogeneity is proposed based on Ye, Liu, and Tan [48], Zhang, Wang, and Hao [79], and Zhao et al. [93]. It is the degree of diversity of collective knowledge such as knowledge, know-what, know-how, and expertise in an organization perceived by team members. The knowledge heterogeneity items were adopted from the research of Zhao, Huang, Xi, and Wang [93]. In light of Bandura [58], Gladstein [71], and Tjosvold [72], the operational definition for team effectiveness is an objective assessment including team performance, capabilities, and psychological expectations of team members. Team effectiveness was measured by 11 items from the research of Ding [94]. Furthermore, seven-point Likert scales were used to measure all variables.
To isolate the effects of the project’s situational factors, product type, product R&D duration, and team size were selected as control variables, and product type was used to differentiate the impact of research objective by different product types. Team size was measured by a number of members, and product R&D duration was reflected by the life cycle of the team.

4. Data Analysis and Results

To clarify the succession of the stages completed in data analysis, the main processes of statistical analysis methods are illustrated in Figure 2.

4.1. Reliability and Validity Tests

Cronbach’s coefficient was used to measure the reliability of the scale by SPSS22.0 software, which included four variables: team innovation climate, knowledge sharing, knowledge heterogeneity, and team effectiveness. As shown in Table 2, the values of Cronbach’s alpha for the four variables all exceeded 0.7, which presented good reliability, as recommended by Fornell and Larcker [95].
Further, the validity of the scales was tested by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The values of KMO for innovation climate, knowledge sharing, knowledge heterogeneity, and team effectiveness were 0.772, 0.803, 0.832, and 0.829, respectively, all with a p-value of 0.000. Thus, the structural validity met the requirements. Moreover, the composite reliability (CR) values of all variables was greater than 0.7, the average variance extracted (AVE) values of all variables exceeded 0.5, and the factor loading (FL) values of all items under their respective variables were greater than 0.6, thus implying the good convergent validity of all constructs [96]. As shown in Table 3, the FL values for all items under their respective variables were significantly higher than those taken under other variables. The square root of AVE exceeded inter-construct correlations. Moreover, the four-factor model resulted in a relatively good fit to the data (χ2/df = 1.947, IFI = 0.953, TLI = 0.942, CFI = 0.951, RMSEA = 0.051) and was better than competing models when some variables were combined into one factor. This indicated good discriminant validity [97].

4.2. Aggregation Tests

As described above, this research adopted data from team leaders and members. Referring to the recommendations from James et al. [98] and LeBreton and Senter [99], the intergroup variation and homogeneity tests were required for the aggregation data at the team member level. As shown in Table 4, for the variables knowledge sharing, knowledge heterogeneity, and part of team effectiveness, the values of Rwg (within-group interrater) exceeded 0.7. values of ICC (1) (the reliability of score within team) and ICC (2) (reliability of mean group score); i.e., they were higher than 0.5. Therefore, using knowledge sharing, knowledge heterogeneity, and team effectiveness1 (including members’ work engagement and satisfaction) at the team level is acceptable.

4.3. Results

Table 3 illustrates the descriptive statistics and the correlation coefficients of the constructs. The results present that team effectiveness is significantly correlated with innovation climate, knowledge sharing, and knowledge heterogeneity. The results also show that team innovation climate is significantly related to its knowledge heterogeneity, as well as to its knowledge sharing. Following Cohen et al. [100], Hu et al. [101], and Xie, Huo, and Zou [6], this study used hierarchical regression analysis to examine the mechanisms of innovation climate, knowledge sharing, and team effectiveness. The steps are as follows. First, the relationships between innovation climate and team effectiveness, innovation climate, and knowledge sharing, knowledge sharing, and team effectiveness were tested to clarify whether they were significant. Second, when all the relationships above were significant, innovation climate and knowledge sharing were brought into the regression model at the same time. If the effect of innovation climate on team effectiveness was still significant but less strong, it indicated that knowledge sharing played a partial mediating role. If the effect of an innovation climate on team effectiveness was not significant, This indicated that knowledge sharing played a complete mediating role.
Table 5 shows the regression results of the proposed model in this study. The results of model 1 in Table 5 show the effect of control variables on team effectiveness. Model 5 in Table 5 was used to test the relationship between an innovation climate and knowledge sharing, and the regression results (β = 0.602, p < 0.001) indicate a significant positive correlation; thus H1 is supported. Meanwhile, the results of Model 3 in Table 5 indicate that knowledge sharing is positively associated with team effectiveness (β = 0.287, p < 0.001), so H2 is supported. Further, the results of Model 2 in Table 5 demonstrate that an innovation climate is significantly related to team effectiveness (β = 0.656, p < 0.001), and thus H3 is supported.
Moreover, the results show that knowledge sharing mediates the relationship between innovation climate and team effectiveness [102]. On one hand, an innovation climate is positively associated with team effectiveness and has a positive influence on knowledge sharing. On the other hand, knowledge sharing is positively related to team effectiveness, within the coefficient of innovation climate becoming smaller when knowledge sharing is entered into Model 3 in Table 5. On this basis, we suggest that H4 is supported. The results demonstrate that an innovation climate has the potential not only to affect team effectiveness directly but also to influence team effectiveness via knowledge sharing. Knowledge sharing plays a partially mediating role between innovation climate and team effectiveness.
Next, the moderating effect of knowledge heterogeneity was tested by controlling for product type, team size, and product R&D duration. The results of Model 4 in Table 5 shows that knowledge heterogeneity enhances the relationship between knowledge sharing and team effectiveness (β = 0.186, p < 0.01), so H5 is supported. Figure 3 shows that the positive impact of knowledge sharing on team effectiveness is more significant among teams with a better knowledge heterogeneity, indicating that knowledge sharing is more useful when the green finance R&D team has a better knowledge heterogeneity. Figure 4 presents the result path of the model.

5. Discussion and Conclusions

In this research, using a questionnaire survey to capture the data from a green finance product’s R&D team in China, we show that an innovation climate has a positive impact on knowledge sharing and that teams’ innovation climate and knowledge sharing can both improve team effectiveness. The theory of TRA, organizational learning theory, and self-efficacy theory can be used to explain the positive impact on team effectiveness improvement [14,49]. According to TRA theory, a project-based organization’s or team’s climate, including equity climate, innovation climate, and interpersonal climate, has an impact on members’ knowledge-sharing behavior in companies [49]. In addition, the positive effects of knowledge sharing on team effectiveness are verified. Based on social network theory, diverse knowledge and information are shared [64]. In the process of knowledge sharing, team members can not only expand the boundaries of their knowledge acquisition but also put themselves as a hub in the knowledge transfer network, which can improve their self-efficacy and team collective effectiveness [65].
We also find that knowledge sharing mediates the relationship between an innovation climate and team effectiveness. Knowledge sharing among team members enhances the impact of innovative atmosphere on team effectiveness. Hence, an innovation climate should be created with knowledge sharing to ensure further research and development for green finance products.
In addition, we find that knowledge heterogeneity moderates the relationship between knowledge sharing and team effectiveness. Information decision theory can help us explain the above moderating relationship. Knowledge heterogeneity can stimulate creativity and innovation, lead to the collision of team members’ thinking, and generate new ideas and approaches [33]. A single knowledge structure can lead to a lack of endogenous power for team innovation; thus it is necessary to continuously improve the level of knowledge diversity within an R&D team to match the knowledge needs of new product development [33]. Therefore, for green financial R&D teams, knowledge heterogeneity will improve the collective effectiveness with the widely shared knowledge of the team.

5.1. Theoretical Contributions

The theoretical contributions of this study are as follows. First, our research extends previous research by investigating the indirect effect of a team innovation climate on collective effectiveness [33,34]. Although the issue of team innovation climate and team effectiveness has received attention for years [34,35], we conduct a study on a project-based emerging team, that is, the green financial R&D team. We explore the direct effect of innovation climates on team effectiveness, as well as the indirect effect. This study confirms that innovation climate improves team effectiveness in green financial R&D team as a useful driver that expands the research boundary for team management. Moreover, the findings of this study enrich our understanding of team climate in green financial R&D teams based on organizational behavior and team management theories.
In this study, knowledge sharing is considered a partial mediator in the path of the relationship between innovation climate and team effectiveness [48,75]. Thus, it is necessary to enhance the level of knowledge sharing in the team in order to exert a positive influence of the innovation climate and improve team effectiveness. The findings in this study complement the research on self-efficacy realization; further, new knowledge on the issue of motivation and the role of knowledge sharing at the team level are generated. The theoretical and practical analysis framework constructed in this study examines the important impact that the interaction between knowledge heterogeneity and knowledge sharing can have on team effectiveness. It shows that the contribution of knowledge within a team is far from sufficient in green financial R&D teams, and the level of knowledge heterogeneity among team members needs to be continuously improved to enhance team effectiveness. Meanwhile, the joint role of task performance (e.g., duration and cost for the task completion) and team members’ perceptions (e.g., satisfaction for the teamwork and commitment for the team objective) is balanced, which enriches the meaning of the concept of team effectiveness from the perspective of a green financial R&D team.

5.2. Managerial and Practical Implications

The study offers several managerial and practical implications to improve the management of green finance products’ R&D teams. First, in recent decades, industries have faced increasing pressure to be greener and have more sustainable development [103]. Because of this, green financial products are currently in greater supply than demand. Financial companies should focus on the R&D of green financial products, which is also an important way to promote the development of innovation in the financial industry. Second, since enterprise innovation climates and heterogeneous knowledge sharing are important to enhance the effectiveness of green financial R&D teams, financial enterprises can give priority to reasonable member combinations with greater knowledge heterogeneity when forming R&D teams so that the teams have diverse knowledge and rich information channels in the process of innovation practice. Meanwhile, financial enterprises should build a platform conducive to knowledge sharing and exchange activities and cultivate a good team knowledge sharing climate [33]. Third, as the P/E ratio of green financial products is currently lower than other types of financial products due to policy guidance, policymakers should design more effective mechanisms beyond green subsidies to incentivize and push green investment strategies in the financial sector [6]. Thus, the government can listen to practitioners’ opinions from specific theories to better recognize the difficulties faced by financial firms in developing green financial products.

5.3. Limitations and Future Research

This research provides an important theoretical model for green finance products’ R&D teams’ management in the era of sustainable social development. However, there still are a few limitations that can direct future research. First, in this study, the data were collected from two sources in order to reduce common-method bias. According to previous studies, multiple-time data collected can effectually evade the risk of common method bias. Future research could use two point-in-time access questionnaires to eliminate these concerns [104,105]. Second, team knowledge sharing is a complex and dynamic process, and this paper provides an inference through the survey method of cross-sectional data; thus, future researchers are suggested to study the phenomenon of team knowledge sharing under dynamic games from the whole life cycle for project development. In addition, the knowledge sharing process consists of two phases, including knowledge contribution and knowledge gathering. It is suggested that researchers explore the roles that two segmentation variables play in this model, and the possible interactions between the two variables can be further discussed. Third, the respondents of the survey were all from China and were not from various countries or other cultural contexts. Future studies could test the proposed framework with data from different countries and regions to enrich the generalization of the results in this study.

Author Contributions

Conceptualization, methodology, investigation and data curation, writing—original draft, X.D. and W.L.; writing—review and editing, D.H. and X.Q.; funding acquisition, X.D. D.H., and X.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shanghai Fintech Research Center Funding grant number 2021-JK07-A, National Natural Science Foundation of China grant number 72002001, Anhui Natural Science Foundation grant number 2008085QG339, Universities Natural Science Research Project of Anhui Province grant number KJ2020A0009, China Postdoctoral Science Foundation grant number 2019M663483, the Scientific and Technological Research Program of Chongqing Municipal Education Commission grant number KJQN202100816, the Planning project for the 14th five year plan of Chongqing education sciences grant number 2021-GX-025.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

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

Data Availability Statement

The original data are provided by all four authors. If there are relevant research needs, the data can be obtained by sending an email to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Measurement Items.
Table A1. Measurement Items.
VariablesItemMeasurements
Innovation climateIC1Time and resources are provided for team members to generate, share/exchange, and experiment with innovative ideas/solutions.
IC2Team members are working in diversely skilled workgroups where there is free and open communication among each other.
IC3Team members frequently encounter non-routine and challenging work that stimulates creativity.
IC4Team members are recognized and rewarded for their creativity and innovative ideas.
Knowledge sharingKS1Team members share their special knowledge and expertise.
KS2If a member of our team has some special knowledge about how to perform the team task, he/she will tell other members about it.
KS3There is virtually no exchange of information, knowledge, or sharing of skills among team members. (R)
KS4More knowledgeable team members freely provide other members with hard-to-find knowledge or specialized skills.
KS5Team members provide a lot of work-related suggestions to each other.
KS6There is a lot of constructive discussion during team meetings.
KS7Team members provide their experience and knowledge to help other members find solutions to their problems.
Knowledge heterogeneityKH1The knowledge that is disparate, useful, and relevant is readily accessible.
KH2There are more new ideas for building causal understanding.
KH3There is rich expertise for exchanging and communication.
KH4Knowledge elements are beneficial for executing complex tasks.
KH5Diverse knowledge elements improve creative potential.
KH6Abundant knowledge elements enhance opportunity recognition for innovation.
Team effectivenessTE1Team members work effectively.
TE2Team members put considerable effort into their jobs.
TE3Team members are concerned about the quality of their work.
TE4Team members meet or exceed their productivity requirements.
TE5Team members are committed to producing quality work.
TE6Team members do their part to ensure that their products will be delivered on time.
TE7Team members are very satisfied with their work.
TE8Team members feel a strong commitment to their work.
TE9Team members feel highly committed to the goals of their work.
TE10The way we manage our work inspires us to better job performance.
TE11All things are considered, the team is highly pleased with the way it manages its work.

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Main processes of statistical analysis methods.
Figure 2. Main processes of statistical analysis methods.
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Figure 3. Moderating effect of knowledge heterogeneity on the relationship between knowledge sharing and a team’s team effectiveness.
Figure 3. Moderating effect of knowledge heterogeneity on the relationship between knowledge sharing and a team’s team effectiveness.
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Figure 4. Result path of the model. Note: The coefficients are normalized. ** p < 0.01, *** p < 0.001.
Figure 4. Result path of the model. Note: The coefficients are normalized. ** p < 0.01, *** p < 0.001.
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Table
Table 1. Descriptive statistical analysis.
Table 1. Descriptive statistical analysis.
MeasureItemPercentage
GenderMale64.32%
Female35.68%
Highest educationCollege degree or below20.90%
Undergraduate or above45.00%
Master’s degree 34.10%
Work experience1~2 year(s)14.09%
3~5 years39.09%
6~10 years34.55%
Over 10 years12.27%
Product typeCredit32.30%
Bonds27.69%
Insurance24.61%
Others15.40%
Product R&D duration3 months or less13.85%
3~12 months32.31%
13~24 months35.38%
25 months or more18.46%
Team size5 persons or less20.00%
6~10 persons20.00%
11~20 persons36.92%
More than 20 persons23.08%
Position in the teamManager or above14.77%
Technical engineer60.22%
Other staff25.01%
Table
Table 2. Results of reliability and validity test.
Table 2. Results of reliability and validity test.
ConstructItemFL AVECRCronbach’s α
Innovation climateIC10.7920.5900.8520.800
IC20.753
IC30.742
IC40.784
Knowledge sharingKS10.8160.6490.9280.879
KS20.805
KS30.806
KS40.820
KS50.763
KS60.807
KS70.822
Knowledge heterogeneityKH10.7450.5280.8700.767
KH20.723
KH30.767
KH40.691
KH50.729
KH60.702
Team effectivenessTE10.8500.6950.9620.894
TE20.808
TE30.824
TE40.839
TE50.862
TE60.800
TE70.834
TE80.852
TE90.801
TE100.832
TE110.865
Table
Table 3. Descriptive statistics and correlations.
Table 3. Descriptive statistics and correlations.
VariablesDescriptive StatisticsCorrelation Coefficients
MeanS.D.F1F2F3F4F5F6
F1 Team effectiveness5.7520.323(0.834)
F2Team size2.6311.241−0.108—
F3 Product R&D duration2.5851.1140.0140.365—
F4 Innovationclimate3.5020.4670.605 **−0.1500.062(0.768)
F5 Knowledge sharing3.4860.5120.695 **−0.1420.0220.465 **(0.806)
F6 Knowledge heterogeneity3.7120.3660.582 **0.328 **0.265 *0.610 **0.626 **(0.727)
Note: ** p < 0.01(2-tailed); * p < 0.05(2-tailed). The data in the diagonal brackets are the square root of the AVE value for each variable.
Table
Table 4. Results from aggregation test at team-level.
Table 4. Results from aggregation test at team-level.
IndicatorsKnowledge SharingKnowledge HeterogeneityTeam Effectiveness 1
Rwg0.7980.7550.818
ICC (1)0.1760.1660.198
ICC (2)0.6790.6500.690
Notes: Team effectiveness 1 are the aggregation test result from team member evaluation question items.
Table
Table 5. Results of Regression Analysis.
Table 5. Results of Regression Analysis.
VariablesTeam EffectivenessKnowledge Sharing
Model 1Model 2Model 3Model 4Model 5
Controls
Product type−0.102 **−0.116 **−0.131 **−0.1030.017
Team size0.135 **0.0720.120 *0.1020.080
Product R&D duration−0.128 **−0.050−0.0290.091−0.075
Predictors
Innovation climate 0.656 ***0.292 ***0.656 ***0.602 ***
Knowledge sharing 0.287 ***0.206 ***
Moderators
Knowledge heterogeneity 0.123 **
Knowledge heterogeneity× Knowledge sharing 0.186 **
F-value3.162 *7.942 ***12.608 ***11.226 ***10.742 ***
R squared0.0520.2280.3660.3350.235
Adjusted R squared0.0240.1060.2430.2640.240
Note: The coefficients are normalized. * p < 0.05, ** p< 0.01, *** p < 0.001.
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Ding, X.; Li, W.; Huang, D.; Qin, X. Does Innovation Climate Help to Effectiveness of Green Finance Product R&D Team? The Mediating Role of Knowledge Sharing and Moderating Effect of Knowledge Heterogeneity. Sustainability 2022, 14, 3926. https://doi.org/10.3390/su14073926

AMA Style

Ding X, Li W, Huang D, Qin X. Does Innovation Climate Help to Effectiveness of Green Finance Product R&D Team? The Mediating Role of Knowledge Sharing and Moderating Effect of Knowledge Heterogeneity. Sustainability. 2022; 14(7):3926. https://doi.org/10.3390/su14073926

Chicago/Turabian Style

Ding, Xue, Wei Li, Dujuan Huang, and Xinghong Qin. 2022. "Does Innovation Climate Help to Effectiveness of Green Finance Product R&D Team? The Mediating Role of Knowledge Sharing and Moderating Effect of Knowledge Heterogeneity" Sustainability 14, no. 7: 3926. https://doi.org/10.3390/su14073926

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