1. Introduction
With the rapid development of the economy, the natural environment faces a severe threat, and the contradiction between economic growth and the environment is increasingly becoming eminent [
1,
2]. Combining the relationship between economic growth and the environment and realizing sustainable economic development has become the focus of world attention [
3,
4]. This goal can be achieved by ensuring minor resource consumption, minimum negative impact on the environment, and maximum economic benefits [
5]. According to the “decoupling theory,” green innovation is a crucial measure to consider economic development and environmental protection and to realize strong decoupling between them [
6]. Decoupling theory depicts the process of delinking economic growth and carbon dioxide emissions from increasing environmental issues [
7,
8]. Thus, studies have shown that growth is achieved in the early stages of economic development with growing environmental challenges, pollution, and resource exploitation. With the continuous development trend, the economy becomes less harmful to the environment due to investments in technological and economic efficiency [
6]. Green innovation is an innovation guided by the values of sustainable development, centered on saving resources and protecting the environment, and taking the pursuit of sound and rapid development as the starting point and foothold [
9,
10]. It can “force” producers to improve their technical level, carry out cleaner production to promote energy conservation and emission reduction, and then realize gainful economic development and environmental protection [
11,
12,
13,
14,
15].
Simultaneously, green innovation can improve resource efficiency and corporate reputation and improve financial performance [
16]. In addition, the green innovation capability and development level directly affect the economic development of a country or region and its competitiveness in the international community [
17,
18]. Therefore, green innovation is of great significance to the sustainable development of society [
19]. As early as 2005, the current President of China, Xi Jinping, put forward the scientific judgment that “Lucid waters and lush mountains are invaluable assets”. In 2015, “green” was formally put forward as the five development ideas of China for the first time at the Fifth Plenary Session of the 18th CPC Central Committee policies. In 2016, the United Nations officially launched ‘the 2030 Agenda for Sustainable Development’, calling on all countries to take action to explore the road of green and low-carbon sustainable development and jointly write the blueprint for the future. China’s inclusion as a permanent member of the United Nations actively responded to this plan. The report of the 19th National Congress of the Communist Party of China put forward that “building a market-oriented green technology innovation system to enhance green development, promote the construction of ecological civilization, and protecting lucid waters and lush mountains is vital”. Thus, promoting green technology innovation as a strategic decision at the national level. Therefore, this study is based on the essence to deeply explore the influencing factors of green innovation and then find out the merit to promote green innovation [
12,
13,
14].
This study deepens the discussion of the influence of knowledge learning on green enterprise innovation. Knowledge learning is a process of connecting and absorbing specialized information sources for expansion through which knowledge is acquired and applied in a specific field [
20,
21,
22]. Knowledge learning is classified into external green knowledge search and internal green knowledge absorption and integration [
23]. It further suggests that enterprises continuously acquire external new knowledge, new technology, and new ideas through exploratory learning. It then absorbs, integrates, and applies them to the activities of enterprises through utilization learning. Thus, providing new ideas and new directions for green innovation and development of enterprises. Knowledge has the characteristics of concealment. Thus, the sharing of knowledge, skills, and information among enterprises, customers, and suppliers can effectively promote the green innovation behavior of enterprises [
24].
First, although most scholars posited that external knowledge search positively affects enterprises’ green innovation, the literature review revealed that the existing empirical research had not reached a consistent conclusion. Some research results showed that enterprises’ access to external resources positively impacts green innovation [
25,
26,
27,
28]. However, some scholars, from the perspective of the external network, pointed out that the relationship between the external cooperation network depth of enterprises and the green innovation of enterprises are not in a simple linear relationship but an inverted U relationship [
29,
30,
31], and a too-high depth knowledge search would also lead enterprises to rely on strong links with external resources, which would lead to increased opportunism risks for enterprises [
32,
33], and may not be conducive to enterprise innovation [
34,
35,
36].
Secondly, the intensity of internal absorption and integration of knowledge on green enterprise innovation was also different. Some studies showed that the green innovation of enterprises with a high capacity to absorb and integrate external knowledge was significantly higher than that of enterprises with low ability, and the correlation coefficient between the ability to absorb external integration and enterprise innovation was greater than 0.5 [
27,
37]. However, the correlation obtained by some other studies was much weaker [
38,
39]. Different studies showed differences in the intensity of external knowledge search and internal knowledge absorption and integration on green enterprise innovation. Each study adopted other research methods based on various research objects, making the conclusions not universal.
Therefore, it is both practically and theoretically important to synthesize the existing literature and examine the effect of knowledge learning on the green innovation of enterprises. There is no general conclusion to identify the factors that influence the current relationships. It is important to conduct a meta-analytical investigation of the prior literature to synthesize the effects of green resource search breadth, green resource search depth, and green resource absorption and integration on green innovation of enterprises. Simultaneously, this study provides up-to-date findings on which factor is more important to enterprises’ green innovation.
Based on the different results of previous research, the relative importance of these three factors for enterprises’ green innovation and the potential role of the definition of green innovation and research objects through the application of meta-analysis for analysis has several merits. Firstly, this investigation provides an overview of the previous literature on the relationship between knowledge learning and green innovation and puts forward the corresponding assumptions. Secondly, a meta-analysis is conducted to examine these three factors’ respective and relative effects on enterprises’ green innovation. Thirdly, the study also examines two moderators to draw their moderative effects on the relationship between green resource search breadth, green resource search depth, and green resource absorption and integration on green innovation. Finally, based on the meta-analytical findings, this study provides countermeasures and suggestions for further theory development and managerial practices in enterprises’ green innovation management. Meta-analysis is an empirical research method that systematically collects the statistical indicators of the same kind of research in the past, integrates and analyzes them, and finally draws a more accurate and general conclusion [
40]. It simply counts the average effect value of the relationship between two variables and explores the moderating effect of situational factors on the relationship between research variables. The moderating effects of two potential variables were also analyzed: the definition of green innovation and the research object.
Specifically, this study contributes to the existing body of knowledge by deepening the discussion on the influence of knowledge learning on green enterprise innovation. Additionally, this study broadens the scope of research by analyzing the influence of green resource search breadth, green resource search depth, and green resource absorption and integration on the green innovation of enterprises in China. This study provides an in-depth analysis of the selected variables by analyzing the publication bias heterogenous test effects and applying the “failure safety factor N” test to evaluate the publication bias more accurately. Thus, this study is the pioneer in investigating knowledge learning and green enterprise innovation through the moderation effect of meta-analysis methodology.
The principal objective of this study seeks to explore the direction and intensity of the influence of knowledge learning on green enterprise innovation employing 32 independent empirical literature documents as research samples. Further, this study aims to broaden the knowledge on effective resource consumption while projecting the merits of ensuring minimum negative impact on the environment to achieve maximum economic benefits.
This current study is structured as follows:
Section 1 of this study provides the introduction,
Section 2 outlines the literature review and research hypothesis development,
Section 3 provides the methodology,
Section 4 outlines the results and analysis, and
Section 5 provides the research conclusions and suggestions.
3. Research Methods
3.1. Literature Search and Select
This current study is based on the influence of knowledge learning on Chinese enterprises’ green innovation and provides suggestions that are feasible for the Chinese enterprises’ green innovation development. The following steps were used to search and screen documents to avoid omitting documents as much as possible and ensure complete data collection. (1) Searching documents in China National Knowledge Infrastructure (CNKI) database by theme. The search terms were set as green innovation, green management innovation, green process innovation, green energy innovation, green technology innovation, terminal technology innovation, and low-carbon technology innovation. All the literature related to green innovation were the research objects, and the period was from January 1994 to July 2021, mainly including academic dissertations and journal papers. (2) According to the title and abstract of the paper, preliminary screening was carried out to obtain empirical research related to green innovation, and the full texts of these papers were downloaded. (3) Through browsing the full text for secondary screening, the papers containing both knowledge learning and green innovation and the correlation coefficient between them were selected. (4) After collecting the data, a second comparison was made to avoid wrong data collection.
Figure 1 indicates the flow chart of document retrieval and screening processes.
To ensure the effectiveness of meta-analysis application and to consider the research topic, the documents included in the final meta-analysis must meet the following conditions: (1) It must simultaneously contain any two of the three variables of external knowledge search, internal knowledge absorption and integration, and green innovation. (2) The research object must be related to enterprises. (3) It must be an empirical study and include sample size, correlation coefficient, or other indicators that can be converted into a correlation coefficient. (4) Each study must provide independent data sets. Studies with the same sample size were selected and categorized based on sufficient details. After screening, there were 32 documents suitable for the analysis and 45 independent data sets encoded into meta-analysis. Because some papers were classified under green innovation, the data sets were larger than the documents. The variables K(k) were used in this study to denote the number of studies obtained from the database and the final samples selected and screened.
3.2. Coding Process
Due to various researchers’ different expressions of related concepts, there was some uncertainty in the classification of variables, which made the document coding work subjective to some extent. Simultaneously, there was the possibility of errors caused by a heavy workload. Therefore, to increase the reliability of document coding, two coders were employed in the coding process of this study, and the document coding work was completed according to the following steps: (1) Two coders independently completed data coding; (2) After the first coding, a discussion was made on the inconsistent information until the opinions were consistent, to avoid personal subjective judgment; (3) Comparing the coding results by sampling, and reviewing the original text to check and discuss the questionable results again; (4) 79 effect values were obtained from 45 independent samples in 32 works of literature, and the data were rechecked to complete the final coding. In addition to the basic information of the study, including the authors’ name, publication year, degree thesis and school, or journal name, the encoded data also included different dimensions of variables such as green product innovation, green process innovation, etc. and the related statistics comprising of sample size, correlation coefficient, and Cronbach alpha and possible regulatory variables containing the industry in which the enterprise is located, literature type, research time point, etc. The regulatory variables of this paper were mainly the definition of green innovation, including comprehensive green innovation, green product innovation, green process innovation, green marketing innovation, breakthrough green innovation, and progressive green innovation, and the research objects are resource-based enterprises, manufacturing enterprises, and intelligent or green manufacturing enterprises.
3.3. Meta-Analysis
The meta-analysis, first named by American educationist Glass in 1976 [
108], is a method of reanalyzing data based on several empirical research results of the same problem to obtain a more general conclusion. In this study, the measurement error was corrected by the reliability correction of the correlation coefficient. The heterogeneity test determined the model selection, followed by the publication bias analysis. It was carried out according to the “failure safety factor N”. Finally, the Comprehensive Meta-analysis 2.0 software was employed to analyze the data effectively. The specific process was as follows.
- (1)
Reliability Correction of Correlation Coefficient
Firstly, to correct the deviation of correlation coefficient in different empirical studies, this study adopted the approach of [
109] to evaluate the reliability of each original correlation coefficient of all samples.
where
denotes the reliability of the correlation coefficient of the samples,
is the correlation coefficient of each pair of observed variables in all samples, that is, the effect value;
and
are the Cronbach’s
value of the corresponding explanatory variable and the dependent variable, that is, the reliability value.
Many researchers report that the values of some variables were missing in individual studies where the reliability is affected. This recent study contributes to the body of knowledge and literature by employing the weighted mean of reliability of non-missing variables to fix all missing variable problems. The specific calculation is shown in Formula (2), and the objective reliability was replaced by 1.
Additionally, is the corresponding sample size of each similar study, and denotes the Cronbach’s α value of an observed variable studied.
Meta-analysis mainly adopts the fixed effect model or the random effect model, and the difference between them primarily lies in the different weight components. The former assumes that there is only one true effect value behind all samples in the meta-analysis. The difference in the effect values is considered to be a sampling error. The latter projects that the true effect value of each sample is not the same and the difference is caused by different true effects and sampling errors. This current study projects that using two different models for analysis would make the significance of the average effect value, interval estimation value, and moderating variable in the meta-analysis results differently. Therefore, to avoid the situation that the model would be replaced because the results were inconsistent with the assumptions, it was necessary to determine which model to choose from the theoretical and practical analysis level before the effect analysis.
Conversely, 32 independent research documents were finally selected as samples in this paper. The research objects of the chosen samples were different, comprising of manufacturing enterprises and resource-based enterprises. There were also distinct characteristics of addresses and levels of the same type of enterprises. Therefore, the effect quantity obtained by meta-analysis could not be limited to all the objects in a certain study. On the other hand, the analysis of regulatory variables was involved. Therefore, theoretically, the random effect model is projected to be more suitable for this meta-analysis than the fixed effect model. Further, the heterogeneity test would be used to verify the choice of the model from the practical level.
When published research cannot represent the overall objective in related fields, it would be considered that there has been a “publication bias” [
110]; when this occurs, it will lead to the final effect value being higher than the true value. In this paper, firstly, the funnel chart was used to analyze “publication bias” to intuitively determine whether there was a publication bias in the samples selected in this paper. Afterward, “failure safety factor N” and Egger’s test method were used to evaluate the publication bias of all samples in meta-analysis. “Failure safety factor N” posits that it is necessary to add some unpublished studies so that the final correlation coefficient or its significant level is lower than a specific critical value, that was, 5k + 10 (k is the sample size of meta-analysis); otherwise, there would be publication bias [
111].
5. Discussion and Conclusions
5.1. Discussion
Green innovation is a kind of innovation with high technical cost and complexity [
119], which needs more green knowledge than traditional innovations. In implementing green innovation into practice, firms should learn to attain green resources and improve green innovation [
120]. Some quantitative empirical studies show that involving a large number of external knowledge sources in innovation is a promising alternative for large companies [
121]; it plays an important role in enterprises’ green innovation.
(1) The influence of green resource search on green innovation. This study reveals that external green resource search breadth and depth are positively related to green innovation propensity and process, which agrees with the results of prior research which was in the context of the Spanish food and beverage manufacturing industry over the period 2008–2014 [
122]; but there are some other research findings with similar research objects that demonstrate an inverted U-shaped relationship between the breadth of firm’s knowledge network and its eco-innovations in a random sample of 279 food firms in Spain [
123]. Notwithstanding, most studies have shown that green resource search breadth and depth positively affect enterprise innovation, and there is no inflection point of the opposite effect [
124]. This indicates that enterprises can acquire more quantity, diversity, and heterogeneity of green resources to enrich the foundation of green innovation enterprises by broadening external channels. Simultaneously, strengthening the contact and cooperation with the outside, digging deep into knowledge and information, finding valuable resources, and reducing resource acquisition costs can help enterprises quickly grasp the market development direction and customers’ green demand. These would enable enterprises to implement effective green innovation.
(2) The influence of green resource absorption and integration on green innovation demonstrates that green resource absorption and integration had a more significant impact on green enterprise innovation. Thus, the search for green resources is an essential foundation for the green innovation of enterprises [
125], and the failure of individuals and industries to absorb, integrate and make effective use will create resource redundancy. Thus, green resource absorption and integration leads to much green knowledge and helps reduce market risk in business fluctuations [
84]. Green absorptive and integrative capacity can positively affect green innovation, consistent with prior research [
126]. The statistical test results of this study demonstrated a significant positive correlation between green resource absorption and integration and green innovation. The findings indicate that increasing green resources’ absorption and integration capability can help enterprises internalize the green resources through the new knowledge, information, and technologies obtained from the external search. Thus, providing a stable material basis for enterprises’ green innovation. Concurrently, the findings further denote that enhancing green resources’ absorption and integration ability can generate positive two-way impetus with enterprise employees’ continuous learning and progress, provide continuous intellectual support for green enterprise innovation, and then lay a solid foundation for green enterprise innovation.
(3) The adjustment function of the different definitions of green innovation revealed four crucial findings:
Firstly, from the perspective of innovation content: (1) The search breadth of green resources had the strongest correlation with comprehensive green innovation, which may be due to the fact that when comprehensive green innovation is defined, the respondents will be affected by different items, and the obtained green innovation value will be relatively high. Secondly, there is a significant correlation between the search breadth of green resources and green product search, which showed that diversified external resources could help enterprises be more innovative in product design and material selection stages. (2) The search depth of green resources and the green absorption and integration had the strongest correlation with green marketing innovation, which indicates that enterprises can grasp consumers’ preferences by strengthening cooperation and communication with the outside partners. Moreover, by absorbing and integrating green resources, enterprises can promote innovation achievements and increase the profits of new products. Conversely, in terms of green product and process innovation, green innovation would have been influenced by enterprises’ capability and social responsibility, so the influence effect was relatively small.
Secondly, from the perspective of innovation intensity: (1) The search breadth of green resource had a higher correlation with exploratory analysis of green innovation, which indicated that the search breadth of external resources could help enterprises to explore new resources, provide direction for continuously exploring current innovational ideas and help in the exploratory analysis of innovation. (2) The green resources absorption and integration had a stronger correlation with progressive green innovation, but it was not significant, which indicated that enterprises constantly make use of the obtained external resources to carry out progressive innovation and provide them with sufficient resources. However, enterprises’ absorption and integration ability was relatively weak, and the positive impact on green innovation is not significant enough.
(4) Further, the regulatory role of the research object based on the moderating effect of the meta-analysis revealed that knowledge learning had a positive impact on different types of enterprises’ green innovation, and there was a significant correlation with resource-based enterprises’ green innovation. The possible reason was that the economic activities of resource-based enterprises were more dependent on natural resources. With the development of the economy and society, natural resources are becoming scarce. It had become an essential concern of resource-based enterprises to maximize limited natural resources by learning external knowledge and understanding new technologies.
5.2. Conclusions
This study employed 32 pieces of empirical literature to investigate the relationship between knowledge learning and green enterprise innovation in China through meta-analysis methodology. This study initially analyzed the main effects of green search breadth, green search depth, and green resources absorption and integration on green innovation. Afterward, the moderating effects of green innovation definition and research objects on the three-pair relationship were explored. The results of this current study revealed that knowledge learning could significantly and positively influence green enterprise, and green innovation definitions and the sample research objects had moderating effects.
5.3. Countermeasures and Suggestions
(1) Enterprises must pay attention to the accumulating external resources and promote breaking green innovation. Thus, with the development of social connections and global integration, the external network members of enterprises are gradually extending to government departments, supply chain partners, management consulting institutions, universities and research institutes, competitors, customers, and other enterprises. It may also include relevant organizations and individuals in the international community. By enhancing the capability of enterprises to search, and absorb more green resources, it would be cumbersome for the competitors to imitate. Hence, it is helpful to provide human, material, and financial support for enterprises’ green innovation. Additionally, enterprises can collaborate with other external organizations by participating in various related conferences held at home and abroad. The collaborations will help demonstrate enterprises’ healthy competitiveness to attract other participants to expand the external network to members. On the other hand, with the advent of the era of big data, enterprises can learn and master big data analysis methods to actively understand the dynamic development of the market and dig out hidden information to predict the future trend of the market.
(2) Improving the capability of resource absorption and integration and promoting progressive innovation. In the era of massive data, identifying how to quickly perceive, acquire and absorb useful information and integrate, utilize and transform the searched external resources into the core competitiveness of enterprises has become the top priority of green innovation. This study projects that enterprises can carry out brainstorming activities such as holding meetings or making suggestions to assist managers with strategic ideas to motivate employees to make meaningful suggestions for the company’s development. Simultaneously, enterprises should pay much attention to employee training, set up special funds, and adopt the path of opening up or broadening employees’ horizons. This approach will stimulate employees’ new ideas and creativity, continuously improve the learning potential of managers and employees, strengthen the cultivation of their comprehensive skills, and provide sufficient workforce support for enterprises to absorb and integrate external resources.
(3) Resource-based enterprises need to pay more attention to knowledge learning. Limited by the constrained resources, resource-based enterprises are even more necessary to acquire and absorb external information to reduce over-dependence on specific resources and produce a new path for energy development. A green innovation alliance for resource-based enterprises led by leading industries, universities, research institutes, and other parties should be established. Further, the industries could form a high-quality green innovation working mechanism and provide a cooperation platform for SMEs to obtain green resources. Simultaneously, the contract spirit should be cultivated and strengthened to avoid free-riding behavior.
(4) Enterprises should engage in effective collaborations and focus on the whole process of green innovation. Thus, the enterprises should strengthen the series of processes involved in the enterprises’ green innovation cycle, including product design and marketing, due to the inherent economic impact of new products. Enterprises should apply the acquired and absorbed technologies to actual production and actively improve processes and technologies through collaborative efforts. Additionally, industries should effectively save energy, reduce emissions, produce easily degradable and recyclable products, and create avenues for recycling available waste. This study suggests that enterprises must go beyond the traditional notion of taking economic profit as the sole goal and support environmental protection. Therefore, managers of the enterprises should strengthen the cultivation of environmental education awareness to promote green innovation culture.
5.4. Limitations and Future Study
In this current study, the results indicated a positive relationship between knowledge learning and green enterprise innovation. The green resources absorption and integration had the most significant influence on green enterprise innovation. In contrast, different definitions of green innovation and different research objects had moderating effects on the relationship between knowledge learning and green innovation. However, the constraint of this paper was that the meta-analysis conducted is only based on Chinese literature collected from the CNKI database. The study did not consider foreign literature for the analysis; therefore, the authors of this study project to conduct another relevant study that combines domestic and foreign countries’ research data sources to obtain dynamic results. The authors aim to increase the sample size and the scope of the research in future research.