The Impact of Governmental Regulations and Environmental Activities on Innovation Efficiency

Abstract

Innovation efficiency plays a key role in ensuring that sustainability efforts are implemented effectively and yield maximum benefits. This study emphasizes the impact of government regulations and environmental resource-saving approaches on innovation efficiency. Within this study, environmental resource-saving approaches include activities against air pollution, water pollution, energy consumption, waste, and hazardous substances. By analyzing data from 1196 German companies statistically, this study examines the importance of governmental and legal regulations as well as engagement in environmental activities for green innovation. The findings reveal that companies prioritizing regulations and environmental practices achieve higher innovation output from the same innovation input than other firms, leading to improved innovation efficiency. In a developed country, companies focusing on sustainability aspects exhibit a higher level of innovation efficiency. For them, embracing regulations and environmental practices are firm resources that raise the possibility of competitive advantage. Companies embracing these aspects hence contribute to both their own success and the well-being of the environment.

1. Introduction

Climate change is an ongoing and pressing global concern. As the effects of climate change continue to manifest, and as recognized by scientific consensus, it is increasingly imperative to shift towards sustainable practices and take action to mitigate its impacts [1]. One of the essential tasks at hand is to implement strategies aimed at reducing resource consumption and adopting environmentally-friendly practices [2]. As sustainability increasingly becomes a key part of corporate strategy, research in this area has also grown significantly in recent years [3]. Addressing environmental and sustainability challenges relies heavily on innovation as a crucial factor [4]. Sustainable innovation has been identified as one of the most important strategic tools for corporate management [5]. External factors, such as societal pressures for greater sustainability from stakeholders and consumers, are also driving the adoption of sustainable innovation practices [6].

Innovation is essential for survival within a competitive marketplace, as sustainable product and process innovations can help organizations increase their efficiency, performance, and competitiveness and, therefore, their competitive advantage [5,7]. As efficiency is necessary for companies to survive and compete successfully [8], being efficient in the implementation of sustainable innovation is crucial not only for addressing environmental and sustainability challenges but also for the success of the company itself. Therefore, it is important to consider how companies approach key areas such as energy consumption, material consumption, water consumption, air pollution, highly hazardous substances, and waste when it comes to innovation, and how these factors impact their efficiency. On the other hand, government pressure is one of the most significant external stakeholder pressures in the context of environmental practices [9]. Different governments have various means of intervention [5], such as implementing environmentally friendly policies through subsidies, laws, and regulations, which, in turn, impact the competitive conditions for businesses. The creation of such regulatory frameworks plays a role in the advancement of green innovation [10]. Li et al. [11] highlight that governmental influence, through strict regulations and the obligation to disclose sustainability-related information, has the potential to significantly enhance firms’ green innovation efficiency. The structure of national policies, international agreements, and their monitoring is crucial for sustainable development, enhancing green innovation efficiency and corporate sustainability performance [11]. It remains uncertain whether it is advantageous or not for companies to merely meet the statutory minimum requirements for sustainability or if they should fulfill the existing legal obligations in good time and continue beyond them. This raises the question of whether companies should consider legal requirements as significant and consider surpassing them by setting their own higher standards in the future, thereby exceeding the expectations and benchmarks set by stakeholders.

The impact of sustainability on innovation efficiency is complex and multifaceted, as sustainability encompasses many dimensions and cannot be measured by a single factor alone. Given the dual pressures of economic advancement and environmental conservation, the emphasis on green innovation efficiency has intensified worldwide [12]. While some studies suggest that companies that engage in environmental and resource-saving activities can enhance their innovation efficiency, others argue that these activities can hinder it. For example, Shin et al. [1] discovered that the goal of environmental improvement, such as reducing material or energy use, has a negative impact on innovation efficiency, whereas safety improvement, focusing on health and safety in working conditions, positively influences innovation efficiency. However, they did not find any significant effect of ‘material and energy reduction’ as an objective of innovation on innovation efficiency. Similarly, Shin et al. [13] also reported a negative effect of environmental improvement on innovation efficiency in Korea. On the other hand, Bresciani et al. [14] found a positive relationship between innovation, environmental sustainability, and economic development. Moreover, Kuzma et al. [15] demonstrated a strong and positive correlation between innovation and sustainability performance, especially between economic innovation and sustainability performance and between environmental innovation and sustainability performance. Ouyang et al. [16] investigated the impact of coordinated local and neighboring environmental regulations on green innovation. Their study provided evidence on why collaborative environmental governance promotes green innovation, offering theoretical insights for governments to develop environmental policies and establish governance systems. However, it is unclear to what degree companies experience a measurable influence on their innovation efficiency of active engagement in government and legal regulations and a focus on environmental and resource-saving activities.

It is important to note that companies in developing and developed countries may face different environmental concerns and responsibilities. This distinction is highlighted in various studies, such as those by Wang et al. [17] and Kvasničková Stanislavská et al. [18]. This study aims to explore whether companies in developed countries that strongly commit to both complying with government and legal regulations and actively engage in environmental and resource-saving activities achieve higher levels of innovation and efficiency compared to companies that are less concerned about these aspects. The research investigates 4528 companies in Germany (after data cleansing and matching 1196 companies) and addresses two core hypotheses. First, it investigates whether these companies regard legal regulations and public financial support or restrictions as key drivers for developing innovations and whether they actively leverage these opportunities to enhance innovation efficiency. Second, the study examines whether adopting an environmental resource-saving approach has a positive impact on companies’ innovation efficiency. The findings indicate that both, adhering to regulations as well as actively pushing environmental initiatives correlate positively with innovation efficiency. The study bridges two literature fields, the literature of innovation efficiency and (green) innovation: it contributes to a better understanding of how the integration of environmental initiatives and regulations affects the development of competitive advantage from a resource-based view, especially focusing on the improvements when it comes to the innovation process Thereby the study addresses the view from companies within a developed country, which in the literature field of innovation efficiency is only rarely the case.

2. Theoretical Foundation

2.1. Innovation

The term innovation is comprehensively defined to encompass a diverse wide range of types and outcomes, wherein pre-existing entities are supplanted by novel and distinct constructs. The cornerstone of innovation research was laid by Schumpeter [19], who defined innovation types such as product innovation, process innovation, creation of new raw materials, establishment of new markets, and organizational change. Between these broad categories, there are a variety of forms of innovation [20,21,22,23].

The present work focuses mostly on innovation as well as on terms like eco-innovation, green innovation, environmental innovation, and sustainable innovation. In firms, emphasis is not solely on innovation novelty but on the entire life cycle, encompassing development, social elements, material sourcing, and environmental and societal impacts [20]. The transition to a sustainable economic system is a challenge posed by the climate crisis [24]. In their literature review, Schiederig et al. [25] (p. 180) noted that the “different notions of green, eco/ecological and environmental innovation are used largely synonymously”. In contrast, sustainable innovation includes the social aspect and is thus a broader concept than the those previously mentioned [25]. All four terms (eco, green, environmental, and sustainable innovation) describe a process “that contributes to the creation of new production and technologies with the aim of reducing environmental risks, like pollution and negative consequences of resource exploitation” [5] (p. 2), with the goal of “balance[ing] social, environmental and economic considerations” [20] (p. 25). In the present work, we want to encompass all relevant concepts and ideas, rather than focus on the subtle differences among them. Research on green innovation is continuously increasing, as such innovation has become a critical strategic tool for achieving both environmental and economic success [5]. However, the advancements in sustainable technology are difficult to implement, and many companies are struggling to achieve their sustainability goals [26]. Furthermore, the rewards from sustainable innovation might not manifest as immediate financial gains. Instead, they often yield intangible benefits tied to long-term objectives, such as reputation enhancement [27]. Over time, this improved reputation, coupled with reduced resource consumption, can lead to substantial savings in energy and materials, making the innovation process not only efficient but also cost-effective.

2.2. Innovation Efficiency

Innovation efficiency, as defined by Cruz-Cázares et al. [28], represents the firm’s capability to optimize the conversion of innovation inputs into outputs. This concept hinges on a company’s capacity to produce more innovation outputs than other companies from the same number of inputs or require fewer inputs to generate the same outputs, thereby relating it closely to productivity [29]. Wang et al. [30] centered their examination on new energy companies, highlighting the necessity for these organizations to bolster their innovation effectiveness (i.e., innovation efficiency). This is crucial for ensuring these companies stay competitive in the domains of emerging technologies and products. On a similar note, Bae and Chang [31] used several metrics, including efficiency and effectiveness, to evaluate an organization’s innovation outcomes. They considered elements such as cost and time, indicators that mirror a firm’s performance. It is crucial to consider more than just economic factors when measuring innovation efficiency, particularly regarding the environment. Schilirò’s [4] research highlighted that what matters most in the long term regarding efficiency is how the innovations contribute to the quality of life for living beings and the environment.

The measurement of innovation efficiency involves a complex process, as it requires the handling of multiple input and output indicators simultaneously. To accommodate this complexity, Data Envelopment Analysis (DEA) is utilized, as it measures the relative efficiency among decision-making units without the need for prior knowledge of the input–output production function or statistical assumptions about data distribution [32]. Given that the production function of the innovation process within a company is usually unknown, DEA is advantageous because it does not require specifying a functional form for the production process [32]. In this context, DEA has been employed widely to assess innovation efficiency, offering a comprehensive, non-parametric method to handle multiple variables and accommodate the iterative process of innovation [28,29,30,32,33,34]. This calculation methodology aligns with the resource-based view (RBV), where the efficient transformation of resources (e.g., R&D) into desirable outputs (e.g., innovations) is central. Firms that are more efficient in innovation usually outperform others, as demonstrated through higher market valuations and superior operating performance.

Green innovations are distinct from regular innovations because they are more intricate and require the consideration of additional sustainability factors that, at first glance, appear unrelated to functionality [25]. Gao et al. [35] have identified and grouped factors that influence green innovation. External factors include “government, consumers, related industries, and international trade relations”, while internal factors include “corporate goals, corporate culture, green resource inputs, entrepreneurial spirits, and firm size”.

Despite all the seemingly positive outcomes for firms adopting green innovation, Zhang et al. [36] point out that there is still a risk that firms will be inefficient and lose productivity. Innovations in general are often associated with high costs and risks, which is why the relationship between the input and output of an innovation should be made as efficient as possible. For this reason, it becomes intriguing to understand how concentrating on (1) legal requirements and external factors and (2) aspects of environmental sustainability affects innovation efficiency (i.e., positively, negatively, or not at all) in addition to increasing long-term performance.

2.3. Resource-Based View, Innovation Efficiency and Green Innovation

Innovation efficiency, which focuses on optimizing the transformation of innovation inputs into outputs, aligns closely with the resource-based view (RBV). The RBV belongs to the most popular contemporary management [37] and provides an “inside-out” perspective on achieving sustainable competitive advantage, emphasizing that a company’s success depends on its ability to effectively utilize and leverage its available resources [38,39]. Resources are defined as the assets, capabilities, organizational processes, firm attributes, information, and knowledge that a company controls and develops to implement effective strategies and enhance efficiency and effectiveness [40].

From a sustainability perspective, RBV may help to identify firm resources to incorporate green innovation into firm practices, processes, and activities [41] as well as the possibility of gaining a competitive advantage as a firm [42,43,44]. Khanra et al. [44] identified various thematic areas of green innovation as a firm resource: green supply chain management, green product design, corporate environmental responsibilities, and social sustainability. Connecting it back to innovation efficiency: to turn green activities into a competitive advantage, operational effectiveness and efficiency become important [44].

Although innovation efficiency and green innovation are increasingly studied and interconnected in China [45,46,47], there remains a notable lack of research bridging these two fields in developed countries. However, there are fundamental differences between developed and developing economies regarding their focus areas and priorities: while developing economies often face the challenge of insufficient resources to meet essential needs [17] and report issues related to “sustainable production” and “supply chain emissions” [18], developed economies have mature production technologies in place which might complicate environmental changes [17]. This study hence addresses the gap between studies on innovation efficiency and green innovation from the perspective of a developed country and further contributes to the understanding of green innovation, especially the proactive push of regulations and environmental activities, as a firm resource to push competitive advantage.

3. Literature Review and Hypothesis Development

Previous research has shown conflicting results regarding the impact of sustainability practices on innovation efficiency, with some studies reporting negative effects and others identifying positive relationships. Moreover, most studies in this context have primarily focused on innovation itself, rather than on it. The studies that have focused on innovation efficiency have been mostly driven in China, a developing country.

3.1. Legal Requirements and External Factors

Some companies adhere only to basic environmental standards, while others are steadfast in their commitment to sustainable progress, exceeding all required standards and regulatory requirements. In the literature, there has been an increase in research exploring the relationship between governmental regulations and environmental practices, and it has been shown that governmental pressure is one of the most important external factors in terms of influencing sustainability within companies [5,9,27]. For example, legal changes can prohibit the production of certain products or the use of specific materials, thus greatly restricting a company’s activities. In these cases, it is almost obligatory for companies to take the initiative in terms of green innovation [27]. In China, for instance, the government has set strict energy and emissions standards for regional evaluations and introduced fiscal incentives, along with pollution-related charges [48]. The influence of environmental policy is considered a significant driving force for pursuing sustainable development goals and associated green innovation, as e.g., non-compliance might come with significant penalties, which also pose a risk to the survival of companies [10]. Especially crucial is compliance with environmental protection laws and regulations, as well as standards for sustainable innovation [49]. Legislative pressure aims to reduce environmental impact, decrease material and energy costs, and enhance health and safety

The study of Zhang et al. [36] indicates that legislative support and regulation enforcement positively influence green innovation as well as firm performance [36]. Also, Bai et al. [22] suggest that legislative mandates can serve as catalysts to push innovation and sustainability. However, this does not necessarily mean that standards and regulations push innovation efficiency: Zeng et al. [50] identify different views of the impacts of environmental regulations. One is, that it negatively influences innovation efficiency because of the potentially lower return of green innovations in terms of productivity. On the other side, the environmental regulations may in turn promote green innovation—and lead to a beneficial effect for firms that generate and exploit the newly generated knowledge. Regulations can provide companies with valuable insights and guidance on the directions to pursue or avoid when engaging in innovation [27]. Also, firms with closer relationships with governments seem to have an advantage when it comes to leveraging green innovation [36] which speaks in favor of proactively dealing with regulations to improve innovation efficiency. Fostering collaboration with governmental bodies, and emphasizing joint endeavors to bolster green production incentives across sectors might be helpful [50]. This can be realized by refining overarching industrial policies, streamlining standardization systems, formulating precise environmental protection measures, and promoting sector-specific green certification standards and institutions [51].

Based on the above reasoning, in the present study, we investigate and differentiate between companies within a developed country that merely meet the minimum requirements for environmental protection and those that actively drive, develop, and implement green innovations and are considered first movers in the implementation of legal aspects. We hypothesize as follows:

Hypothesis 1. 

The innovation efficiency is significantly higher in companies within developed countries that emphasize and actively engage in adhering to governmental and legal regulations compared to those that allocate less effort to these activities.

3.2. Environmental Sustainability Aspects

According to Ketata et al. [27], sustainable products are those that require minimal resources or energy during production and promote better health and safety standards for both employees and customers. In technologically advanced industrialized countries, there’s an increasing demand for sustainably produced products that enhance energy efficiency and reduce a company’s carbon footprint [52]. Green innovations can optimize material use and enhance recycling processes, promoting the substitution of eco-detrimental materials with environmentally benign alternatives. Such transitions address pivotal environmental sustainability aspects, influencing the direction and relevance of new innovations [9,49,53]. Reducing energy consumption involves using less energy to carry out a particular task or activity. This can be achieved through the technological redesign of products [23], the use of innovative and more efficient technologies, energy-efficient practices, or changes in behavior that decrease the amount of energy needed to complete a task. The potential to reduce energy consumption spans the entire product lifecycle [54] and often results in the technological redesign of products [23] or the optimization of industrial processes.

Reducing material consumption entails adopting practices such as minimizing packaging, optimizing the use of materials in general, and utilizing more durable and long-lasting materials in manufacturing and construction. During the product design and development phase, considerations can be made to ensure easy recyclability, disassembly into original parts, or reusability [55]. Companies are increasingly adopting sustainable business models [26] to reduce material usage through efficient processes [56].

Air pollution is another significant environmental issue, particularly in rapidly growing and economically developing nations such as China and India [57]. Brunekreef and Holgate [58] already highlighted the significant health impacts of air pollution in 2002, estimating that tens of thousands of deaths annually in Europe alone can be attributed to pollution-related causes. These estimates do not account for developing countries, where the burden is likely even greater. The authors emphasize that air pollution contributes to both short-term and long-term health consequences, underscoring the critical need for sustained regulatory efforts to reduce air pollution levels. Furthermore, lowering CO₂ emissions is crucial for minimizing the negative impacts of climate change, as CO₂ is a greenhouse gas that contributes to rising temperatures on Earth. An essential approach to decrease CO₂ emissions involves replacing environmentally harmful substances with cleaner or less damaging substitutes during the manufacturing process [59].

Similarly, reducing water consumption involves efficient practices such as optimizing industrial processes, using water-efficient fixtures in buildings, and adopting water recycling innovations. Agronomy, notably studied by García-Granero et al. [59], is crucial due to groundwater and soil pollution and excessive water use.

Another way of mitigating climate change is by prohibiting highly hazardous substances [9]. Replacing hazardous materials involves finding and using safer substitutes in place of dangerous substances. Many materials used in industry, manufacturing, and consumer goods can be hazardous if mishandled, with potential negative impacts on human health and the environment.

Conservation of resources also involves the recycling of waste and used materials. Recycling of waste is explicitly mentioned in various definitions of green innovation [7,25], which highlights the “recyclability of products, the reduction of raw materials, the selection of environmentally healthier raw materials, and the removal of hazardous substances” [60] (p. 698). The recycling of waste and materials involves gathering, organizing, and treating garbage and discarded materials to transform them into fresh items or raw materials that can be utilized again. Firms may also consider ways to “repair, reuse, disassemble, remanufacture, and/or recycle a product” [55] (p. 67). Geissdoerfer et al. [26] (p. 406) also describe an efficient recycling process as “closing the resource loop”.

The overall aim for a company should be to mitigate imminent climate change by integrating practices such as reducing energy consumption; minimizing material use; mitigating air, water, and soil pollution; decreasing noise levels; incorporating recyclable materials; avoiding environmentally harmful substances; and other related efforts [9,49,53,61]. Therefore, it is crucial to strategize and execute the development or implementation of such innovations in a manner that optimizes efficiency.

Hypothesis 2. 

The innovation efficiency is significantly higher in companies of developed countries that emphasize and actively engage in environmental and resource activities compared to those that allocate less effort to these activities.

4. Material and Method

4.1. Sample

This study focuses on the intersection of sustainability and innovation efficiency within a firm’s operational context. The data used in this research are based on data from the Center for European Economic Research. Since 1993, ZEW has been collecting information on the innovation behavior of the German economy on an annual basis. Companies with five or more employees from the sectors of mining, manufacturing, energy, construction, business-related services, and distributive services are surveyed. The resulting Mannheim Innovation Panel (MIP) provides information on the introduction of new products, services, and processes in companies; the applications for innovations; and the success that companies achieve through innovations [62]. It offers supplementary insights into the relevance of specific sustainability initiatives for companies, as well as information regarding compliance with regulatory requirements and resource management activities. The annual survey is designed as a panel survey, which is why the same companies are repeatedly surveyed on the same topics at different points in time [63]. The questions for this analysis are from the 2009 (dataset group 1) and 2015 (dataset group 2) surveys.

In this study, the selected approach for assessing innovation efficiency is Data Envelopment Analysis (DEA), a non-parametric mathematical programming method [64]. The reason for selecting DEA is rooted in the complexity of innovation as a process, involving multiple iterations and interactions of various inputs and outputs. DEA is well-suited to deal with such complexity, as it is capable of handling multiple input and output indicators simultaneously, without requiring any prior knowledge of the input–output production function or any statistical assumptions about data distribution [32]. According to Cruz-Cázares et al. [28], DEA overcomes the difficulties associated with the simultaneous evaluation of firms based on multiple dimensions. It estimates the best-practice frontier without a specific functional form assumption, calculating the maximal performance measurement of each decision-making unit (which in this study refers to firms) in comparison to all other decision-making units within the sample.

In this context, we have chosen to utilize the output-oriented, variable returns-to-scale (VRS) model provided by Banker et al. [65]. This model aligns with the definition of technological innovation efficiency as the relative capability of a firm to maximize innovation outputs given certain innovation inputs [28]. An output-oriented model is appropriate as it evaluates the level of inefficiency based on the maximally producible output at the corresponding input level [32]. The innovation efficiencies calculated for the companies in this study range from 0 (not efficient) to 1 (highly efficient), as described in Section 4.2.

Innovation efficiency establishes a relationship between a firm’s inputs and outputs, requiring a meticulous evaluation of these input and output variables. The input and output variables used in this study have been found to be strongly representative of the concept of innovation efficiency in previous literature [31,33,66,67], providing a robust framework for analyzing innovation efficiency. However, it is important to note that selecting the right indicators for innovation efficiency poses a challenge, as there are numerous indicators available, and identifying the relevant ones can be difficult [68]. Furthermore, as the number of inputs and outputs increases, the discriminatory power of DEA decreases due to the increased Euclidean distance between observations, resulting in many observations lying on the frontier [69]. Therefore, to ensure meaningful and robust results, it is necessary to limit the number of variables used in the model [70].

For the purpose of this study, input variables include Innovation Expenditures and Highly-Skilled Staff. Innovation expenditures encompass all labor and capital costs tied to a range of activities. These include internal and external research and development, acquisition of necessary machinery and software for innovation projects, procurement of external intellectual property related to such projects, and product design and construction for the production and distribution of innovations [31,71]. These expenditures also cover ongoing training related to innovation projects and the costs of introducing these innovations to the market through marketing campaigns and market research [31,71]. The second input variable focuses on the influence of highly skilled staff on the generation of ideas [28]. The variable encompasses all employees who have a university degree or other higher education qualification. The output variable in this analysis is Sales from New or Clearly Improved Products. This measure aims to quantify the successful implementation of innovative activities that translate into tangible products that in turn generate revenue for the company. Both innovation inputs, Highly-Skilled-Staff and Innovation Expenditures, have already been used as input variables in different publications on innovation efficiency [1,13,31,66]. The economic outcome of the innovation process is generally the sales from new products, which has been used in a variety of innovation efficiency studies using DEA [1,13,33,72].

4.2. Data Variables and Statistics

The dataset distinguishes between small companies (1–50 employees), medium-sized companies (51–250), and large companies (>251). As shown in

Table 1. Company size.

Company Size	Group 1	Percentage	Group 2	Percentage
<50 people	705	59%	369	55%
50–250 people	358	30%	187	28%
>250 people	133	11%	116	17%
Total	1196	100.00%	672	100%

, there is an acceptable distribution of different company sizes: 55–59% micro/small companies, 28–30% medium-sized companies, and 11–17% large companies.

In total, 21 distinct industries were represented, with no single sector unduly dominating the dataset. Although the ‘Electrical industry’ and ‘Mechanical engineering’ sectors each constitute more than 10% of the dataset, the distribution across industries remains equitable and robust.

To address the research objectives, two sets of items were selected, each tailored to investigate one of the hypotheses. The descriptions of these items, which are analyzed in the following sections, are detailed in

Table 2. Description of the items.

GOV	Description
GOVA	Compliance with existing legal requirements and regulations
GOVB	Existing environmental taxes or levies
GOVC	Expectation of future legal requirements, regulations and environmental taxes
GOVD	Public financial support for environmental innovations
ENV	Description
ENVA	Reduction of material consumption
ENVB	Reduction of energy consumption
ENVC	Reduction of CO2 emissions
ENVD	Reduction of other air emissions
ENVE	Reduction of water pollution
ENVF	Reduction of soil pollution
ENVG	Reduction of noise pollution
ENVH	Substitution of hazardous materials
ENVI	Improvement of recycling

.

The survey participants are requested to evaluate the significance of all items on a three- or four-point scale ranging from “not relevant” to “high”, providing a structured approach to quantifying perceptions and actions related to the hypotheses. Following data collection, the data were rigorously sorted and cleaned. Statistical analyses were then conducted using IBM SPSS Statistics 30 and R version 4.3.2, allowing for comprehensive evaluation and graphical representation of the results.

5. Results

5.1. Exploratory Factor Analysis

To validate the preselected item groups shown in

Table 3. Exploratory factor analysis.

Factors	Items	Factor Loadings	Factors	Items	Factor Loadings
GOV1	GOV1A	0.51	GOV2	GOV2A	0.80
	GOV1B	0.65		GOV2B	0.71
	GOV1C	0.87		GOV2C	0.85
	GOV1D	0.79		GOV2D	0.55
ENV1	ENV1A	0.65	ENV2	ENV2A	0.61
	ENV1B	0.74		ENV2B	0.65
	ENV1C	0.75		ENV2C	0.70
	ENV1D	0.71		ENV2D	0.69
	ENV1E	0.70		ENV2E	0.66
	ENV1F	0.70		ENV2F	0.55
	ENV1G	0.66		ENV2G	0.35
	ENV1H	0.54		ENV2H	0.48
	ENV1I	0.61		ENV2I	0.51

, a factor analysis was performed on the items that were initially grouped into a single category in the MIP. The exploratory factor analysis confirms the validity of the construct defined in Table 3, indicating that it accurately represents the underlying factors. The datasets used for the factor analysis are considered appropriate because they meet the requirements of the Kaiser–Meyer–Olkin measure (KMO) and the assumption of sphericity, as determined by Bartlett’s test. The KMO index is calculated to be 0.84, which is reasonably good and well above the satisfactory threshold of 0.5–0.6. The chi-square value and degrees of freedom for all measured constructs are positive, suggesting that the data fit the statistical model. The findings of the analysis reveal that the correlation coefficients present in the correlation matrix exhibit statistically significant deviations from zero. Bartlett’s Test of Sphericity also yields a significant result (p < 0.001, df = 325), further supporting the suitability of the data for factor analysis. However, item ENV2G of factor ENV2 was excluded from further calculations due to its low factor loading. The factors GOV1 and GOV2 represent a common theme but cannot be directly compared because there are differences in the item descriptions between the two years. In contrast, the item descriptions for factors ENV1 and ENV2 are almost identical, allowing for meaningful comparisons between them.

The four factors can be divided into two themes, as shown in

Table 4. Factors for the calculation.

	Reliability Statistics
Factors	Cronbach’s Alpha	Number of Items
GOV1 (Government and legal regulations 2009)	0.926	4
ENV1 (Environmental and resource activities 2009)	0.905	9
GOV2 (Government and legal regulations 2015)	0.872	4
ENV2 (Environmental and resource activities 2015)	0.868	8

. Firstly, there are governmental regulations, requirements, taxes, norms, and standards, which are collectively referred to as the government and legal regulations factors (GOV1 and GOV2). Secondly, there are factors related to the sustainable management of resources such as materials, energy, air, water, and soil, which are combined to form the environmental and resource activities factors (ENV1 and ENV2). The Cronbach’s Alpha values range from 0.868 to 0.926 and, therefore, are all more than 0.6, indicating a reliable set of measures of the underlying factors.

5.2. Correlation Analysis

Spearman’s rank correlation coefficient (

Table 5. Correlation coefficients.

Descriptive Statistics
Variable	CA	Mean	SD	GOV1	ENV1	GOV2	ENV2	IE1	IE2
GOV1	0.84	1.46	0.85	---
ENV1	0.89	0.67	0.68	0.49 **	---
GOV2	0.85	0.85	0.88	0.26 **	0.43 **	---
ENV2	0.84	0.49	0.45	0.28 **	0.53 **	0.51 **	---
IE1	0.91	0.05	0.11	0.18 *	0.33 **	0.26 **	0.10	---
IE2	0.89	0.07	0.13	0.16	0.37 **	0.22 **	0.22 **	0.63 **	---

* indicates p < 0.05. ** indicates p < 0.01.

) was employed in this study due to the non-normal distribution of the dataset. Except for IE1 and ENV2, as well as IE2 and GOV1, all other correlations within the dataset demonstrate statistical significance. It is also noteworthy to highlight that the impact size tends to be less pronounced in larger samples than in smaller ones.

5.3. Innovation Efficiency Values

The innovation efficiency values, IE1 and IE2, range from zero to one, where a value closer to one indicates higher efficiency. The arithmetic mean indicates that, on average, companies achieve an innovation efficiency of 0.053 (IE1) and 0.072 (IE2). The standard deviations (SD) are 0.13 (IE1) and 0.12 (IE2). One of the characteristics of DEA is that it can identify companies that perform exceptionally well compared to others, resulting in outliers with significantly higher efficiency scores. These outliers are distinctly separated from the majority of companies, which tend to have much lower innovation efficiency values. This explains why the average innovation efficiency score for most companies is very low [32]. This explains why most companies have a very low average innovation efficiency score.

5.4. Pairwise Comparision

To identify significant differences between innovation-efficient and -inefficient firms, the datasets of each variable were divided into quartiles, forming four groups (‘low’, ‘middle1’, ‘middle2′, and ‘high’). Of relevance to the study are the companies in the first quartile and the fourth quartile. The first quartile embodies the lowest 25% of the distribution. This segment is termed the ‘low’ group as it comprises firms that assign minimal importance to the respective factor. On the other hand, the companies in the fourth quartile, making up the top 25% (>75%) of the distribution, place high importance on the respective factor. Therefore, this quartile is termed the ‘high’ group. The two groups, middle1 and middle2, are combined into a single group, named “middle”, as they are not particularly relevant to our analysis and will not be subjected to further scrutiny in this research. We focused our analysis on the low and high groups to clearly identify and contrast the key differences between firms with low and high values for the analyzed factors related to governance and environmental engagement, as these extremes provide the most significant insights for our research objectives. The calculation is performed using non-parametric tests using ranks, which helps to further reduce the effect of outliers [73]. The Kruskal–Wallis test and Wilcoxon rank-sum test were employed to conduct pairwise comparisons between the groups, aiming to identify significant differences between individual pairings. All group comparisons between a factor and innovation efficiency are significantly positive. In the pairwise comparison between the ‘low’ and ‘high’ groups, a chi-square value of 65.839 (df = 2) and a p-value of 0.000 were observed for the comparison between ENV1 and IE1. Similar values were also obtained for the comparisons between GOV1 and IE1 (chi-square = 21.297; df = 2; p-value 0.000), ENV2 and IE2 (chi-square = 19.419; df = 2; p-value 0.000), and GOV2 and IE2 (chi-square = 24.271; df = 2; p-value 0.000).

The graphical solution in the form of boxplots provides a clear overview of the distribution and aids in the interpretation of the results. To achieve this, we utilized the R programming language and software environment to create visualizations that effectively illustrate the results, data, and analyses. These visualizations were designed to align with the interpretative framework of boxplots, ensuring clarity and comprehensibility. This study does not specifically focus on outliers or extreme values, as they are not crucial for the interpretation and evaluation of the proposed hypothesis. The focus instead lies on the median of the group comparison and the upper and lower quartiles of the boxplots. Regarding the model setup, the regression analysis confirmed that the variance explained is higher when the sustainability factors are defined as the independent variable and innovation efficiency as the dependent variable. The following boxplots in illustrate the analysis results and demonstrate positive effects between the identified factors and innovation efficiency.

5.5. The Effect of Governmental Regulations and Policies on Innovation Efficiency

In the graphical representation in Figure 1, the X-axis shows the group comparisons between companies that place a low focus on governmental and legal regulations (‘low’ group) and those that place a high value on those topics (‘high’ group). The Y-axis, on the other hand, represents innovation efficiency (IE1). Each point on the chart represents a company, illustrating the distribution. As seen in Figure 1, there is a strong difference between the ‘high’ and ‘low’ groups. Upon direct comparison, it becomes evident that companies in the GOV1 ‘high’ group (n = 299), with a mean of 0.0687, are more than twice as innovation-efficient on average compared to companies in the GOV1 ‘low’ group (n = 299), with an average IE of only 0.0309.

Also, in Figure 2, there is a significant effect between the two groups in GOV2 regarding IE2, as mentioned earlier. Now, innovation-efficient and -inefficient companies exist within both groups of the variable GOV2. Companies in the GOV2 ‘high’ group (n = 168) are 35% more innovation-efficient, with a mean of 0.0879, compared to companies in the GOV2 ‘low’ group (n = 168), with a mean of 0.0650.

In both cases, the stated hypothesis H1 is confirmed. The ‘high’ groups of GOV1 and GOV2 are significantly more innovation-efficient than the ‘low’ groups.

5.6. The Effect of Environmental Activities on Innovation Efficiency

In Figure 3, the boxplots depict a significant difference in the distribution between the ‘low’ and ‘high’ groups of the factor ENV1 regarding IE1. The ‘high’ group comprises a larger number of companies with higher innovation efficiency. With a mean of 0.0729, the companies in the ‘high’ group are, on average, 94% more efficient compared to the companies in the ‘low’ group, which have a mean of only 0.0376.

Furthermore, as shown in Figure 4, there is a significant difference between the ‘low’ and ‘high’ groups of ENV2 regarding IE2. The companies categorized in the ‘high’ group, with an average innovation efficiency of 0.0796, remain notably more efficient compared to the companies in the ‘low’ group, with an average of 0.0631.

In both cases, the stated hypothesis H2 is confirmed. The ‘high’ groups of ENV1 and ENV2 are significantly more innovation-efficient than the ‘low’ groups.

6. Discussion and Conclusions

This study aimed to explore whether companies in developed countries (specifically in Germany) that actively engage in adhering to governmental and legal regulations (Hypothesis 1) and those that emphasize environmental and resource-saving activities (Hypothesis 2) achieve higher levels of innovation efficiency compared to companies less concerned with these aspects.

These findings support the resource-based view (RBV) by illustrating that firms leveraging sustainability practices can create valuable, rare, and inimitable resources that enhance innovation efficiency. The positive correlation between sustainability initiatives and innovation efficiency aligns with theories suggesting that integrating environmental considerations into business strategy can lead to superior performance.

In relation to the first hypothesis, our analysis indicates that companies in developed countries that adhere to governmental regulations, including compliance with regulatory requirements, taxes, norms, and standards, demonstrate higher levels of innovation efficiency. Our findings for Hypothesis 1 reveal that companies prioritizing governmental and legal regulations (GOV1 and GOV2) exhibit significantly higher innovation efficiency (IE1 and IE2) compared to those with lower levels of engagement in these activities. Specifically, companies in the “high” GOV1 group achieve, on average, more than twice the innovation efficiency of those in the “low” GOV1 group. Similarly, companies in the “high” GOV2 group are 35% more innovation-efficient than those in the “low” GOV2 group.

A possible explanation for this is that regulations provide companies with valuable insights and guidance on the directions to pursue or avoid when engaging in innovation [27], thereby enhancing their innovation efficiency, particularly in the realm of green innovation. This argumentation is consistent with the findings of Bai et al. [22], who suggested that legislative mandates can serve as a catalyst for firms towards sustainable practices and drive innovation. Another reason for this observation could be that firms with high innovation efficiency generally display proactive patterns across multiple areas [66]. Consequently, they tend to be proactive not only in managing innovation efficiently but also in advancing green innovations and adhering to regulations. The finding by Nigg-Stock et al. [66], indicating that innovation-efficient firms are less likely to rigidly adhere to established processes, may initially appear contradictory. However, this flexibility could explain why such firms are more inclined to comply with regulations. As suggested by Xu et al. [51], these firms are more likely to engage in dialogue and collaboration with governmental bodies, facilitating their adherence to regulatory frameworks. Finally, strict compliance with these regulations, once implemented, enables companies to mitigate the risk of incurring penalties and potential damage to their reputations [9,27]. Embracing and proactively meeting regulatory requirements can foster a culture of responsible innovation within the organization. Companies that view compliance as an opportunity rather than a burden respond from intrinsic motivation and are more likely to integrate sustainable practices into their operations, resulting in a more efficient development of innovative solutions that align with environmental and societal needs.

Our findings for Hypothesis 2 show that companies prioritizing environmental and resource-saving activities (ENV1 and ENV2) achieve significantly higher innovation efficiency compared to those with lower emphasis on these aspects. Specifically, companies in the “high” ENV1 group are, on average, 94% more efficient than those in the “low” group, while companies in the “high” ENV2 group also demonstrate notably greater efficiency compared to their “low” group counterparts. In addition to regulatory compliance, engaging in environmental and resource activities plays a crucial role in enhancing a company’s innovation performance and efficiency. Economic growth is inherently associated with rising resource demand, particularly in expanding industries, making it crucial to harness technological advancements to identify new opportunities, develop green innovations, and enhance the efficiency of natural resource utilization in production processes [74]. Our findings indicate that companies emphasizing the significance of environmental activities consistently display greater innovation efficiency than those devaluing these factors. In this context, we might again bring up the finding that innovation-efficient firms in general are proactive firms [66], and, hence, pushing environmental activities is as natural to them as innovating. At the same time, dealing with topics such as the reduction of air pollution, water pollution, waste, and hazardous substances, might also have a cross-over effect on ideas that can be applied throughout the innovation process or result in technological, product, or business model innovations, ultimately improving environmental performance [9]. Also, by considering the environmental impact of their innovations throughout the development process, companies can reduce energy and material consumption, mitigate pollution, and decrease carbon dioxide emissions. Additionally, this focus on environmental sustainability strengthens a firm’s environmental-friendly reputation, which can create a competitive advantage [27]. This proactive approach not only benefits the environment but also leads to cost savings in terms of energy and material usage [23]—and hence also contributes to innovation efficiency.

Previous studies have reported conflicting results regarding the impact of environmental and sustainability activities on innovation efficiency. For instance, Shin et al. [1,13] found that environmental improvement objectives negatively affect innovation efficiency, whereas Bresciani et al. [14] and Kuzma et al. [15] reported a positive relationship between sustainability initiatives and innovation performance. This study fills a gap by providing empirical evidence that both adherence to governmental and legal regulations and active engagement in environmental and resource-saving activities correlate positively with innovation efficiency. By analyzing data from 1196 German companies across various industries, the study provides comprehensive empirical evidence within the German economic context and thereby contributes to the innovation efficiency literature by filling in the lack of studies when it comes to “developed countries”.

In conclusion, our findings indicate that companies prioritizing governmental and legal regulations, alongside environmental and resource activities, demonstrate higher levels of innovation efficiency. This can be attributed to their proactive approach, whereby they anticipate and address potential environmental and social challenges early in the innovation process, fostering the development of sustainable innovations. By aligning their innovation strategies with societal and regulatory expectations, these firms not only strengthen their market position but also build trust and enhance their reputation among key stakeholders, including customers, investors, and employees [5]. Thus, this research underscores the critical role of policies, governmental regulations, and environmental initiatives in driving sustainable innovation and improving innovation efficiency, highlighting that companies within developed countries emphasizing these factors are more likely to develop innovations efficiently. It enables firms to maximize their innovation resources and establish innovation leadership [2].

7. Theoretical and Practical Implications

The study helps reconcile conflicting findings by demonstrating that sustainability practices, both in regulatory compliance and proactive environmental initiatives, positively affect innovation efficiency. The findings suggest that companies should view compliance with regulations and environmental initiatives not just as obligations but as opportunities to enhance innovation efficiency.

From a resource-based view, it highlights these “opportunities” of embracing regulations and environmental initiative as firm resources, which are leverage factors for innovation efficiency, and thus potential competitive advantage.

The study connects the literature field of innovation efficiency and green innovation, adding the perspective of a developed country (Germany) to studies on innovation efficiency and green innovation in China (a developing country).

For policymakers, the study provides evidence supporting the development of regulatory frameworks and incentives that encourage companies to engage in sustainability practices, which, in turn, can drive innovation efficiency.

8. Limitations

Our work has certain limitations, which present opportunities for new avenues of future research. Firstly, there is a limitation concerning the data, which only include information from German companies, potentially limiting the generalizability of the findings in an international, cross-country, or regulatory context. The study does not account for the diverse impacts of different types of regulations, as the scope is confined to the items included in the dataset. Different policies and regulations are deployed in countries depending on their economic state [17]. Also, already within a country policies and regulations may show different results in different regions, e.g., as Liu et al. [46] showed in their study on spatio-temporal characteristics and the influential factors of green innovation efficiency in China. Further, regulations vary widely in their nature, intent, and impact on businesses. For example, Wang et al. [17] pointed out that especially the manufacturing, as well as the agricultural industry, seems to be the focus of sustainability studies. Some regulations may stimulate innovation by setting new standards or providing incentives, while others may impose constraints that hinder innovation efforts. Additionally, the effectiveness and enforcement of regulations differ across countries due to variations in legal systems, cultural norms, and governmental capacities. Therefore, our findings are not generalizable to countries with different regulatory frameworks or to all types of regulations.

Additionally, while the items as a group describe similar content, individual items may vary between GOV1 and GOV2. The choice of survey years could also be called into question. However, considering the large dataset and the aim for data representativeness, the available data from the MIP limited the analysis to the selected years, which aligns with the most recent survey. Furthermore, there are other significant factors that influence sustainability and can have an impact on innovation efficiency. The two selected factors examined here are indeed crucial and frequently discussed in the literature [3,4,5,6,7]; however, they alone do not guarantee a company’s success. Moreover, the sustainability landscape is continuously evolving. The study utilizes cross-sectional data from specific years, providing a snapshot rather than a longitudinal perspective. This limits our ability to assess long-term trends or causal relationships over time. The impact of sustainability practices on innovation efficiency may evolve, and short-term data may not capture delayed effects or long-term benefits. Furthermore, policy changes and economic fluctuations over time can alter the dynamics between regulations, sustainability practices, and innovation efficiency.

Further, our findings indicate a positive association between sustainability practices and higher innovation efficiency. However, this does not demonstrate that the inclusion of sustainability practices necessarily drives innovation efficiency. Further analysis is required, as this study does not examine alternative explanations for this relationship. It could also be that companies with inherently higher innovation efficiency are simply better equipped to cope with regulations and proactively engage in sustainability initiatives. An alternative explanation is that firms possessing high innovation efficiency might have more advanced capabilities, resources, and organizational agility, enabling them to adapt more effectively to regulatory requirements and environmental challenges. These companies may have superior processes, technologies, and human capital that allow them to integrate sustainability practices seamlessly into their operations without detracting from, and potentially enhancing their innovation efficiency. It would be interesting for future researchers to dive deeper into the analysis of the relationship between these variables.

Lastly, and as already mentioned, the geographic focus of the dataset is a limitation as well as a strength of this paper: While results cannot be generalized for all companies, especially in developing countries, it provides valuable insight into the effect of regulations and environmental activities within Germany, a developed country.

9. Future Research Directions

Future research endeavors should consider conducting a qualitative analysis of the factors identified in this study. Such work will further facilitate the understanding of specific strategies and best practices that companies demonstrating superior innovation efficiency have implemented. A study published in the short term by Deng et al. [48] employed data envelopment analysis to scrutinize eco-efficiency. As a result, an analytical juxtaposition of innovation efficiency with eco-efficiency, evaluating their disparate impacts, would be of considerable academic interest. Additionally, the examination of textual documents such as sustainability reports and corporate annual reports may offer valuable insights [75].

Future investigations could involve direct company surveys to obtain qualitative insights and identify additional factors beyond the scope of the current study. For instance, the social component and the entire framework of Sustainable Development Goals (SDGs) are increasingly important and should be considered in future research, as they were not addressed in this analysis.