The relation between Environmental Performance and Financial
Performance: Does Collaboration Breadth matter?
Master Thesis Strategic Innovation Management JORDI VAN HOLTEN
University of Groningen Faculty of Economics and Business
j.van.holten@rug.nl Student number: s2366975 Supervisor: Thijs Broekhuizen
Co-assessor: Wim Biemans
Word count excluding appendices and references: 6345
Abstract
Despite an increased interest in environmental innovation among academics and practitioners,
research has not yet adequately addressed whether and when improvements in eco-innovations
lead to financial performance. Recent literature finds mixed results. This study tries to further
explore this question and investigate the important role of collaboration breadth – i.e. the
number of different partners firms work with – in moderating this relationship. By using an
existing dataset of 432 SME’s environmental and financial performance, this study does not
find support for a direct relationship between environmental and financial performance, but it
is found that the environmental-financial link is clearly moderated by collaboration breadth.
Keywords: Environmental Performance, Sustainable Performance, Financial Performance, FirmPerformance, Collaboration Breadth, Collaboration Diversity
Table of Content
1. INTRODUCTION ... 3
2. THEORETICAL BACKGROUND AND HYPOTHESES ... 4
2.1ENVIRONMENTAL PERFORMANCE AND FINANCIAL PERFORMANCE ... 5
2.2COLLABORATION BREADTH ... 7
3. METHODOLOGY ... 8
3.1DATA COLLECTION AND MEASUREMENT ... 8
3.2ANALYTICAL METHOD ... 10
4. RESULTS ... 10
4.1DESCRIPTIVE STATISTICS AND CORRELATIONS ... 11
4.2REGRESSION RESULTS AND HYPOTHESES TESTING ... 12
4.3SIMPLE SLOPE ANALYSIS ... 14
4.4ROBUSTNESS CHECKS AND ADDITIONAL ANALYSIS ... 15
4.4.1 Fischer’s Exact Test ... 15
4.4.2 Effect of cost-saving environmental innovations on the Financial Performance ... 15
4.4.3 Effect of specific collaboration types on the EP-FP relationship ... 16
5. CONCLUSION AND DISCUSSION ... 16
5.1CONCLUSION ... 16
5.2DISCUSSION ... 16
5.3LIMITATIONS AND FUTURE RESEARCH ... 17
APPENDICES ... 19
1. Introduction
Laursen & Salter, 2006; Rennings, 1998). This study contributes to the existing knowledge on the relationship between EP and FP by taking the number of different collaborating partners a firm works with into account as a moderating effect as it is expected that this might influence the EP-FP relationship. Where previous studies use collaboration as an independent variable, this study uses collaboration as a moderator.
The data in this paper comes from research that is initiated by the Samenwerkingsverband Noord-Nederland (SNN) and the RUG in an annual survey: The Innovation Monitor. The paper is structured as follows: section two shows the relevant literature that is used to theoretically support the hypotheses and analysis and contains the conceptual model. The third section contains the methodology that outlines the data and the analytical methods. In the fourth section, the empirical results will be shown as a result from the different tests that are performed. Finally, in the last section the results will be discussed and several implications, as well limitations will be given together with suggestions for future research.
2. Theoretical Background and Hypotheses
In this section, the relationship between EP and FP and how it is possibly moderated by collaboration is examined. To understand this relationship and the possible effect of collaboration, this study draws upon the Resource Based View (RBV). The RBV, in short, is a framework that determines the strategic resources a firm has in order to get a competitive advantage (Barney, 1991). Furthermore, collaboration literature is used that assumes that working together to innovate can yield positive and negative outcomes to determine the effect of CB on the EP-FP relation.
(+)
(+) (-)
Figure 1: Conceptual model
2.1 Environmental Performance and Financial Performance The definition of Environmental Performance
Environmental performance has different meanings in current literature as shown in table 1. A review of these explanations of EP reveals that it can be seen as a measure of the increasing efficiency of production and processes by reducing and decreasing solid waste and consumption and pollution of hazardous and toxic materials. Furthermore, there can be a distinction made between two types of EP. The first is about the environmental consequences that results from environmental innovations such as the reduction in pollution (Ekins, 2010). Secondly, there is the economic consequence resulting from environmental innovations such as the more efficient use of materials and energy that can lead to lower costs (Ekins, 2010). Nevertheless, it should be noted that introducing eco-innovations may not automatically lead to lower costs because the implementation costs should also be considered. In this paper, environmental as well as the economic consequences of EP are involved in the analysis1.
Term Purpose Definition Study
Environmental Performance
Meta-analysis of 40 studies about whether and under which circumstances environmental performance affects financial performance.
Use of recycled materials, environmental liabilities,
environmental penalties, pollution measures, emission reductions and several ratings such as the IRRC efficiency index and the
environmental index.
Horváthová (2010)
1 A firm-based perspective is used for the analysis where only the effects of eco-innovations internal to the firm are considered and not about whether the use of products by customers is more environmental friendly (e.g. the use of more fuel-efficient cars).
Environmental
Performance (EP)
Financial
Performance (FP)
Collaboration
Environmental Performance
When and why does it pay to invest in environmental performance.
A measure of efficiency, meaning that less efficient firms pollute more.
(Ambec & Lanoie, 2008) Environmental
Performance
Role of customer relational governance in environmental and economic performance
improvements through green supply chain management.
Ability of manufacturing plants to reduce air emissions, decrease solid waste and consumption of hazardous and toxic materials.
(Zhu, Feng, & Choi, 2017)
Environmental Performance
Effect of corporate environmental strategy and environmental performance on competitiveness and financial performance.
A measure that assesses the reduction of firms’ environmental impacts in a number of categories: reduction in water, energy, non-renewable resources, toxic inputs, solid waste and contamination, emissions, noise, smell, landscape damage, risk of severe accidents.
Wagner & Schaltegger (2004)
Green Product Innovations
Improve consumer drivers of attractiveness for environmentally preferable products.
“The degree to which green products or environmentally products are commonly used to describe those that strive to protect or enhance the natural environment by conserving energy and/or resources and reducing or eliminating the use of toxic agents, pollution and waste.”
(Dangelico & Pujari, 2010)
Table 1: Definitions Environmental Performance
The relation between Environmental Performance and Financial Performance
(Suarez & Lanzolla, 2007) by selling pollution control technology as well selling products that are produced with more regard for the environment as a premium (Porter & van der Linde, 1995). Moreover, a firm can also be the first to set a product standard that creates a new standard for the industry (Lieberman & Montgomery, 1998), this product standard can be the new ‘environmental standard’ for the industry and other firms have to follow. Porter & van der Linde (1995) also imply that reducing pollution and waste, decrease the expenditures on energy and materials. This can be a reason for firms why FP may improve as a result of EP. Konar & Cohen (2001) found that in general, a moderate positive relationship exists between EP and FP and that an above average EP does at least not result in a negative effect on the FP of a firm. Following Porter (1991), they argue that environmental regulations within a company could lead to a win-win situation for a firm such that the financial benefits increase.
Based on the above literature, I expect that the positive effects of EP result in the following hypothesis regarding the relationship between EP and FP:
H1: Environmental Performance (EP) has a positive relationship with Financial Performance (FP).
2.2 Collaboration Breadth
Collaboration between a focal firm and other firms is already studied frequently (Faems et al., 2005; Laursen & Salter, 2006), also in relation with EP and/or FP. Here it is found inter-firm linkages enhance firm performance in the light of EP (Dyer & Singh, 1998; Grekova et al., 2016), EP should be achieved over the entire supply chain to increase performance (Govindan, Seuring, Zhu, & Azevedo, 2016; Zhu et al., 2017) and the importance of selecting the right collaboration partners (Green, Zelbst, Bhadauria, & Meacham, 2012). CB as a moderating effect on the EP-FP relation specifically was not part of environmental studies yet and therefore studied in this paper. CB contains in general the following collaborating types: (1) vertical collaboration with suppliers and clients, (2) horizontal collaboration with competitors and (3) collaboration with universities or research institutions (Faems et al., 2005). Every collaboration type has certain opportunities and challenges that companies face depending on their collaboration goal. I first discuss the negative direct effect of CB on FP. Thereafter, the positive moderating effect of CB on the EP-FP relationship is discussed.
different partners: (1) too many ideas to manage and to choose between (absorptive capacity), (2) innovative ideas may come at the wrong time and place to be fully exploited (timing) and (3) there are too many ideas and only few of them are taken seriously by the manager (attention/allocation problem). These coordination costs could lead to additional costs of collaboration with different types of partners. Considering above literature, the following hypothesis is developed regarding the direct relation between CB and FP.
H2a: Collaboration Breadth has a negative relationship with Financial Performance.
Furthermore, I argue that CB has a strengthening effect on the EP-FP relationship. In recent literature it is found that inter-firm linkages enhance firm performance in the context of EP (Dyer & Singh, 1998; Grekova et al., 2016), that collaboration with external partners enhance product innovation performance and create new innovation opportunities for firms (Faems et al., 2010; Laursen & Salter, 2006) and that firms with heterogeneous collaboration networks perform better in terms of FP (Faems et al., 2005). Drawing upon RBV that portrays relationships as social capital, I expect that a greater number of different partners yield greater social capital that can be leveraged (de Clercq et al., 2009), a firm with a larger network might be able to find more easily the correct collaboration partners and complementary resources by using their network. For small firms social networks are found to be important because their need for external resources (Tung, 2012). Second, a greater number of partners yields greater access to complementary resources, more possibilities for effective knowledge recombination and more market share (Faems et al., 2005; Rennings, 1998). Finally, companies can learn from each other by sharing knowledge and obtaining resources, a process that takes longer without collaboration (Teece, 1986; Hagedoorn, 1993). Collaboration increases the effectiveness of making innovations successful such that the effect of EP on FP gets stronger for higher levels of CB resulting from the learning, networking, access to complementary resources and knowledge sharing capabilities. Therefore, based on the above literature, I hypothesize that CB strengthens the EP-FP relationship.
H2b: Collaboration Breadth strengthens the relationship between EP and FP such that the positive effect of EP on FP becomes stronger for higher levels of Collaboration Breadth.
3. Methodology 3.1 Data Collection and Measurement
Netherlands and compare these activities with other firms in this area. The year 2016 was the first year that the survey was conducted and the companies were asked to base their answers between the time period of 2014 and 2016. A total of 432 responses were captured and the survey was sent to 2995 companies, resulting in a response rate of 14.4% All companies are small and medium sized with a maximum number of 200 employees. All questions in this survey are asked in Dutch and were sent by e-mail with at least two reminders.
Independent variable: Environmental Performance
For this research, the dataset of the SNN exists of six questions about environmental innovations that caused a reduction in for example materials, waste, energy and pollution. In the dataset, the reduction in certain environmental areas are measured by dummy variables on a yes/no scale. The exact question is: “Did your company introduced a product, process or organizational change that resulted in the following environmental advantages”: (1) lower material usage per unit, (2) less energy usage per unit, (3) smaller CO2 footprint, (4) substitution of materials by less toxic or dangerous materials, (5) less
pollution of surface, water, air of noise and (6) recycling of waste, water and materials. Questions 1 and 2 can be seen as cost-saving innovations that might have a direct effect on the FP. Questions 3-6 are not directly cost-saving results and can be seen as measurements that only have environmental impact (cost-enhancing). In this paper, the items all pertain to one overall construct and will be seen as one variable called ‘Environmental Performance’ (EP). I tested the factorial structure of the EP construct and find a single solution. This provides evidence that the respondents do not consider these innovations to be part of two dimensions but treat it as a unidimensional construct. Also Cronbach’s alpha is reasonably high (0.855) (see Appendix 1 and 2 for the factor loadings and Cronbach’s Alpha). For the regression analysis I created a composite score by summarizing the six environmental dummy variables, which has a minimum value of zero (no innovation introduced) to a maximum score of six (six different eco-innovations introduced) with a mean of 1.54 and a standard deviation of 1.99.
Moderator: Collaboration Breadth
a minimum value of zero (no collaboration partners) to a maximum score of six (six different collaboration partners) with a mean of 1.27 and a standard deviation of 1.78.
Dependent variable: Financial Performance
Financial Performance is measured by a relatively simple measure on a three point scale to indicate whether a firm has losses (1), draws break-even (2) or reports profits (3). In the usable responses, 15.7% of the respondents reported losses, 18.9% break-even and 65.4% answered that they were profitable. Control variables
It is important to control for factors such as firm size, firm age and industry sector when doing research on the FP of a firm (Doğan, 2013; Horváthová, 2010; Wagner & Schaltegger, 2004; Wagner et al., 2001; Wagner, Van Phu, Azomahou, & Wehrmeyer, 2002). Therefore, I control for these three factors because of their availability in the dataset and their importance. Firms size is measured by the number of employees, firm age in years and the industry sector is a dummy variable for six different industry sectors: (1) Consultancy (2) Industry (3) Construction (4) Retail (5) Information and Communication (6) Others (accommodations, transport, education, culture, recreation, healthcare). Consultancy is considered as the base-line dummy and therefore not entered in the regression analysis.
3.2 Analytical Method
To test for the relationships between variables, a linear hierarchical regression is performed in SPSS.
Thereafter, a simple slope analysis is done to test if there is a significant relation between the independent variable (EP) and dependent variable (FP), also at different levels of the moderator (CB) (Dawson, 2014; Aiken, West, & Reno, 1991). Before performing the tests, all variables except the dependent variable (FP) are mean-centered to get a better interpretation of the results and meaningful values can be used (Dawson, 2014)2. Mean-centering the variables does not have any influence on the detection of moderating or interaction effects, but it does fix the problem of multicollinearity (Dalal & Zickar, 2012; Kromrey & Foster-Johnson, 1998). Finally, in section 4.4 several robustness checks are performed.
4. Results
In this section, an overview of the descriptive statistics and correlations are given in table 2 and 3, the outcomes of the regression will be shown in table 4. Moreover, a simple slope analysis and several robustness checks can be found in section 4.3 and 4.4.
2The graphs and simple slope test in this paper are based on the article of Dawson (2014), the documents with
4.1 Descriptive Statistics and Correlations
N Min. Max. Mean Std. Deviation
Financial Performance (FP) 356 1 3 2.50 0.752
Company age 414 0 204 25.22 30.27
Company size 363 0 200 18.99 32.25
Environmental Performance (EP) 414 0 6 1.54 1.99
Collaboration Breadth (CB) 414 0 6 1.27 1.78
Valid N (list-wise) 354
Table 2: Descriptive statistics
In tables 2 and 3 the descriptive statistics and correlations are shown for the variables that are used in the regression before mean-centering and without the interaction term.
Table 3: Pearson Correlations Statistics. Two-tailed
*significant at p ≤ 0.05 level, **significant at p ≤ 0.01 level
Frequencies N 0 1 2 3 4 5 6
Financial performance (FP) 1=loss, 2=break-even, 3=profit
356 56 67 233
Industry sector dummies 348 172 39 18 30 20 69
4.2 Regression Results and Hypotheses Testing Table 4: Hierarchical Linear Regression
Model 1 Model 2 Model 3 Model 4
(Constant) 2.488*** (0.057) 2.487*** (0.058) 2.491*** (0.057) 2.466*** (0.059) Control variables: Firm age 0.003** (0.002) 0.003** (0.002) 0.003** (0.002) 0.003** (0.002) Firm size 0.004*** (0.001) 0.004*** (0.001) 0.004*** (0.001) 0.004*** (0.001) Sector dummies: Industry 0.082 (0.132) 0.084 (0.133) 0.111 (0.133) 0.149 (0.134) Construction 0.124 (0.193) 0.124 (0.193) 0.095 (0.193) 0.134 (0.193) Retail 0.075 (0.150) 0.075 (0.150) 0.077 (0.149) 0.093 (0.149) Information and Communication -0.074
(0.176) -0.070 (0.176) -0.069 (0.176) -0.065 (0.175) Others 0.215* (0.110) 0.214* (0.110) 0.189* (0.111) 0.175 (0.110)
Environmental Performance (EP) 0.006
(0.020) 0.018 (0.021) 0.012 (0.021) Collaboration Breadth (CB) -0.047* (0.024) -0.063** (0.026) EP*CB 0.020* (0.010) R2 0.082 0.083 0.094 0.105 Adjusted R2 0.060 0.057 0.066 0.074 F-test 3.763*** 3.295*** 3.378*** 3.417*** N 302 302 302 302
*significant at p ≤ 0.1 level, **significant at p ≤ 0.05 level, ***significant at p ≤ 0.01 level
The first model of the regression only contains the control variables and industry dummies. In the second and third model the independent variable EP and the moderator CB are added. The fourth model contains the interaction term between EP and CB. The results of model 4 are reported because of the highest R2
(10.5%) and the p-value level of the F-test is less than the significance level meaning that the data provides sufficient evidence to conclude that the regression model fits the data. Also there is no sign of multicollinearity as can be seen in table 5, all VIF values are all well below the suggested cut off of 10 (Schumacker & Robinson, 2009).
Variable VIF Firm age 1.187 Firm size 1.236 Industry 1.160 Construction 1.090 Retail 1.075
Information and Communication 1.060
Others 1.140
Environmental Performance (EP) 1.167
Collaboration Breadth (CB) 1.322
EP*CB 1.262
Table 5: Variance Inflation Factors values
The regression outcomes imply that the control variables firm age (b = 0.003, p ≤ 0.05) and firm size (b = 0.004, p ≤ 0.01) have a positive effect on FP, meaning that the greater the firm size and age, the higher the FP. The sector dummies have no significant effect on FP. The regression also shows that EP has no significant effect on FP, so H1 is rejected. As hypothesized by H2a, CB has a significant negative effect (b = -0.063, p ≤ 0.05) on the dependent variable, meaning that higher CB leads to lower FP. The interaction effect between EP*CB shows a positive significant effect on FP (b = 0.020, p = 0.062) despite the direct negative effect between CB and FP, this implies that CB strengthens the relationship between EP and FP as expected in H2b.
analysis is performed in the next section to identify the significant regression betas under different conditions of CB.
Figure 2: Relationship between EP and FP Figure 3: Relationship between EP and FP 4.3 Simple Slope Analysis
A simple slope analysis is done to test if there is a significant relation between the independent variable (EP) and dependent variable (FP) for different levels of the moderator (CB) to check whether the simple slopes as plotted in figures 2 and 3 are different from zero. To calculate this, I use the insights and the calculation documents from Dawson (2014). As mentioned earlier, the variables are mean-centered to be able to get meaningful interpretations of the results of the simple slope. To perform the test, the variances3 of the coefficient of the independent variable (EP) and interaction term (EP*CB), as well as the covariance of the coefficient of the independent variable – interaction term are needed. Next to the information about the coefficients and the descriptives, the simple slope analysis can be performed according to the calculations and documents from Dawson (2014).
Value of CB: 0 1 2 3 4 5 6
Gradient 0.012 0.032 0.052 0.072 0.092 0.122 0.132
t-value 0.571 1.459 1.973* 2.113** 2.168** 2.179** 2.176**
p-value 0.568 0.146 0.054 0.035 0.031 0.030 0.030
Table 6: outcomes simple slope analysis. *significant at p ≤ 0.1 level, **significant at p ≤ 0.05 level
Table 6 shows that when the value of the moderator CB is 0 or 1, the slope is not significantly different from zero, but when the value of CB is 2 or more it becomes significant and positive. This means that the value of CB has to be at least 2, to be of significant influence. A company thus needs a certain amount of collaboration partners in order to establish a successful positive relation between EP-FP.
4.4 Robustness Checks and Additional Analysis 4.4.1 Fischer’s Exact Test
Fischer’s exact test is performed to check the assumption that CB helps the conversion of EP into FP and whether there is an association between product/process innovation and CB. Product/process innovations are found to be an important factor because it can show that CB works through knowledge sharing as provided by product innovation, as stated by Faems et al. (2005) arguing that explorative collaboration can create learning processes. A Fischer’s test is performed because the data is dichotomous and there were multiple columns with n<5, what makes a Chi-squared test unreliable. Respondents that answered at least one questions about product or process innovation with a ‘yes’, are coded as ‘1’. If the respondents did not do any form of product or process innovation, they are coded as a ‘0’. A higher rate of product innovation (e.g. the percentage of firms that have introduced a product innovation) or process innovation (e.g. the percentage of firms that have introduced a process innovation) is indicative that a higher CB is indeed associated with greater innovation. An absence of this association would indicate that the strengthening goes via other mechanisms.
The result of Fischer’s test is that only product innovation turned out to be significant at p = 0.000 (table 7). This means that there is a significant association between companies that do product innovations and CB. To check whether this association is positive, a Pearson correlation is performed that shows a positive relation (0.283, p = 0.000). Furthermore, I look at Cramer’s V to check the strength between the variables, where a value of 0 indicates no association and a value of 1 perfect association. Table 7 shows a significant association between product innovation and CB (0.305, p = 0.000).
Table 7: Fisher’s Exact Test Product Innovation
Product Innovation
Collaboration breadth score
0 1 2 3 4 5 6
Yes 152(52.6%) 9(3.1%) 26(9.0%) 39(13.5%) 38(13.1%) 18(6.2%) 7(2.4%)
No 101(80.8%) 7(5.6%) 2(1.6%) 9(7.2%) 5(4.0%) 1(0.8%) 0(0.0%)
Fisher’s exact test: (n=414) = 40.096, p=0.000 (two-sided), Cramer’s V: 0.305, p = 0.000
4.4.2 Effect of cost-saving environmental innovations on the Financial Performance
and FP, also the interaction between the cost-saving innovations and CB shows no significant effect. Therefore, it can be concluded that companies with more cost-saving efforts regarding environmental innovations do in general not have a stronger conversion into FP, even with the presence of multiple collaboration partners.
4.4.3 Effect of specific collaboration types on the EP-FP relationship
It is possible to distinguish between public collaborations (universities and knowledge institutions) and private collaborations (e.g. customers and competitors). To check whether the effect of EP on FP is stronger for certain collaboration types, a separate regression is done showing a direct negative effect between public collaborations and FP (b = -0.541, p = 0.004), no significant relation between private collaborations and FP is found. Also no significant relation is found between the interaction of public or private collaboration with EP on FP. Therefore, it can be concluded that in this case, collaboration with either public or private partners does not positively affect the EP-FP relationship and a combination of both is needed as shown in the regression in section 4.2. This is in line with the argument that multiple partners are needed to strengthen the relationship between EP and FP.
5. Conclusion and Discussion 5.1 Conclusion
This thesis investigates and builds upon existing literature by examining the effect of CB (i.e. the number of different collaboration partners), on the relationship between EP and FP. In line with the mixed findings regarding the relationship between EP-FP (Horváthová, 2010), this study also does not find a strong main effect. This study - also in line with findings from other studies that find that firm characteristics act as moderators - finds that an organizational characteristic, namely CB, strongly determines the relationship strength.
5.2 Discussion
shows that the positive effect only appears when both public and private partners are involved. These findings are an addition to current studies from Horváthová (2010) and Grekova et al. (2016) where this thesis gives an additional explanation for the strengthening effect of CB on the EP-FP relationship. Possible explanations for this effect can be the networking capabilities of a firm that can be used to enhance the transformation of EP into FP, the access to additional resources and the learning opportunities of a firm from external partners. This paper also links product innovation to collaboration to find additional explanations for the positive effect of CB on the EP-FP. Supportive analysis reveal that CB is positively associated with more product innovation. This implies that CB works through knowledge sharing as provided by product innovation. However, no evidence is found that firms with explorative oriented collaborations (universities and research institutions) have stronger effects on the EP-FP relation. An explanation for this can be that managing partnerships with science-based organizations costs time and money and is difficult because of different orientations and mindsets (Du, Leten, & Vanhaverbeke, 2014). This may undo these positive effects or do not impact it at all.
This study also provides several important implications and insights for managers. It supports previous findings that managers should be careful in considering the number of collaboration partners because it might have negative effects on the FP of the firm. Nevertheless, it is supported that collaborating with different external partners has advantageous effects on the relation between EP and FP. This study shows that at least two different partners are needed to gain benefits from collaborating to increase the EP of a company resulting in a better FP. By having more diverse partners, firms get access to additional resources and can learn from each other resulting in a more efficient and effective development of environmental innovations. For managers it is important to realize that by collaboration with multiple partners, a company could be able to benefit from EP. EP itself has no significant effect on FP what might be caused by the combination of cost-saving and cost-enhancing effects of environmental innovations, resulting in that the pay-off remains unchanged.
5.3 Limitations and Future Research
the sample only contains companies up to 200 employees situated in the northern part of the Netherlands. Companies that are larger and from other geographic areas are not taken into account. Future research could assess whether the moderating effect holds for larger firms and whether they can also benefit from greater diversity in partners.
Appendices
Appendix 1: Factor Analysis Rotated Component Matrix (Varimax), N = 414
Factor 1 Factor 2
Material reduction .727 .081
Energy reduction .806 .097
CO2 reduction .771 .240
Less toxic materials .731 .084
Less noise pollution .754 .140
More recycling .709 .126
Collaboration customers .088 .804
Collaboration consultants .119 .656
Collaboration suppliers .163 .743
Collaboration competitors .112 .505
Collaboration knowledge inst. .056 .793
Collaboration other companies .163 .815
Appendix 2: Cronbach’s alpha
Cronbach’s alpha Number of items N
Environmental Performance .855 6 414
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