This is a self-archived – parallel published version of this article in the 

publication archive of the University of Vaasa. It might differ from the original. 

Does corporate social responsibility impact 

firms' innovation capacity? The indirect link 

between environmental & social governance 

implementation and innovation performance 

Author(s): Broadstock, David C.; Matousek, Roman; Meyer, Martin; Tzeremes, 

Nickolaos G. 

Title: Does corporate social responsibility impact firms' innovation capacity? 

The indirect link between environmental & social governance 

implementation and innovation performance 

Year: 2020 

Version: Accepted manuscript 

Copyright ©2020 Elsevier. This manuscript version is made available under the 

Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 

International (CC BY–NC–ND 4.0) license, 

https://creativecommons.org/licenses/by-nc-nd/4.0/ 

Please cite the original version: 

 Broadstock, D. C., Matousek, R., Meyer, M. & Tzeremes, N. G. (2020). 

Does corporate social responsibility impact firms' innovation capacity? 

The indirect link between environmental & social governance 

implementation and innovation performance. Journal of Business 

Research 119, 99-110. https://doi.org/10.1016/j.jbusres.2019.07.014 

 



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Does Corporate Social Responsibility Impact Firms' Innovation Capacity? The Indirect Link between Environmental & Social Governance Implementation and Innovation Performance   David C. Broadstock1, Roman Matousek2, Martin Meyer3*, Nickolaos G. Tzeremes4  

1School of Accounting and Finance, Center for Economic Sustainability and Entrepreneurial Finance, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Email: david.broadstock@polyu.edu.hk 
2 Nottingham University Business School, Jubilee Campus, Nottingham NG8 1BB, UK. Email: Roman.Matousek@nottingham.ac.uk 

3*Kent Business School, University of Kent, Canterbury, Kent, CT2 7FS, UK. Email: M.S.Meyer@kent.ac.uk (Corresponding author) 
4Department of Economics, University of Thessaly, 28th October street, 78, 38333, Volos, Greece. Email: bus9nt@econ.uth.gr 



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Does Corporate Social Responsibility Impact Firms' Innovation Capacity? The Indirect Link between Environmental & Social Governance Implementation and Innovation Performance   Abstract 

Firms’ choices on corporate social responsibility (CSR) and on environmental, social and governance (ESG) implementation strategies can arguably have a positive impact on their value and performance. This “doing well by doing good” view remains debated in the literature. Our 

study contributes to this debate by investigating the impact of firms’ engagement on ESG policies on their innovation capacity levels. More specifically, we apply a nonparametric frontier analysis framework to a sample of 320 Japanese firms over the period 2008-2016. Our study 
provides evidence of a nonlinear relationship between ESG policy adoption and firms’ innovation capacity. In other words, our findings are consistent with a process of “indirect value- 
creation” under which firms’ CSR/ESG policy adoption initially enhances their ability to pursue innovation activities and, then, eventually affects positively their value creation and financial/operational performance. 
Keywords: Corporate Social Responsibility (CSR); Environmental and Social Governance (ESG); Corporate Social Performance (CSP); Innovation capacity; Efficiency and Productivity; Nonparametric Analysis. 
JEL Classification: C14; D24; M14; O3     1. Introduction  The Berle-Dodd debate challenges whether there is any impact of firms’ engagement in 

corporate social responsibility (CSR) activities to shareholder valuation and firms’ performance, 

and is the basis of an ongoing research agenda (Ferrell et al. 2016). Specifically, Berle’s view is 

that firms should be seen only as the maximisers of shareholders’ value. Dodd’s view supports 

the notion that firms should be accountable to stakeholders and general public. Deng et al. (2013) 
assert that when firms are well governed they are able to be both socially responsible and of 
higher performance, maximizing also shareholders’ value. However, the opposing view suggests 

that firms’ objective functions are to generate profits and any managerial involvement in CSR 



2  

activities is rather a barrier to the accomplishment of the profit maximizing purpose (Cheng et 
al., 2013). There has been a large number of empirical studies that support both the positive and 
negative effect of CSR activities on firm’s performance (Jawahar et al., 2001; Jensen, 2001; 
Freeman et al. 2004; Surroca and Tribo, 2008; Cronqvist et al., 2009). 

However, empirical studies that test the “doing well by doing good” hypothesis (Bénabou 
and Tirole, 2010; Krüger, 2015) are rather inconclusive and conflicting. Factors which contribute 
to this myriad of findings include applying different methods, sample sizes and using different 
proxies of CSR activity/performance (Margolis and Walsh, 2003; Wang et al., 2016). The 
phenomenon of conflicting results may however also be attributed to the fact that there are 
missing links between CSR activities and firms’ performance which have not been thoroughly 
parameterized, analyzed and explained (Isaksson and Woodside, 2016; Bhardwaj et al. 2018). 
Lioui and Sharma (2012) refer to these ‘missing links’ as the indirect effects of CSR on firms’ 

performance, which can give rise to indirect value creation. Moreover, Lioui and Sharma (2012) 
contend that this indirect effect of CSR activities to firms’ performance comes through the 

enhancement of firms’ research and development (R&D) efforts. It is evident that there remains  

a gap in the literature to investigate the influences of CSR practices on firms’ performance 

through its indirect effect on firms’ innovation capacity (McWilliams and Siegel, 2000; Lioui 

and Sharma, 2012; Hull and Rothenberg, 2008; Bocquet et al. 2013; Costa et al., 2015). The 
European Commission (2011) also identifies this as a research gap and has placed an emphasis 
on the need to further explore the empirical influences of CSR to economic growth through the 
process of innovation. 

In this paper, given the above identified gap in the literature and set against the backdrop 
of institutional relevance established by the European Commission, we explore whether 



3  

enhanced CSR acts as a stimulant of successful innovation activity. Starting out from the premise 
that CSR ‘needs some shaking up’, Mirvis and colleagues argue that “successful innovation is 

about dramatically improving what currently exists or creating something new that is significant 
and useful. This requires new voices, new ideas, new processes and interactions, and renewed 
passion to make a difference in the world” (Mirvis et al., n.d.; p. 8). Building on the work of 

stakeholder theorists (Freeman, 1984; Donaldson & Preston, 1995; Battacharya & Korschun, 
2008; Freeman et al., 2010; Brower & Mahajan, 2013) who remind us that managers seek to 
satisfy a variety of constituents who can influence or are influenced by the firm’s objectives, 

Costa el. (2015) argue that satisfying their organization’s stakeholders depends on managers’ 

ability to learn about the stakeholder landscape, develop a good understanding of and effectively 
incorporate this wide range of interests. Establishing and maintaining good external stakeholder 
relations in this framework can enable the firm to access diverse external knowledge and 
information (Choi & Wang, 2009; Costa et al.; 2015) and thereby increase its absorptive capacity 
as well as grow its innovation related knowledge, encouraging higher levels of innovation 
activity and capacity (McWilliams & Siegel, 2000). Some authors go so far as to suggest that 
companies which do not take CSR into account, may not survive as they may struggle to 
innovate (see, e.g., MacGregor et al., 2007; Mahlouji & Anaraki, 2009). While not arriving quite 
at that conclusion, Porter and Kramer (2007, 2011) make the case that CSR can be a potent 
source of innovation and competitive advantage, pointing to a wide range of case studies. In this 
contribution, we will look more systematically at the link between CSR engagement and 
innovation performance. 

We advance the literature on innovation by gleaning new insights on the positive effects 
of firms’ engagement in corporate social responsibility activities to their innovation capacity 



4  

levels. Our analysis, and evidence, is consistent with the idea that CSR activities act as a source 
of organizational ambidexterity, catalyzing both traditional, and non-technological innovation 
processes that can be exploited by firms to enhance corporate financial performance. For the 
purpose of our analysis, we apply a nonparametric frontier estimation procedure (Daraio and 
Simar, 2005; Bădin et al., 2012, 2014). We examine whether firms’ involvement in CSR 

activities affects the technological change levels (i.e., movements of firms’ estimated production 

frontier). According to Shao and Lin (2016), the technological change levels reveal firms’ 

innovation capacity. We use a sample of 320 Japanese firms over the period 2008-2016. We 
apply conditional time-dependent estimators (Mastromarco and Simar, 2015) that allow us to 
reveal potential dynamic nonlinear effects of firms’ environmental, social and governance 

(ESG)1 practices on their innovation capacity levels. Our estimation is based on the method 
introduced by Daraio et al., (2018) that further allows us to assume that firms’ decisions which 

are related to ESG adoption criteria, affect the shape and the level of the boundary of the 
estimated production frontier alongside with the distribution of firms’ efficiency levels. As a 

result of that, the adopted methods reveal the existence of potential dynamic nonlinear effects of 
ESG practices on firms’ operational performance levels through their impact on firms’ 

innovation capacity (technological change). 
To our best knowledge this is the first study which models the indirect effect of CSR 

practices on firms’ performance levels through its impact on firms’ innovation capacity. The 

related literature (McWilliams and Siegel, 2000; Lioui and Sharma, 2012; Hull and Rothenberg, 
 

1 Environmental, Social and Governance (ESG) is composite index (score) which is provided by Bloomberg and is 
used as a proxy of firms’ engagement on CSR activities (Gillan et al.,2010). More specifically, one might think of CSR as a subjective and broadly encompassing notion, with imprecisely defined (or fuzzy) boundaries. Conversely, ESG scores are objectively and consistently defined measures permitting like-for-like measurement of firm-specific CSR activities. ESG and CSR are intimately connected though it should be recognised that CSR may encompass additional activities that either are not, or cannot be, reflected in standard ESG scores processes.  



5  

2008; Bocquet et al., 2013) provides empirical evidence of the impact on CSR practices on 
firms’ R&D levels but fails to link this indirect effect on firms’ operational performance. Our 

study fills this gap by using a probabilistic approach for estimating firms’ production frontiers. 
The remainder of the paper is as follows. Section 2 discusses the background literature, while, 
Section 3 presents the data and the methods adopted. Section 4 presents our main findings and 
finally, the last Section offers a discussion of findings vis-à-vis the literature and outlines 
avenues for further research. 
2. Corporate social responsibility as an enabler of firm performance  In this section we provide a brief synopsis of the background literature relevant to our 
own study. There are several core bodies of literature which are relevant. We begin by first 
setting out the conflicting studies of the CSR-firm performance link and briefly reflect on the 
importance of and perils involved in measuring CSR and corporate social performance (or CSP) 
i.e. the positive/negative outcomes that come with CSR activities. We then move towards a more 
thorough summary of existing research findings on the impact of CSR to firm financial, or 
operational, performance. After this we proceed to discuss additional literature outlining 
potentially more nuanced channels through which CSR can foster indirect benefits to firm 
performance, with a special emphasis on CSR’s role in catalyzing innovation. 

   2.1 CSR and Firm Performance  There has been a long-standing interest among scholars, in understanding whether CSR 
activities enhance, diminish, or have no material impact to firm performance. The tension in this 
hypothesis can be easily established. Good CSR should in principle align with good overall 



6  

governance, which is a core component of a strong and sustainable business model (albeit not 
guaranteed to be) capable of delivering long term financial performance. However good CSR 
also comes at a cost, requiring firms to engage in new activities to build and maintain their CSR 
image, some of which having intangible returns. If CSR expenses are material, and returns to 
CSR intangible, investors and stakeholders may struggle to accurately appraise the long-run 
value proposition. The literature studying this relation has until now accrued in excess 50 years 
of contributions and is booming in interest during recent years. In light of the large body of 
existing literature and in lieu of the fact that regarding this aspect of the literature we essentially 
‘inherit’ existing conceptualizations and theories, as opposed to developing new ones of our  

own, to keep our review both succinct and focused we do not provide a comprehensive review of 
existing work. Instead we take advantage of several high-quality review studies that provide 
sound coverage of the corpus of prior work. 

Margolis and Walsh (2003) was perhaps the earliest major review article in this space, 
providing an extensive literature review of 127 papers over the period 1972-2002. They look for 
evidence about the overall effect of corporate social performance (CSP) on firms’ financial 

performance. The results of the literature are inconclusive since half of the evaluated studies 
provide evidence of a positive statistical significant effect of CSP on firm financial performance 
levels.2 These general conclusions, namely that the relation between CSR and firm performance 
is sometimes positive, and sometimes negative also survives (i) among more recent literature and 
(ii) when scrutinized using more systematic literature analysis tools as can be observed from the 
meta-analytic review conducted more recently by Wang et al. (2016). Taking some specific 
examples,  Shen  and  Chang  (2009)  verify  a  positive  effect  of  CSR  on  firms’  financial 
2 Similarly, Mahoney and Roberts (2007) in their literature review argue that CSR practices can have an either 
positive, negative or neutral unidirectional effect on firms’ financial performance. 



7  

performance. Gillan et al., (2010) provide evidence of a positive effect of ESG score on firms’ 

value over the period 1992-2007. Ameer and Othman (2012) suggest that those companies that 
apply sustainability policies have increased financial performance. Interestingly, Becchetti et al. 
(2012) provide evidence of negative abnormal returns around the dates in which CSR related 
events had occur. Similar results have also been found by Doh et al. (2010), who argue that CSR 
activities do not generate positive market reactions. 

However, Wu and Shen (2013) show, in their study that examines data covering the 
period 2003-2009, that there is a positive influence of firms’ CSR policies on their ROE (return 

on equity) and ROA (return on assets) levels. Kemper et al. (2013) furthermore provide evidence 
of a positive effect of CSR activities on the impact of firms’ marketing strategies. In contrast Di 

Giuli and Kostovetsky (2014), who use a data sample from 2003 to 2009, show a negative 
correlation between CSR and firms’ operating performance. Fatemi et al. (2015) present similar 
findings that are derived by using simulation analyses. They suggest that firms’ CSR practices 

have a positive effect on firms’ value creation. Saeidi et al. (2015) who examines 205 Iranian 

manufacturing and consumer product firms suggest that the positive effect of CSR policies on 
firms’ performance levels is direct through the generation of competitive advantages, increased 

firm reputation, and customer satisfaction. In addition, Wang et al. (2015) examine a sample of 
Taiwanese high-tech companies over the period 2010–2013. They use a quantile regression and 
structural equation models. The results indicate a significant positive effect of brand equity and 
CSR on firms’ performance levels. These results, however, contradict with Jain et al.’s (2016) 

findings. They argue that firms’ higher performance levels are associated with lower values of 

the composite ESG scores. Brower et al. (2017) then suggest that firms’ prior CSP reputation has 

influence not only on future firms’ social but also financial performance. Price and Sun (2017), 



8  

who study relations using a sample of US firms point out that firms which are not highly  
engaged on both CSR and corporate social irresponsibility (CSI) activities, are the ones which 
perform better. CSR and CSI are essentially opposing forces, one working to enhance corporate 
reputation, and the other resulting in reputation losses. It is difficult to say which has a stronger 
effect, though Price and Sun (2017) argue that the negative effects of CSI are more enduring. 
The implications of this are that firms not only have an expectation (from their stakeholders) to 
engage in CSR activities, but moreover that there are potentially material consequences lapses in 
corporate social responsibility performance. Finally, Bhardwaj et al. (2018) provide evidence 
that the firms’ which pursue first on CSR activities are more advantageous in comparison with 

their competitors, which are not investing on such activities i.e. that there is evidence of a first- 
mover advantage to CSR adoption. The discussion in this section, to this point, does not lend 
itself to an emphatic conclusion concerning whether CSR benefits firms or otherwise. It is at this 
point useful to bring attention to concerns of measurement. 

   2.2 Measuring the CSR – Firm Performance Relationship  While there are some obvious differences that will arise as a result of empirical sample 
structures (dates, country, and firm coverage etc.) as well as differing choices of empirical 
methodology, there is an additional, and intuitively unavoidable, measurement issue that must be 
acknowledged when engaging in CSR research. Put simply, there is no standard way to measure 
the performance of CSR activities e.g. CSR performance, or more compactly CSP. Measures of 
CSP serve as a proxy for CSR activity by reflecting the level and extent of efforts by firms in 
CSR activities. Early research in this area, for example Sonnenfeld (1982) recognized that 
‘corporate social audits’ embedded a high degree of diversity in their reported content, in terms 



9  

of what was reported and what stakeholders hoped to see. Different stakeholders wanted 
different information from each other, and often also different information from that which the 
firm wanted to present. The diversity of information gives rise to an immediate problem of lack 
of comparability, but is difficult to avoid as stressed by Griffin and Mahon (1997) who 
summarized the incomparability of 25 years of study on CSP.3 The problem remains today as 
highlighted by the more recent review by Wood (2010). Even when comparable/consistent 
measures are provided, this is not guaranteed to eliminate all measurement concerns either. 
Sharfman (1996) for instance explored ‘the construct validity of the Kinder, Lydenberg & 
Domini social performance ratings data’ and (among other things) reminded us that there is no 

single best way to measure CSP. Notwithstanding these points however, for some time there has 
been a growing consensus that CSP can be found to correlate closely with firm performance 
metrics, even in the presence of CSP measurement issues, see for instance Orlitzky et al (2003). 

   2.3 The Link between CSR and Innovation Capacity  2.3.1 CSR, R&D expenditure and innovation  A separate strand of the literature that is closely related to our own work is concerned 
with the interlinkages of environmental regulation, CSR and innovation or in the establishment 
of an empirical link among CSR and R&D expenditure. One of the first studies was presented by 
Jaffe and Palmer (1997). They provide evidence of significant positive effects of environmental 
compliance expenditures on R&D expenditure for regulated industries.4 Similarly, McWilliams 

 
3 Similarly, Waddock and Graves (1997) demonstrate that the conflicting results presented in the related literature can be rather attributed (among other factors) to the different CSR measures that are adopted. 4 It should be noted that Rennings and Rammer (2011) examine firms in Germany and could not provide evidence of a positive impact of environmental regulation on firms’ innovation capacity. These results may reflect the inherently 



10  

and Siegel (2000) provide empirical evidence for a positive correlation among CSR and R&D 
investment. 

This finding also aligns with Porter’s view (Porter 1991; Porter & van der Linde, 1995). 

Porter and colleagues (Porter, 1991; Porter & van der Linde 1995) suggested that environmental 
regulation has a positive effect on firms’ performance levels through the enhancement of 

innovation and competitiveness. More recently, Porter & Kramer (2007, 2011) advocate that 
CSR can be a potent source of innovation and competitive advantage and have refined these 
ideas in the notion of shared-value creation. More recently, Saedi et al. (2015) offer evidence that 
link competitive advantage and financial performance to CSR engagement for the businesses 
they surveyed. 

Hull and Rothenberg (2008) suggest that firms’ involvement in CSR activities affect 

positively their performance through the adoption of innovation related processes. In particular 
they argue that CSR activities enhance firms’ innovation capacity, from which they increase their 

ability to differentiate and gain competitive advantages (Hart, 1995; McGahan and Porter, 1997; 
Russo and Fouts, 1997). This is supported also by Bocquet et al. (2013) who find that firms 
engaging in CSR activities are more likely to be innovative in terms of their processes and 
generation of products. 

Following a stakeholder perspective, authors, such as Costa el. (2015) and Herrera 
(2015), suggest that management is better placed to identify and respond to strategic 
opportunities and challenges thanks to them engaging in CSR and continually evaluating 
corporate influences and relationships with stakeholders and the environment. Stakeholder and 

 
complex relationship between regulation and innovation capacity. Lioui and Sharma (2012) support the view that 
there is an indirect effect of CSR activities on firm performance through the reinforcement of firms’ R&D activities.  



11  

macro-environment assessments are seen as critical for successful innovation and achieving 
competitive advantage (Ferauge, 2013; Herrera, 2007; Li & Liu, 2014). As Herrera (2015)  
argues drawing on case studies at 3M and Intel, implementing CSR and institutionalizing social 
innovation can allow firms to secure a competitive advantage while improving corporate social 
and financial performance. Other recent work offers further support for this notion.5 

The studies presented so far have in common that they link externally focused activities 
(R&D, CSR, stakeholder and macro-environment assessments) to innovation capacity and/or 
financial performance. Padgett and Galan (2010) emphasize the fact that both R&D investments 
and CSR policies are based on firms’ ability to possess intangible resources which they link to 

firms’ competitive advantage. Studying technology exporters, Costa et al. (2015) find that there 

is a positive relationship between CSR and exploratory (rather than exploitative) innovation. 
Their results show that CSR can be used to develop innovation competencies that are new to the 
firm. Interestingly, they also found that, at least in the in the sector they studied, implementing 
CSR principles is not associated with what they termed ‘exploitative’ innovation, i.e., fostering 

efficiency, cost reduction, or superior delivery of customers’ needs in international markets 

(exploitation). In the authors’ view, CSR principles may be more associated with process than 
product innovation. The observed divergence may also help explain the more divergent findings 
in previous research. 

   2.3.2 Capturing innovation capacity    
5 For instance, Reverte et al. (2016) for the case of Spain using a sample of 133 eco-responsible firms provide evidence that the adopted CSR policies had a direct effect on their performance and innovation capacity. Ueki et al. (2016) using questionnaire survey data on Thai trucking firms provide empirical evidence of a positive significant 
effect of firms’ CSR activities on innovation. 



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Innovation capacity can be defined in many ways and at various levels. According to 
Szeto (2000), innovation capacity is “a continuous improvement of the overall capability of firms 
to generate innovation for developing new products to meet market needs” (Szeto, 2000, p. 

150).6 Irrespective of level (micro, meso or macro), often innovation capacity is operationalized 
via proxy measures that are focused on technological change, i.e., R&D expenditures,  
technology output, such as patents or patent applications. At the aggregated level, Furman et al. 
(2002) provide evidence that innovation capacity is associated with total factor productivity 
growth. Similarly, Hamidi et al. (2018) suggest that innovation capacity refers to productivity 
growth or other innovative processes, products and/or services. Halkos & Skouloudis (2018) 
offer an overview of macro-level indicators that underline this.7 Acknowledging that, also at 
firm-level, innovation capacity is often associated with formal R&D activities and innovation 
output with new products (e.g., Kirner et al., 2009), Forsman (2011) argues that innovation 
cannot be created simply by R&D investment, but instead emerges from a complex process of 
6 Forsman (2011) highlights an important distinction in her work by exploring the difference between innovation capacity and capability. Pointing to Amit and Schoemaker (1993) who define resources as stocks of available factors that are owned or controlled by an organization, she defines capabilities as the capacity to deploy the resources of an organization. As a result, the innovation capabilities have an impact on innovation capacity.  7 Hakos and Skouloudis (2018, 294-295) compiled an overview of composite measures of innovation capability: (a) the World Economic Forum (WEF) macro-level innovation potential is assessed through a composite measure examining: (i) Capacity for innovation, describing how companies in a country obtain technology; i.e. by licensing or imitating foreign companies vs. by conducting formal R&D and new product development; (ii) Quality of scientific research institutions; (iii) company R&D spending; (iv) University-industry collaboration in R&D; (v) government procurement of advanced technology products; (vi) availability of scientists and engineers; (vii) patent applications per million population. (b) INSEAD's Global Innovation Index (GII) captures enabling conditions facilitating innovation and the innovation outcomes: (i) Institutions, via attraction of business opportunities, fostering growth through good governance, adequate protection and incentives for innovation; (ii) human capital, i.e. the level and standard of  education and research activity; (iii) infrastructure, esp. information and communication technologies, energy supply and dissemination-quality of general infrastructure; (iv) market sophistication, i.e., access to credit, investment funds and international markets; (v) business sophistication, via the employment of highly qualified professionals- technicians; (vi) knowledge and technology outputs, i.e., patent applications, utility model applications as well as scientific and technical articles; (vii) creative outputs, including creative intangibles. (c) the Innovation Capacity Index (ICI), proposed by the European Business School, covers five variables: (i) Institutional environment, i.e., good governance and effective country policy assessment; (ii) human capital training and social inclusion, i.e., level of education system as well as social inclusion and equity policies; (iii) regulatory and legal framework facilitating doing business; (iv) R&D, assessing available infrastructure and workforce, plus registered patents, trademark applications and royalty-license fees; (v) adoption and use of information and communication technologies. 



13  

business development and the optimization of firms’ production processes. Several studies (e.g., 

de Jong and Marsili 2006; Hirsch-Kreinsen, 2008; Santamaría et al., 2009) link innovation 
capacity also to experimentation, learning, technological catch-up which is not necessarily linked 
to the engagement in formal R&D activities. We recognize the point these authors make and 
acknowledge that future research will need to explore new ways of capturing innovation 
capacity. 

For the purpose of this study we follow Shao and Lin (2016) in suggesting that firms’ 

technological change levels (i.e. movements of firms’ technological production frontier) reveal 
firms’ innovation capacity levels. We contend that improvements of firm’s technological levels 

can signify also higher innovation capacity levels. Boly et al. (2014) show that innovation 
capacity can be described by a three-stage innovation process containing resources, activities and 
innovation outcome. 

Furthermore, in an extensive review of the literature Boly et al. (2014) conclude that 
innovation capacity among other factors interrelates to firms’ ability to adopt and foresee 
technological changes. Firms’ ability to utilize effectively their resources has been proven to be 

an incremental factor within firms’ innovation capacity processes (Yam et al., 2004; Guan et al., 

2006). Therefore, we contend that firms’ ability to increase their technological change levels is a 
reasonable proxy for their innovation capacity (Furman et al., 2002; Shao and Lin, 2016; Hamidi 
et al., 2018). 

   2.3.3 CSR and non-technological innovation 



14  

Much of the focus of innovation literature is on technological innovation, captured 
usually through a variety of R&D related outcomes. However, there are many more nuanced 
innovation outcomes, non-technological in nature, which can underpin firm performance. We 
develop the case here that CSR related initiatives may contribute to such non-technological 
innovation processes. 

At the firm level, there is an interwoven nexus spanning exploration and exploitation that 
can be levered to promote the supply, demand and spatial search for innovation by employees 
(Sidhu et al., 2007). In a somewhat nuanced strand of the literature, it has been demonstrated that 
innovation can be fostered through non-technological developments (social innovation) as well, 
in some cases more impactful benefits than from pure technological change. Research on 
absorptive capacity and ambidexterity also demonstrates the importance of social and 
organizational factors in fostering value-enhancing exploratory innovation, see for example 
Jansen et al. (2005, 2006). Bridging the connection to our own work, CSR may be taken as an 
antecedent which gives rise to heightened levels of organizational ambidexterity thereby 
fostering both exploitative and explorative innovation. Management innovation, which may 
result from enhanced CSR activities, can improve the underlying conditions for innovation 
within a firm as emphasized by Volberda et al. (2013) and Walker et al. (2015). While in this 
paper we do not go as far as disentangling the unique effects of individual CSR dimensions to 
innovation capacity, this additional strand of literature helps to reinforce our underlying 
hypothesis that enhanced CSP creates the potential, likelihood even, for superior innovation 
capacity. 

   3. Data Description and Methodological framework 



15  

3.1 Data description  In our analysis, we use a sample of 320 Japanese firms over the period 2008-2016 that  
are collected from Bloomberg.8 The reason why we have used the particular sample is twofold. 
Firstly, by choosing firms from a single country we are able to eliminate any institutional 
differences which appear in cross country studies and can mask over the estimated CSR effect 
(Cai et al. 2016). Moreover, we follow Reverte et al. (2016) and we choose a sample of eco- 
responsible firms’. In the Bloomberg database these firms are those which report their CO2 
emissions (among other pollutants) and provide unique access to firm level emissions data over 
several years. As a proxy of firms’ corporate social responsibility (CSR), we use a third-party 
rating calculation (ESG Disclosure Score) developed by Bloomberg.9 Husted and de Sousa-Filho 
(2018) show that the Bloomberg ESG measures provide consistent and comprehensive estimates 
of CSR practices that minimize the potential error in measurement. This ESG score evaluates the 
quality and comprehensiveness of firms’ Environmental and Social Governance disclosure and 

reporting activities and is based on several quantitative and policy-related measures. The ESG 
scores range from 0.1 for firms that disclose a minimum amount of ESG data to 100 for those 
that disclose every data point collected by Bloomberg, where each data-point reflects a type of 
CSR activity and with some mediation by Bloomberg to account for the quality of reporting. As 
a result, the higher the ESG score the higher the firms’ involvement in CSR activities. For the 
estimation of the firms’ production function, we use as inputs firms’ total employee numbers and 

 
8 We thank an anonymous referee for reminding us that the decision to disclose CSR activities may not be perfectly correlated with actual CSR activities. There is a possible scenario in which firms engage in CSR activities but never report them. We acknowledge this possibility, but within the scope of our data we are unable to explore this possibility more carefully. We note however that our sample is restricted to reporting firms only, limiting the influence to our analysis. 9 As discussed in the literature review, we recognize that this is not the only possible measure for CSP, but it has the distinct advantage of being consistently measured, which is an important pre-requisite for comparability. While different indicators might give rise to quantitative differences, we do not anticipate they would result in any qualitative challenge to our main findings. 



16  

  

total assets, while total revenues are used as the only output.10 Table 1 presents diachronically  
the descriptive statistics of the variables used in our analysis. It is evident that, over the years, 
firms’ ESG scores are varying (in average terms) from 35 to 40 suggesting a medium ESG 
compliance. 

  Table 1 about here   
3.2 Methodological approach  In this section we outline our methodology, which is adapted from the framework 
outlined by Daraio and Simar (2005, 2006, 2007a, 2007b). Let and denote the 
input and output vectors relating to a firms’ production process. Given these, the ‘attainable’ set 

can be characterized as: 
                                   , (1)  and the output correspondence set as: 
P(x) = y  Rq ( x, y ) 

+ . (2)  
Following the influential works by Debreu (1951), Farrell (1957) and Shephard (1970) the 
efficient boundaries (or the isoquants) can be defined in radial terms as: 
P ( x) = y y  P ( x),  y  P ( x),   1, (3) 
whereas the output measure of efficiency for a firm operating at level can be defined as:11     
10 Even though we have a relatively large sample 2880 observations we estimate a simple production function and in order to minimize the well-known dimensionality problem (Dyson et al., 2001 ; Wilson, 2018). 11Assuming free disposability and convexity of the production set (see, Shephard 1970) the measurement of efficiency can be obtained by linear programming estimators, known as data envelopment analysis-DEA estimators (Charnes et al. 1978). 



17  

 ( x, y ) = sup  y  P ( x) = sup ( x,  y ) . (4) 
Building on the work by Cazals et al. (2002), Daraio and Simar (2005) proposed a probabilistic 
approach towards efficiency measurement that is based on the probability             of dominating 
a firm operating at level ........... The probability, , can be expressed as: 
H ( x, y) = Prob( X  x,Y  y) , (5)  

which then can be decomposed to:  
H ( x, y ) = Prob(Y  y X  x)Prob( X  x) = SY X ( y x) Fx ( x ). 

   (6)  
Then the output-oriented efficiency measure in eq. (4) can be presented as: 
 ( x, y) = sup SY X ( y x)  0 

 

 
 (7)  

Furthermore, let     to be the vector of a factor  influencing firms’ technological change  levels. 

In our case this factor is the ESG criterion, which is adopted by firm decision makers. 
Then the conditional distribution of given can be defined as: 
H (x, y z ) = Prob(Y  y X  x, Z = z )Prob( X  x Z = z ) 

= SY X ,Z ( y x, z ) FX Z (x z ), 

    (8) 
  where SY X ,Z ( y x, z ) = H (x, y z ) / H (x, 0 z ) . Then the conditional output measure of efficiency 
 

facing the effect of can be defined as:12 
 ( x, y z ) = sup  0 ( x,  y )  z  

= sup  0 SY X ,Z ( y X  x, Z = z )  0. 

     (9)  
Mastromarco and Simar (2015) suggest that in order to account for dynamic effects and thus to 
create time-dependent conditional efficiency measure, we also must add time, , as an extra 

 

12For the asymptotic properties of conditional measures see the study by Jeong et al. (2010).  



18  

t t 

Y X ,Z 

conditional variable. Then the conditional attainable set can be re-defined as 
 

z = ( x, y) Z = z,T = t, x can produce y   z     p+q t , where t t + and its distribution can be  
characterized as:  
                                              (10)  

( x, y)
z 

Thus, a firm’s conditional output-oriented efficiency measure at level  t facing the effect of ESG criteria z can be defined as:13 
 ( x, y z ) = sup  0 ( x,  y )  z  

t at time 

= sup  0 St ( y X  x, Z = z,T = t )  0.  (11)  
where  t Y X ,Z 

 
( y x, z ) = Prob(Y  y X  x, Z = z,T = t ) 

 

 

. (12)  
The estimation of firms’ efficiency measures is obtained using data envelopment analysis 

(DEA) estimators.14 Moreover, in order to take account of firms’ scale effects in our efficiency 

estimations, we impose the assumption of variable returns to scale (VRS). To construct 
conditional estimates, we need to apply a localizing procedure that determines the data points in 
a neighborhood of 15 

In order to graphically represent the effect of ESG criteria adoption on firms’ innovation 

capacity (technological change), we follow Bădin et al. (2012, 2014). We construct the following 
ratio which will act as the dependent variable in our nonparametric regression analysis: 

 
13Based on Simar and Wilson (2007, 2011) and Daraio et al. (2018), in our estimation we assume that the separability assumption does not hold. 14The calculation of the full and partial frontiers (both for the conditional and the unconditional measures) several 
LP programs were carried out using ‘FEAR’ which is an integrated program in ‘R’ language (Wilson 2008). 15This requires smoothing through appropriate bandwidths using Kernels with compact support. See the study by 
Bădin et al. (2010) for computational issues regarding the choice of the optimal bandwidth based on least squares cross-validation (LSCV) criterion (Hall et al. 2004; Li and Racine 2004, 2007). 

S 



19  

  
                

          . (13) 
       

 

A next step of our analysis is the following nonparametric regression model:  
yi  = g (xi ) + ui , i =1, 2,..., n, 

  
(14)   Where the dependent variable yi represent the ratio defined in (13) and xi 

 (the  
independent variables) represent the ESG criteria and time.16 In expression  (14),  is  the 
unknown smooth function and is interpreted as the conditional mean of given  (see Li and  
Racine 2007, Theorem 2.1, p. 59). Then, as described by Li and Racine (2007), we can estimate 
the unknown function using a local linear estimator, since it minimizes the bias when estimating 
a regression function near the boundary of support. In our analysis we use product kernels and 
bandwidths based on Least Squares Cross-Validation (LSCV) criterion as described, for 
example, by Hall et al. 2004; Li and Racine, 2007. By applying the nonparametric regressions, 
we can determine the effect of time and ESG criteria on firms’ technological change levels (i.e. 

on its innovation capacity).17 As suggested by Bădin et al. (2012), an increasing regression line 

indicates a positive effect on firms’ technological change (shift on the frontier) whereas, the 

opposite phenomenon indicates an unfavorable effect. In the output-oriented case when the 
impact of the exogenous factors (time and ESG criteria) is positive, then, these factors act as 
extra inputs which are ‘freely available’, whereas, in the opposite case they act as ‘compulsory’ 

or ‘unavoidable’ output on firms’ estimated production process (Bădin et al. 2014, pp. 15-16). 
  4. Empirical findings     

16The 'time' variable is discrete; therefore, discrete kernels have been used following Mastromarco and Simar (2015). 17
According to Shao and Lin (2016) the estimated technological change reveals firms’ innovation capacity levels. 



20  

Firstly, we analyze diachronically the distributions of firms’ estimated efficiency levels 

obtained from both the conditional and unconditional cases. Figure 1 presents kernel density 
plots displaying the original (unconditional) estimates of firms’ efficiency with black lines, 

whereas the conditional estimates are presented in red. Additionally, two vertical lines are used 
to indicate the mean values of the estimated efficiency scores with respect to each of the 
distributions. It should be noted that the plotted efficiency estimates, both for original and 
conditional cases, are defined and presented in terms of Shephard-type efficiency measures 
taking values between 0 (fully inefficient firms) to 1 (fully efficient firms). Subfigure 1a contains 
the efficiency distributions for all years, while 1b through 1j plot the efficiency scores for 
individual years. For both the unconditional and conditional measures we observe bimodality 
with a cluster of firms concentrated around 1 and a second larger mass centered just below 0.5. 
This pattern persists for all the years in our analysis, therefore the mean of the estimated 
efficiencies (for both the original and the conditional estimates) is also stable over the examined 
periods. It is particularly noteworthy that when taking into consideration the potential effect of 
ESG criteria (conditional estimates), the distributions of the estimated efficiencies became more 
platykurtic. It appears under the effect of the ESG criteria adoption firms’ efficiency levels have 

a lower likelihood of taking values less than 0.5, and a higher likelihood of taking efficiency 
scores closer to 1 – this also reflects in consistently higher mean efficiency scores. 

Figure 1 about here  In line with our above finding of overall diachronic stability among the estimated 
efficiency measures, Table 2 deepens our analysis and presents sector specific summaries of the 
conditional efficiency estimates, concentrating on the full sample. The evidence suggests that, on 
a per sector basis, the deviations in efficiency scores are similar across the sectors, with an 



21  

overall standard deviation ranging from 0.23 to 0.28 (with the exception of the Utilities sector). 
The sectors with higher performance under the effect of ESG criteria adoption are ‘Utilities’ and 

‘Energy’ with mean efficiency scores of 0.820 and 0.950 respectively, whereas the sector with 

the lowest performance is the ‘Healthcare’ sector with a mean efficiency score of 0.466. All the 
other sectors at least on average terms have similar efficiencies reported around 0.5. The 
similarity of the conditional estimates across the sectors suggests that (on average) the benefits 
emerging from ESG criteria adoption are not uniquely available/biased to any given sector, i.e. 
that all sectors can in principle derive benefits of similar magnitude. 

Table 2 about here  
The overall effect of ESG criteria to firms’ innovation capacity (i.e. technological 

change) is graphically examined in Figure 2. Subfigure 2a examines the effect for the entire 
sample, while 2b through 2k present sector specific results. In these plots the vertical axis 
represents the ‘               ’ ratio while the other two axes represent the ESG score and time (i.e. 

the years of our study), therefore these plots illustrate how time and ESG scores align with 
efficiency. It is evident that as the ESG adoption criteria imparts a positive effect to firms’ 

innovation capacity, further confirming that that ESG practices enhance firms’ ability to 

innovate. However, this innovation capacity enhancement increases up to a certain level after 
which diminishing returns appear. It is therefore evident that we have a nonlinear behavior of 
ESG policies on firms’ innovation capacity, which incidentally provides some implicit validation 
that the flexible nonparametric methodology we adopt is suitable for the analysis. As a 
generalization of our findings we can infer that CSR engagement stimulates innovation but up to 
a certain threshold after which over adoption causes the effects of socially responsible business 
practice may become negative to the process innovation capacity enhancement. In order to check 



22  

of the robustness of this finding we perform our analysis separately for each of the industrial 
sectors contained in our data sample. The results are, in the majority of cases, robust (refer to 
subfigures 2b, 2c, 2d, 2g, 2h and 2i) signifying in several cases an inverted ‘U’-shape relation 
albeit with some variation in the strength of inflection. However, for the cases of the ‘Energy’ 

(subfigure 2e), ‘Financials’ (subfigure 2f), ‘Technology (subfigure 2j) and ‘Utilities’ (subfigure 

2k) sectors, the effect of ESG policy adoption is more consistently positive across all levels of 
ESG adoption, as indicated by an increasing nonparametric regression line and the absence of a 
clear inverted ‘U’-shape profile. Notwithstanding the absence of clear inverted ‘U’-shapes for 
these sectors, the nonparametric regression surfaces uncover clear non-linearities and patterns 
that would not otherwise emerge using simple linear regressions. 

In summary, we find empirical evidence of a positive influence of CSR to innovation 
which is robust to industry and time. In an effort to pull out the contributions as they relate to 
specific bodies of literature we note first that our work supports several previous studies in the 
literature spanning innovation and CSR (McWilliams and Siegel, 2000; Hull and Rothenberg, 
2008; Bocquet et al. 2013; Reverte et al. 2016; Ueki et al., 2016). However none of these studies, 
or others that we are aware of within the wider literature on CSR/ESG performance, reported a 
possible nonlinear association between CSR and innovation capacity. Thus, we feel our analysis 
provides a unique contribution salient to two core strands of literature spanning innovation, and 
CSR/ESG performance. Finally, we note the fact that our primary findings point towards an 
inverted ‘U’-shape profile containing decreasing returns of ESG policies to firms’ innovation 

once some threshold level of CSR engagement has been reached, allows us also to reconcile our 
analysis against studies arguing negative returns to firms’ CSR engagement activities (Margolis 

and Walsh, 2003; Wang et al. 2016). 



23  

  
Figure 2 about here     

5. Discussion and Conclusions  5.1 The CSR-Innovation Capacity Relationship: a Case of Indirect Value-Creation  
The effect of CSR practices on firms’ performance is an ongoing research priority for 

scholars of business and related disciplines. The “doing well by doing good” hypothesis 

(Bénabou and Tirole, 2010; Krüger, 2015) under which the engagement in CSR activities will 
result on maximizing both firms’ performance and shareholders value, has been often debated 

and the empirical findings over the years provide contradictory results (see Cheng et al. 2013; 
Deng et al., 2013; Ferrell et al. 2016; Wang et al. 2016). However, the opposing results can be 
attributed at least in part to the fact that CSR indirectly affects firms’ value and performance, 
through its positive influence on firms’ ability to engage in innovation creating activities. This is 

to say that once a more nuanced view is taken as to how CSR activities can stimulate value 
enhancing business activity through indirect channels such as innovation, the scales are more 
favorably tilted towards a positive impact from CSR. 

Our study provides new evidence of the existence of an indirect link. By using 
nonparametric frontier analysis our paper models explicitly, the effect of ESG policies adoption 
on the estimation of firms’ production efficiency levels. Specifically, through the application of 

the recently developed probabilistic approach to efficiency analysis (Bădin et al., 2012. 2014, 

Mastromarco and Simar, 2015; Daraio et al., 2018), we could examine firms’ production 

efficiency measurement and the effect of ESG choices on firms’ innovation capacity levels. We 



24  

confirm of the existence of a nonlinear effect of ESG adoption policies on firms’ innovation 

capacity. 
Our findings provide empirical evidence of the indirect value-creation hypothesis (Lioui 

and Sharma, 2012) under which the engagement on CSR/ESG activities influence firms’ 

performance. We provide convincing evidence that firms’ engagement on ESG activities 
catalyzes firms’ mechanisms for innovation capacity creation, which in turn reflects on their 

performance levels. This path of value creation implies that we have a potential delay and/or 
indirect effect of the CSR/ESG impact to firms’ performance levels since the CSR activity 
stimulates a sequence of events that eventually give rise to the indirect benefit, and which 
justifies conflicting empirical findings reported in the related literature (Deng et al., 2013; Ferrell 
et al., 2016; Wang et al., 2016). Our findings further show that the engagement on ESG activities 
enable firms to utilize successful innovation processes (Wang et al., 2008), which initiate R&D 
related activities (Reverte et al., 2016; Ueki et al., 2016) and greater innovation intensity. As a 
result, this phenomenon reflects on firms’ innovation capacity levels and then on firms’ 

technological change levels. Finally, our findings align with those by Hull and Rothenberg 
(2008) by suggesting that the positive effect of CSR/ESG activities on firms’ performance levels 

is moderated by firms’ innovation capability levels. In fact, this adjustment effect attributed to 

different levels of firms’ innovation capability is evident from the observed nonlinear behavior  

of ESG impact on firms’ technological levels. 
   5.2 Nonlinearity of the Relationship and Sectoral Differences 



25  

Overall our results further suggest that there is a positive effect on innovation capacity as 
firms’ compliance with ESG criteria increases. However, there is critical cut-off ESG level after 
which the effect becomes negative. We deploy a robustness check on our main results by 
performing our analysis separately for different sectors. The results, in the majority of cases, 
verify the presence of an inverted “U”-shape profile, suggesting a nonlinear relationship. 

Nevertheless, in some instances and especially for the sectors of: Energy, Financials, 
Technology and Utilities, the effect is positive characterized by a nonparametric (curvilinear) 
increasing line. Specifically, as firms’ ESG criteria compliance increases, the positive effect to 

firms’ innovation capacity also increases but in a nonlinear manner. Our results provide support 
that the nonlinear effect on the examined sectors is consistent with unique sectoral 
characteristics. Such an outcome is to be expected especially when the effect of CSR/ESG 
policies is evaluated among industries. Beschorner and Hajduk (2017) provide convincing 
evidence that the industry-specific characteristics shape the CSR/ESG practices and the interlink 
among the different ‘actors’ involvements. This is referred to as ‘industry-specific corporate 
social responsibility’ (Beschorner and Hajduk, 2017; p.635). Matten and Crane (2005) and 

Kinderman (2011) argue that the different institutional arrangements and different business 
systems among the sectors have a different impact on CSR/ESG opportunities and practices. 
Thus, the effect on firms’ performances is also expected to vary. Jain et al. (2017) argue that 

specific industries’ institutional dynamics initiate different stakeholders’ orientations across 

firms in different industries. As a result, industry-specific characteristics that include competitive 
environment, nature of product/services, negative externalities, and social activism among others 
will give rise to difference influences over the potential benefits from CSR/ESG practices. 
Carrigan et al. (2017) suggest that to avoid such diverse effects and practices among industries, 



26  

policy makers are advised to pursue initiatives to all the “actors” that are engaged to co- 
transform their business model and enhance multi-stakeholder involvements alongside industries 
and business of different size and operations. Carrigan et al. (2017) further argue that such 
solutions can only be obtained through the redesign of national and international legislative 
frameworks. Therefore future research should be directed on the examination of potential unified 
industry interrelated CSR adoption frameworks, alongside with simulated potential effects on the 
performance both on industry and firm level. Though the complexity of developing such 
frameworks is formidable (Hess, 2001), and requires a concerted effort. 

Our finding supports those studies providing empirical evidence of a positive effect of 
firms’ CSR on their R&D activities (McWilliams and Siegel, 2000; Hull and Rothenberg, 2008; 
Padgett and Galan, 2010; Lioui and Sharma, 2012; Bocquet et al., 2013; Reverte et al., 2016; 
Ueki et al., 2016). Finally, by using a production efficiency framework, our findings further 
show that the revealed positive effect of ESG policies on firms’ innovation capacity (movements 
of the estimated frontier) translates also to a positive effect on firms’ operational performance 

supporting (at least for some cases) i.e. the “doing well by doing good” hypothesis is empirically 

supported. 
   5.3 Future research  With respect to future research directions, there are several ways in which our work 

might be extended. Disentangling the unique contributions of CSR activities to explorative and 
exploitative innovation seems to be a viable research objective. Within the performance 
benchmarking paradigm, it may be feasible to map exploitative innovation to efficiency scores 



27  

with respect to conditional frontiers, and explorative innovation to the technology gap implied by 
the distance between conditional frontiers. 

The dynamics of the CSR-innovation nexus may be of interest also. Tsekouras et al. 
(2016) examine, among other things, the absorptive capacity of firms within a dynamic modeling 
context, uncovering patterns of technological evolution and convergence. In a related literature, 
Makni et al. (2009) also consider dynamic patterns by exploiting dynamic causality tests. It 
seems intuitive that similar dynamics might also carry over to the processes of innovation 
capacity expansion and exploitation. Also, it remains important to better understand the 
measurement of corporate social performance and to further inquire into the potential influence 
to the multiple dimensions of firm performance and value creation. In this spirit additional 
insights into the relative importance of specific/individual types of CSR activities may uncover 
richer insights that can translate into more prescriptive managerial implications. Further 
explorations in this direction might also remain cognizant of opportunities to link specific CSR 
activities to specific stakeholder groups. 

In this contribution, we focused on the link between CSR and innovation capacity with 
the latter being viewed through the lens of technological change. While we follow Shao and Lin 
(2016) and their claim that technological change levels reveal firms' innovation capacity, we 
recognize this is a focus that is chosen. Innovation goes beyond technological change. As many 
authors, in particular Volberda and colleagues (e.g., Volberda et al, 2016; Jansen et al., 2005, 
2006) have highlighted, innovation results also from non-technological developments, which 
often can be more important than technological change. Extant work on absorptive capacity and 
ambidexterity illustrates the importance of social and organizational drivers of innovation. In 
many ways one can argue that engagement in CSR and innovation activities of various nature are 



28  

a sign of absorptive capacity (Cohen & Levinthal 1990, Zahra & George 2002, Todoreva & 
Durisin, 2007) that firms have developed. These broader inter-relationships, in particular links 
between CSP and different types of innovation remain a relevant topic to be explored in future 
research. 

Finally, our research examined the CSR/innovation capacity connection drawing on data 
on large firms. We know of some studies that explicitly address SMEs (e.g., Martinez-Conesa et 
al., 2017) or cover a sample of firms that includes mostly SMEs (e.g., Reverte et al., 2016). 
While these studies tend to concur with our observation of a positive effect of firms’ CSR on 

their R&D activities or innovation capacity, there is a need to further explore the CSR/ESG- 
innovation connection for firms in this category. In light of the nonlinear relationship we found 
for our sample of firms, there may be value in exploring also for SMEs if there is critical cut-off 
ESG level after which the innovation capacity effect becomes negative. 

  Acknowledgments   The authors wish to thank three anonymous referees for their helpful and stimulating feedback. 
Any remaining errors and inaccuracies are solely the responsibility of the authors. 

   
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45  

Table 1: Descriptive statistics of the variables used   Total Assets Number of Employees Total Revenues ESG Disclosure Score 
2008 Mean 25503.170 18640 7964.965 35.084  Std 153239.444 41797 18559.305 7.067 
2009 Mean 25355.665 18749 8172.855 35.849  Std 157169.533 41855 17815.059 6.634 
2010 Mean 27727.665 19197 8050.451 37.844  Std 171639.686 44026 17720.812 6.762 
2011 Mean 32088.886 19498 9074.516 38.622  Std 200624.985 44197 19844.122 6.861 
2012 Mean 33268.132 19473 9815.627 38.700  Std 210397.155 42877 21306.216 6.685 
2013 Mean 30933.789 19654 9624.169 39.179  Std 196623.916 42503 21808.299 6.897 
2014 Mean 30122.077 20001 8842.086 39.635  Std 191969.618 42836 20339.210 7.020 
2015 Mean 28353.931 20457 8394.349 40.113  Std 182649.193 43428 19475.312 7.072 
2016 Mean 30628.731 20704 7751.555 40.545 

 Std 200712.476 43580 18198.937 7.212 



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Figure 1: Density plots of firms’ estimated efficiency levels  
a 

 

  

b 
 

  

c 

d 
 

  

e 
 

  

f 
 

  g 
 

  

h 
 

  

i 
 

  
j 

 

  

  



47  

 
Table 2: Per industry conditional efficiencies estimates 

  INDUSTRY t ( x, y z )  INDUSTRY t ( x, y z ) 
COMMUNICATIONS mean 0.534 COMSUMER STAPLES mean 0.574  

 std 0.275  std 0.250  
CONSUMER DISCRETIONARY mean 0.577 ENERGY mean 0.820  

 std 0.251  std 0.224  
FINANCIALS mean 0.514 HEALTHCARE mean 0.466  

 std 0.248  std 0.249  
INDUSTRIALS mean 0.508 MATERIALS mean 0.428  

 std 0.251  std 0.234  
TECHNOLOGY mean 0.542 UTILITIES mean 0.950  

 std 0.284  std 0.114  



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Figure 2: The effect of ESG criteria on firms’ innovation capacity  a 
 

   

b 
 

   

c 
 

   d 
 

   

 

  e 

 

  f 
g 

 

   

h 
 

  

i 
 

   
j 

 

    

k