Accumulate or Diversify Ecological Innovation Assets? The Effect of Ecological Innovation Asset Depth and Breadth on Firm Financial Performance
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Gropengießer‐Arlt, Louisa; Zacharias, NicolasA. Article — Published Version Accumulate or Diversify Ecological Innovation Assets? The Effect of Ecological Innovation Asset Depth and Breadth on Firm Financial Performance Business Strategy and the Environment Provided in Cooperation with: John Wiley & Sons Suggested Citation: Gropengießer‐Arlt, Louisa; Zacharias, NicolasA. (2025) : Accumulate or Diversify Ecological Innovation Assets? The Effect of Ecological Innovation Asset Depth and Breadth on Firm Financial Performance, Business Strategy and the Environment, ISSN 1099-0836, Wiley, Hoboken, NJ, Vol. 34, Iss. 4, pp. 4001-4029, https://doi.org/10.1002/bse.4182 This Version is available at: https://hdl.handle.net/10419/323830 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/
Business Strategy and the Environment, 2025; 34:4001–4029 https://doi.org/10.1002/bse.4182 4001 Business Strategy and the Environment RESEARCH ARTICLE OPEN ACCESS Accumulate or Diversify Ecological Innovation Assets? The Effect of Ecological Innovation Asset Depth and Breadth on Firm Financial Performance LouisaGropengießerArlt | NicolasA.Zacharias Faculty of Economics and Business Administration, Friedrich Schiller University Jena, Jena, Germany Correspondence: Louisa GropengießerArlt ([email protected]) Received: 21 June 2024 | Revised: 21 January 2025 | Accepted: 22 January 2025 Keywords: asset breadth| asset depth| ecological innovation| firm financial performance| resourcebased view| technological portfolio ABSTRACT This study investigates the role of innovation portfolio structures in the relationship of ecological innovation and firm financial performance. We draw on the resourcebased view and the natural resource–based view to examine the effects of the depth and breadth of firms' ecological innovation assets (EIAs) while conceptually and empirically accounting for the substantial differences between two distinct firm financial performance dimensions. To test our conceptual framework, we rely on a panel data set based on 340 US firms listed in the S&P 500 index over a 10year period. Fixedeffects regressions confirm that EIA depth and EIA breadth fundamentally differ in their effects on firm financial performance. Moreover, we find that the results vary considerably between accountingbased and valuebased financial performance. Thereby, this study makes a significant contribution to the ongoing debate about the nexus of ecological innovation and firm financial performance. 1 | Introduction Recent studies show that ecological innovations, herein defined as “technological innovations or applications for mitigation or adaption against climate change” (EPO2024), are one of the most promising ways for firms to reduce their environmental impact (Liang, Zhang, and Qiang2022; Wang, Li, and Liao2021). However, they represent a risky investment for firms because of their specific and uncertain characteristics compared with regular innovations (Barbieri, Marzucchi, and Rizzo 2020; DeMarchi 2012; Rennings 2000). To stay competitive in the long term, firms need to develop innovations that are both ecological and profitable. Research is still inconclusive regarding the circumstances in which firms benefit most from ecological innovation (Hermundsdottir and Aspelund2021; López Pérez, García Sánchez, and Zafra Gómez2024). To innovate successfully, firms build up technological portfolios, in which they distribute innovation assets within or across different technology fields (Leten, Belderbos, and van Looy2007). Market and technology trends, as well as the increasing complexity of products and production processes, are forcing firms to expand their core technological competence and diversify into further technology fields (Leten, Belderbos, and van Looy2007; Lin, Chen, and Wu2006). At the same time, increasing evidence shows that building up deep innovation stocks (i.e., the accumulation of technological innovation assets within a single technology field) can enable firms to innovate more easily because a high depth of knowledge in a technology field facilitates the creation of new combinations of that knowledge (Prabhu, Chandy, and Ellis2005; Zhang and BadenFuller2010). Thus, managers must decide strategically about the distribution of technological innovation assets within and across technology fields to build an innovation portfolio that allows the firm to fully leverage This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd.
4002 Business Strategy and the Environment, 2025 its value and gain a competitive advantage (Barney1991; Lin, Chen, and Wu2006). Ecological innovations are highly complex (Barbieri, Marzucchi, and Rizzo 2020) and demand specific resources (Cainelli, de Marchi, and Grandinetti2015; DeMarchi2012); thus, managers must proceed with particular caution when deciding on the distribution of ecological innovation assets (EIAs) to exploit their full value. The literature on performance outcomes of ecological innovation has widely neglected a detailed analysis of the structure of firms' EIAs within their innovation portfolios. To address this research gap, we strive to answer the following research question: How does the structure of EIAs influence firms' accountingand valuebased financial performance? In particular, we investigate the effects of EIA depth and EIA breadth on accountingbased performance (ABP) and valuebased performance (VBP) outcomes. To develop our research framework, we draw on insights from the ecological innovation and technological portfolio literature streams and merge those arguments under the resourcebased view (RBV; Barney 1991) and its extension, the natural resource–based view (NRBV; Hart 1995; Hart and Dowell2011). To test our hypothesized relationships, we rely on patent data from the US Patent and Trademark Office (USPTO) for 340 US firms listed in the S&P 500 index over a period of 10 years (2009–2018), resulting in over 3000 firmyear observations. In total, our analysis includes more than 54,000 ecological patents. The results of our study provide relevant contributions for research and practice alike. First, by transferring insights from the literature on technological portfolios to the ecological innovation and firm performance research stream, we shed light on the ongoing debate about the effect of ecological innovation on firm performance (Hermundsdottir and Aspelund2021; López Pérez, García Sánchez, and Zafra Gómez 2024). We expand the existing body of knowledge by examining the nexus of EIA portfolio structures and firm financial performance from both the RBV and NRBV. Using these theoretical lenses, we provide a more finegrained explanation of the effects of different EIA portfolio structures. Building on extant research that provides initial insights into the effects of EIA breadth (La Leyvade Hiz, FerronVilchez, and AragonCorrea2019), we expand the state of knowledge by including further portfolio structures, such as EIA depth and the interplay of EIA depth and EIA breadth. These differentiated insights into the effects of EIA portfolio structures can support managers in deciding how to distribute EIAs within innovation portfolios to best profit from these innovations. Second, when testing the effects of EIA portfolio structures on ABP and VBP, we account for the distinct dimensions of firm financial performance identified in our conceptual reasoning, predicting different effects for the outcomes. This is important because they represent related but different dimensions of firm performance (Gentry and Shen2010; Hoskisson, Johnson, and Moesel1994; Keats1988): VBP reflects a longterm, futureand marketoriented measure in which investors' perceptions are considered, whereas ABP reflects a more shortterm, pastand operationoriented measure (Gentry and Shen2010; Keats1988). In the context of research on corporate sustainability, Grewatsch and Kleindienst(2017) note that studies on ecological innovation and firm performance have mostly used either only one dimension of firm performance or both without sufficiently recognizing the differences, leading to inconclusive results. We address this shortcoming, and our results show essential differences in the empirical effects of ABP and VBP. By uncovering the effects of EIA portfolio structures on distinct dimensions of firm financial performance, our study can help managers make more effective decisions regarding EIAs based on the expected effects on different financial outcomes. Third, our study contributes to research on technological portfolio structures by specifically testing the effects of the ecological part of firms' innovation assets. Previous studies in the field of technological portfolios typically consider a firm's entire patent portfolio, not accounting for the peculiarities of ecological innovations (Chen, Yang, and Lin2013; Kim, Lee, and Cho2016). In contrast, paying attention to the specific nature of ecological innovations as part of firms' entire innovation portfolios could help explain the variety of outcomes in extant research investigating the effect of portfolio structures. The rest of the paper is structured as follows. Section2 introduces the conceptual background of our study. In Section3, we describe our research framework and develop the hypotheses. Section4 describes the methodology of the study, followed by Section5, which presents the results of our analysis. In the last section, we conclude by developing theoretical and practical implications. 2 | Conceptual Background 2.1 | Literature Review on Ecological Innovation and Firm Financial Performance Ever since Porter and van der Linde's (1995) seminal article, in which they highlight ecological innovations' potential for competitiveness, researchers have been interested in drivers and financial outcomes of ecological innovation. Ecological innovation fundamentally differs from regular innovation in its potential to reduce environmental impact (OECD 2009). The term can refer to “new or modified processes, techniques, practices, systems and products” (Beise and Rennings2003, 8), and these innovations can range from “endofpipe” technologies, which aim at controlling and minimizing pollutants at the end of the production process (Berrone and GomezMejia2009; Rennings 2000; Xie et al. 2016), to preventive technologies, which focus on minimizing and eliminating the generation of pollution and waste throughout the production process (Berrone and GomezMejia 2009; Xie et al. 2016). Numerous studies have evolved that examine critical drivers of ecological innovation, such as organizational innovation (Bataineh, SánchezSellero, and Ayad2024a) or green dynamic capabilities (Singh etal.2022). Based on different conceptualizations of ecological innovation, another research stream focuses on its financial performance outcomes. Table1 presents an overview of relevant studies in this research stream.
4003 TABLE 1 | Literature review table with relevant studies on ecological innovation and financial performance. Study Independent variables Dependent variables Conceptual differentiation between ABP and VBP Theory Sample Exemplary studies testing the general effect of ecoinnovation AguileraCaracuel and OrtizdeMandojana(2013) Green innovation intensity ROA —Institutional theory 70 green innovative vs. 70 nongreen innovative firms Crosssectional Li(2014)Environmental innovation practices Financial performance construct —Institutional theory/RBV 148 Chinese manufacturing firms Crosssectional BermúdezEdo, HurtadoTorres, and OrtizdeMandojana(2017) Patented environmental innovations Tobin's Q —NRBV 35 ICT FT 500 firms 2005–2009 116 observations Cai and Li(2018)Ecoinnovation Economic performance construct — — 442 Chinese firms Crosssectional LealRodríguez etal.(2018) Green innovation performance Organizational performance construct — — 145 Spanish manufacturing firms Crosssectional GarcíaSánchez, GallegoÁlvarez, and ZafraGómez (2020) Ecoinnovation ROA ROE Tobin's Q Yes — 6454 international firms 2002–2017 95,489 observations Rezende etal.(2019) Green innovation intensity ROA — — 356 firms 2006–2016 2492 observations Zhang, Rong, and Ji(2019) Green innovation Sales growth Net profit — — 764 firms 2000–2010 5727 observations Przychodzen, La Leyvade Hiz, and Przychodzen(2020) Green innovation Overconcentration of green innovation ROA, OM, ROEC, MV/MV No —500 companies 1999–2016 9009 observations (Continues)
4004 Business Strategy and the Environment, 2025 Study Independent variables Dependent variables Conceptual differentiation between ABP and VBP Theory Sample MarínVinuesa etal.(2020) Ecoinnovation ROE —RBV 87 Spanish firms from industrial, transport, logistics, and waste industry Crosssectional Farza etal.(2021)Environmental innovation Markettobook ratio ROA ROIC No Theory of slack resources, NRBV 110 German HDAX firms 2008–2019 Singh etal. (2022)Green innovation Firm performance construct —Stakeholder theory, RBV 248 SME firms from manufacturing sector in Abu Dhabi Suki etal.(2022)Green innovation Business sustainability construct —NRBV 243 manufacturing firms in Malaysia Crosssectional Tian etal.(2023)Green innovation Tobin's Q —NRBV 351 Chinese firms 2007–2018 2734 observations Exemplary studies differentiating between ecoinnovation types Chen, Lai, and Wen(2006) Performance of green product innovation Performance of green process innovation Corporate competitive advantage — — 203 firms from the information and electronics industry in Taiwan Crosssectional Chang (2011)Green product innovation Green process innovation Competitive advantage —Institutional theory, stakeholder theory, RBV 106 manufacturing firms in Taiwan Crosssectional AmoresSalvadó, Martínde Castro, and NavasLópez(2014) Environmental product innovation ROA growth ROS growth ROCE growth —RBT/NRBV 157 firms Crosssectional (Continues) TABLE 1 | (Continued)
4005 Study Independent variables Dependent variables Conceptual differentiation between ABP and VBP Theory Sample Ghisetti and Rennings(2014) Energy and resource efficiency innovation Externality reducing innovation ROS —Porter hypothesis 1063 German firms Crosssectional Rexhäuser and Rammer(2014) Regulationinduced vs. voluntary environmental innovations Efficiency improving vs. other environmental innovations ROS —Porter hypothesis 3618 German firms Crosssectional Przychodzen and Przychodzen(2015) Product ecoinnovation Process ecoinnovation Market ecoinnovation Sources of supply ecoinnovation ROA ROE ERR — — 439 firms 2006–2013 632 vs. 2648 observations (eco vs. conventional) Chan etal. (2016)Green product innovation Cost efficiency Firm profitability —Contingency theory 250 firms Crosssectional Hojnik and Ruzzier(2016) Process ecoinnovation Company growth Company profitability —Porter hypothesis, institutional theory 223 Slovenian firms Crosssectional Xie etal.(2016) Green process innovation (clean technologies, endofpipe technologies) ROA —RBT/NRBV Chinese manufacturing firms 2001–2010 196 observations Huang and Li(2017) Green product innovation Green process innovation Organizational performance construct —Dynamic capability perspective/ SNT/ecological modernization theory 418 firms from ICT industry in Taiwan Crosssectional Hojnik, Ruzzier, and Manolova(2018) Product ecoinnovation Process ecoinnovation Organizational ecoinnovation Firm performance construct — Learning theory 151 Slovenian firms Crosssectional Tang etal. (2018)Green product innovation Green process innovation Firm performance construct —Porter hypothesis 188 Chinese manufacturing firms Crosssectional (Continues) TABLE 1 | (Continued)
4006 Business Strategy and the Environment, 2025 Study Independent variables Dependent variables Conceptual differentiation between ABP and VBP Theory Sample Tumelero, Sbragia, and Evans(2019) Product ecoinnovation Process ecoinnovation Organizational ecoinnovation Socioeconomic performance construct — — 221 electrical and electronic manufacturers in Brazil Crosssectional Xie, Huo, and Zou(2019) Green product innovation Green process innovation ROA —RBV 209 firms Crosssectional Wang etal.(2021)Green product innovation Green process innovation Economic performance construct — — 642 industrial Chinese firms Crosssectional Iqbal etal.(2022)Environmental process innovation Environmental product innovation Markettobook ratio Tobin's Q —RBV US listed firms 2002–2019 8511 observations Rahman(2023)Green product innovation ROE —Instrumental stakeholder theory/resource department theory US firms 2000–2019 366 observations Bataineh, SánchezSellero, and Ayad(2024b) Reduction of energy/material Improvement in HSE Compliance with regulations Competitive advantage —RBV Spanish firms 2003–2016 21,140 observations Studies considering the technological distribution of ecological innovation La Leyvade Hiz, FerronVilchez, and AragonCorrea(2019) Focused ecological innovation Tobin's Q —Agency theory 75 US companies 2006–2009 216 observations This study Ecological innovation depth Ecological innovation breadth Market value Net income Yes RBV/NRBV 340 US companies 2009–2018 3400 observations TABLE 1 | (Continued)
4007 The first group of studies does not explicitly distinguish between different types of ecological innovations. Within this field, various studies examine the performance implications for ABP. For example, AguileraCaracuel and OrtizdeMandojana (2013) find that firms introducing ecological innovation do not experience greater performance than other firms. MarínVinuesa etal.'s(2020) results support these findings, showing that owning green patents does not enhance firm performance. However, both studies indicate that the level of ecological innovation is positively associated with performance. Other studies find that this relationship is dependent on state ownership (Zhang, Rong, and Ji2019) or time lags (Rezende etal.2019). In terms of VBP, Tian etal.(2023) show a positive effect on Tobin's Q, while BermúdezEdo, HurtadoTorres, and OrtizdeMandojana (2017) find that ecological innovation only enhances performance when the innovations' international scope of the knowledge sourcing is high. Some studies simultaneously consider ABP and VBP: While Farza et al. (2021) find a positive effect of ecological innovation for both performance types, Przychodzen, La Leyvade Hiz, and Przychodzen (2020) show that an overconcentration on ecological innovation harms ABP and VBP. However, acting as a first mover in ecological innovation can increase VBP (Przychodzen, La Leyvade Hiz, and Przychodzen 2020). In contrast to the aforementioned studies, GarcíaSánchez, GallegoÁlvarez, and ZafraGómez (2020) consider the conceptual differentiation between ABP and VBP and assume different effects for both innovation types. As expected, ecological innovation harms firm profitability but enhances its market value (GarcíaSánchez, GallegoÁlvarez, and ZafraGómez 2020). Further studies in this field rely on varying performance constructs, showing mixed results (e.g., Cai and Li2018; LealRodríguez etal.2018; Li2014; Suki etal.2022). A second group of studies distinguishes ecological innovation types. A majority of research in this field differentiates between ecological product and/or process innovation (e.g., AmoresSalvadó, Martínde Castro, and NavasLópez 2014; Chen, Lai, and Wen2006; Huang and Li2017; Iqbal etal.2022; Lin, Tan, and Geng2013). Other studies include further types, such as organizational ecoinnovation (Hojnik, Ruzzier, and Manolova 2018; Tumelero, Sbragia, and Evans2019) or market and sources of supply ecoinnovation (Przychodzen and Przychodzen 2015). Most of these studies indicate a positive influence of ecological innovation types on ABP (Przychodzen and Przychodzen2015; Rahman2023; Xie, Huo, and Zou2019), VBP (Iqbal etal.2022), and performance constructs (Hojnik and Ruzzier2016; Huang and Li2017), although some cannot support direct significant effects of ecological product innovation (e.g., AmoresSalvadó, Martínde Castro, and NavasLópez2014; Wang etal.2021). Further distinctions within this group of studies are made with regard to energy and resource efficiency versus externalityreducing innovations (Ghisetti and Rennings2014), regulationinduced versus voluntary environmental innovations, and efficiencyimproving versus other innovations (Rexhäuser and Rammer2014), reductionrelated versus improvementrelated and compliancerelated innovations (Bataineh, SánchezSellero, and Ayad 2024b), or clean versus endofpipe technologies (Xie etal.2016). These studies find mixed results for the different innovation types on ABP and firms' competitive advantage. Unlike the aforementioned groups of studies, La Leyvade Hiz, FerronVilchez, and AragonCorrea (2019) focus on the technological distribution of EIAs within firms' ecological innovation portfolios. Building on agency theory, the authors examine the influence of slack resources on the relationship between focused ecological innovation and VBP. As we are also interested in the technological distribution of EIAs, our study is most closely related to La Leyvade Hiz, FerronVilchez, and AragonCorrea(2019). However, we rely on an RBV and examine the firms' ecological innovation portfolio, considering the depth and the breadth of their EIAs. Moreover, to gain a comprehensive understanding of the effects of ecological innovation, we include VBP as well as ABP. 2.2 | The RBV and the NRBV The RBV and the NRBV provide the theoretical foundation to further examine the role of EIAs and their distribution within innovation portfolios for firms' competitive advantage. From an RBV, firms' heterogeneous resources can explain performance outcomes in terms of their value, rarity, inimitability, and nonsubstitutability (Barney 1991). Researchers have argued that innovation assets often meet the required criteria and therefore denote important resources for firm performance (Fang, Palmatier, and Grewal2011). The NRBV extends this perspective to include the natural environment and argues that firms can only sustain their competitive advantage if they develop resources and capabilities that incorporate the challenges of the natural environment (Hart1995). Due to their tacit nature, social complexity, and rareness, such resources and capabilities are costly for other firms to copy. Simultaneously, firms can profit from positive effects of a good reputation through the resources' external orientation, which engenders social legitimacy (Hart 1995). Referring to research on institutional theory (e.g., DiMaggio and Powell1983), Hart(1995) argues that firms can only create competitive advantage when also achieving social legitimacy. In consideration of the growing environmental burdens firms face currently, the NRBV (Hart1995; Hart and Dowell2011) has gained significant importance (Hart and Dowell 2011) and has widely been implemented as a theoretical foundation in research on ecological innovation (Lee and Min2015; Suki etal.2022; Tian etal.2023). Building on these foundations, we classify ecological innovations as critical firm assets for creating and sustaining competitive advantage. Hence, in the remainder of this paper, we consider ecological innovations to be EIAs. 2.3 | Technological Portfolios Previous research has highlighted innovation performance to be a central driver for longterm success (e.g., SánchezSellero etal.2015). In line with the RBV, scholars have taken a portfolio perspective to investigate how firms should distribute their innovation assets to best profit from their innovation stock. In that vein, Prahalad and Hamel (1990) extend the RBV by emphasizing the strategic importance of firms' core competencies for gaining superior firm performance. Core competencies emerge
4008 Business Strategy and the Environment, 2025 through the harmonization of technology streams, which requires a deep knowledge stock in the corresponding technology fields (Bierly and Chakrabarti1996; Prabhu, Chandy, and Ellis2005). By accumulating innovation assets, firms can build up such a deep knowledge stock and thereby profit from specific competencies in their core technological areas. In contrast to this theory of firms' core competencies, researchers have long focused on the role of technological diversification (Ceipek etal.2019). Patel and Pavitt(1997) find that large firms are characterized by broad technological competencies outside their ‘core’ fields and that technological diversification usually exceeds their product diversification. The ability to draw on a broad knowledge base allows firms to be more adaptable and to combine different technologies for new, more complex products and production systems (Bierly and Chakrabarti1996; Patel and Pavitt1997). Grantstrand, Patel, and Pavill(1997) highlight the importance of technological diversification, arguing that instead of focusing on a limited amount of core technological competencies, large firms should build up a broader set of these competencies, even if that means that the existing competency stock becomes less deep. By spreading their innovation assets across multiple technology fields, firms can build up the required knowledge stock to develop such a broad set of technological competencies. However, when diversifying technological portfolios, firms must be careful not to lose their focus on developing strong competencies within specific technological fields (Leten, Belderbos, and van Looy2007). Moreover, diversified portfolios are often accompanied by high costs, such as coordination or communication costs (Leten, Belderbos, and van Looy2007). In summary, the foundations discussed herein support the notion that firms should consider both the depth (accumulation of innovation assets within technology fields) and breadth (diversification of innovation assets across technology fields) of their technological portfolio when developing their portfolio structure. 3 | Research Framework and Hypotheses 3.1 | Research Framework Building on the previous discussion, we assume that EIAs significantly influence firms' financial performance. In line with the NRBV, EIAs can provide firms with a competitive advantage because of their tacit nature, social complexity, and rareness (Barney1991; Hart1995; Hart and Dowell2011). However, the greater novelty and complexity of ecological innovation compared with regular innovation make it more difficult for firms to build up a substantial EIA stock. Moreover, high development costs mean firms must consider that it usually takes some time before EIAs pay off (Hermundsdottir and Aspelund2021). Consequently, it is important for firms to carefully evaluate how to strategically build up such critical assets. Therefore, we examine two specific characteristics of EIA portfolio structures by drawing on knowledge from research on technological portfolios: In line with the theory of firms' core competencies (Prahalad and Hamel1990) and research on technological diversification (e.g., Grantstrand, Patel, and Pavill1997), we assume distinct effects for EIA depth (accumulation of EIAs in the corresponding technology fields of the firm) and EIA breadth (diversification of EIAs throughout various technology fields) on firm financial performance. To shed light on the effects of EIA depth and EIA breadth and to allow for a comparison of results, we rely on two performance outcomes: ABP and VBP. Figure1 summarizes our research framework. 3.2 | Hypotheses The NRBV contends that firms must consider the natural environment when developing strategic resources (Hart1995). As ecological innovations are characterized by rareness, tacitness, and social complexity, which make them difficult to copy, they serve as promising resources to provide firms with a competitive FIGURE 1 | Research framework.
4015 TABLE 3 | Descriptive statistics and correlations. Mean SD VIF 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ABP 1886.28 4353.09 N/A 1.00 VBP 38,427.23 74,584.30 N/A 0.82 1.00 Prior financial performance 3924.10 7299.79 1.73 0.85 0.81 1.00 Firm size (log) 3.04 1.38 1.58 0.38 0.40 0.47 1.00 R&D intensity 0.06 0.25 1.07 −0.02 0.00 −0.04 −0.19 1.00 Leverage 0.25 0.23 1.02 −0.05 −0.05 −0.03 0.03 −0.01 1.00 Capital intensity 0.08 0.11 1.23 −0.05 −0.01 0.04 −0.21 −0.02 0.07 1.00 Competitive intensity 0.02 0.00 1.21 0.12 0.15 0.15 −0.07 0.01 0.00 0.15 1.00 Market turbulence 1.58 2.27 1.13 −0.01 0.02 −0.02 −0.11 0.08 −0.02 −0.07 0.27 1.00 Regular innovation assets 663.37 2195.36 2.16 0.44 0.49 0.46 0.29 0.04 −0.06 −0.05 −0.02 −0.03 1.00 Regular innovation scope 47.38 66.68 5.72 0.39 0.45 0.45 0.41 0.01 −0.06 −0.17 −0.09 0.01 0.63 1.00 EIA total 59.23 246.97 3.22 0.30 0.32 0.40 0.28 0.02 −0.03 −0.03 −0.11 −0.08 0.59 0.65 1.00 EIA relative 0.06 0.15 1.43 0.02 0.01 0.07 0.00 −0.02 0.02 0.23 −0.10 −0.06 0.01 0.07 0.19 1.00 EIA depth 1.17 2.26 2.96 0.35 0.34 0.39 0.18 0.10 −0.08 −0.04 −0.08 −0.02 0.60 0.59 0.75 0.29 1.00 EIA breadth 1.13 1.21 3.73 0.31 0.36 0.36 0.28 0.05 −0.09 −0.13 −0.09 0.06 0.39 0.79 0.39 0.27 0.48 1.00
4016 Business Strategy and the Environment, 2025 TABLE 4 | Effects of EIA on accountingand valuebased performance. Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) (5) (6) (7) (8) Control variables Prior financial performance 0.769*** (0.079) 0.760*** (0.078) 0.760*** (0.078) 0.747*** (0.078) 0.487*** (0.077) 0.482*** (0.077) 0.481*** (0.077) 0.468*** (0.076) Firm size (log) −0.022 (0.045) −0.019 (0.044) −0.025 (0.044) −0.033 (0.044) 0.116* (0.048) 0.123** (0.047) 0.128** (0.048) 0.120* (0.048) R&D intensity 0.001 (0.002) 0.001 (0.002) 0.002 (0.002) 0.002 (0.002) −0.001 (0.002) −0.002 (0.002) −0.002 (0.002) −0.003 (0.002) Leverage 0.014 (0.015) 0.013 (0.015) 0.014 (0.015) 0.013 (0.015) 0.034** (0.013) 0.034** (0.013) 0.033** (0.013) 0.032** (0.013) Capital intensity −0.078* (0.033) −0.079* (0.032) −0.079* (0.032) −0.079* (0.032) 0.040** (0.014) 0.038** (0.014) 0.038** (0.014) 0.038** (0.014) Competitive intensity −0.018 (0.027) −0.018 (0.027) −0.024 (0.028) −0.027 (0.028) −0.008 (0.023) −0.008 (0.024) −0.004 (0.024) −0.007 (0.023) Market turbulence 0.015 (0.014) 0.013 (0.014) 0.013 (0.014) 0.015 (0.014) 0.005 (0.012) 0.004 (0.012) 0.004 (0.012) 0.007 (0.012) Regular innovation assets 0.086 (0.087) 0.080 (0.085) 0.097 (0.086) 0.067 (0.083) 0.311 (0.162) 0.306 (0.161) 0.295 (0.160) 0.264 (0.161) Regular innovation scope −0.062 (0.085) −0.022 (0.087) 0.053 (0.089) 0.052 (0.088) 0.619*** (0.144) 0.663*** (0.146) 0.613*** (0.137) 0.612*** (0.136) EIA total −0.112 (0.069) −0.164* (0.071) −0.162* (0.071) −0.292*** (0.089) −0.076 (0.059) −0.098 (0.060) −0.100 (0.060) −0.232*** (0.059) EIA relative 0.006 (0.009) −0.003 (0.010) −0.004 (0.010) 0.002 (0.010) 0.006 (0.004) 0.009 (0.006) 0.010 (0.006) 0.016* (0.006) Independent variables EIA depth 0.102** (0.033) 0.080* (0.032) 0.095** (0.035) 0.034 (0.033) 0.049 (0.032) 0.064 (0.033) EIA breadth −0.039 (0.023) 0.126* (0.053) 0.185*** (0.055) −0.059*** (0.017) −0.171*** (0.051) −0.112* (0.051) EIA breadth2−0.218** (0.073) −0.232** (0.072) 0.147* (0.065) 0.133* (0.065) Interaction effect EIA depth × EIA breadth 0.150** (0.051) 0.151*** (0.043) Waldχ21238.95 1258.34 1275.37 1296.94 1704.80 1718.56 1731.54 1769.45 (Continues)
4017 support our main findings (see Table8). For VBP, all effects are supported, and we see an additional positive effect for EIA depth. For ABP, the main effect of EIA depth is supported as well as its interaction effect with EIA breadth. While winsorizing the data reduces the influence of extreme outliers, it can distort the distribution and potentially exclude relevant information, which is why we rely on the full range of data in our main analysis. Fifth, one issue that could diminish the validity of our results is that of common method bias stemming from the use of the same data for independent and dependent variables. Although we relied exclusively on secondary data, the fact that we extracted the data from independent data sources reduces the possibility for common method bias (Podsakoff etal.2003). Besides including further variables and lagging our dependent variables to account for the possibility of reverse causality and omitted variables, we relied on a twostage least squares (2SLS) instrumental variable (IV) approach to further address the issue of potential endogeneity. Following extant research, we used the industry mean of our variables of interest (i.e., EIA depth and EIA breadth) as primary instruments (Dotzel and Shankar2019; Iqbal etal.2022; Kim, Lee, and Cho2016) because, while it is likely that firms' EIA portfolio structure could be influenced by that of their competitors, the competitors' EIA portfolios are unlikely to have a direct impact on the focal firms' performance outcomes. The results of the 2SLS IV regression are consistent with our results in the main analysis, suggesting that the estimates are not biased (see Table9). Altogether, considering the conceptual development of the study and our methodological implementation, we conclude that the potential for biases rooted in endogeneity is rather low in our study. 6 | Discussion To respond to growing stakeholder pressure regarding firms' environmental impact and to remain competitive in an environment increasingly characterized by resource scarcity, firms can rely on ecological innovations to reduce their environmental impact. This study investigates the role of firms' EIA portfolio structures for the financial performance outcome of ecological innovation. 6.1 | Theoretical Implications Extant research shows varying results regarding the performance outcomes of ecological innovation: Whereas several studies demonstrate a positive relationship (e.g., AguileraCaracuel and OrtizdeMandojana 2013; Farza et al. 2021), others find a negative relationship (e.g., BermúdezEdo, HurtadoTorres, and OrtizdeMandojana2017) or no effects (e.g., Cai and Li2018). Various studies have made attempts to uncover the circumstances in which firms can benefit from their ecological innovations (GarcíaSánchez, GallegoÁlvarez, and ZafraGómez 2020; La Leyvade Hiz, FerronVilchez, and AragonCorrea 2019). Our research builds on these attempts, delving more deeply into the management of EIAs by taking the perspective of core competencies Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) (5) (6) (7) (8) Observations 3400 3400 3400 3400 3400 3400 3400 3400 R20.290 0.293 0.296 0.299 0.359 0.361 0.363 0.368 Adjusted R20.206 0.209 0.212 0.216 0.284 0.285 0.287 0.293 F statistic 112.632*** 96.796*** 91.098*** 86.463*** 154.982*** 132.197*** 123.681*** 117.964*** Note: This table reports standardized coefficients; robust standard errors are in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001. TABLE 4 | (Continued)
4018 Business Strategy and the Environment, 2025 (Prahalad and Hamel1990) and technological diversification (Grantstrand, Patel, and Pavill1997). In doing so, we disentangle EIA into EIA depth and EIA breadth to examine how different portfolio structures of EIAs influence firms' financial performance. We find different effects of EIA depth and breadth, which indicates that the effect of ecological innovation on financial performance depends on how firms structure their EIA portfolio. These results highlight the importance of considering the management and efficient deployment of resources when considering adding ecological innovation to a firm's portfolio, which is in line with resourcebased perspectives (Amit and Schoemaker 1993; Barney 1991; Fang, Palmatier, and Grewal2011; Hart and Dowell2011). In contrast to studies considering EIAs in aggregated terms (e.g., Cai and Li 2018; Iqbal etal.2022; Xie etal. 2016), we disaggregate EIAs into depth and breadth, which offers a more detailed picture of when EIAs provide a competitive advantage (Barney 1991). This approach enables a better understanding of the effective distribution of EIAs, emphasizing the strategic importance of technological portfolios in maximizing EIAs' value. Therefore, research on ecological innovation and firm performance can benefit from incorporating insights from the broader innovation literature, such as core competencies (Prahalad and Hamel1990) and technological diversity (Grantstrand, Patel, and Pavill1997). Building on our findings, our study also points to the potential presence of nonlinear effects in the context of ecological innovations' performance outcomes. Extant studies have neglected nonlinear effects, which has sometimes led them to underestimate the complex and multifaceted nature of ecological innovation. Especially in the context of innovation TABLE 5 | Effects of nonlinear EIA depth on accountingand valuebased performance. Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) Control variables Prior financial performance 0.747*** (0.078) 0.747*** (0.078) 0.468*** (0.076) 0.467*** (0.076) Firm size (log) −0.033 (0.044) −0.033 (0.044) 0.120* (0.048) 0.117* (0.048) R&D intensity 0.002 (0.002) 0.002 (0.002) −0.003 (0.002) −0.002 (0.002) Leverage 0.013 (0.015) 0.013 (0.015) 0.032** (0.013) 0.033** (0.013) Capital intensity −0.079* (0.032) −0.079* (0.032) 0.038** (0.014) 0.038** (0.014) Competitive intensity −0.027 (0.028) −0.027 (0.027) −0.007 (0.023) −0.005 (0.023) Market turbulence 0.015 (0.014) 0.015 (0.014) 0.007 (0.012) 0.006 (0.012) Regular innovation assets 0.067 (0.083) 0.067 (0.084) 0.264 (0.161) 0.263 (0.161) Regular innovation scope 0.052 (0.088) 0.051 (0.089) 0.612*** (0.136) 0.602*** (0.136) EIA total −0.292*** (0.089) −0.292** (0.093) −0.232*** (0.059) −0.221*** (0.061) EIA relative 0.002 (0.010) 0.002 (0.010) 0.016* (0.006) 0.012 (0.007) Independent variables EIA depth 0.095** (0.035) 0.098+ (0.054) 0.064 (0.033) 0.104* (0.044) EIA depth2−0.003 (0.051) −0.042 (0.038) EIA breadth 0.185*** (0.055)) 0.184*** (0.055) −0.112* (0.051) −0.114* (0.051) EIA breadth2−0.232** (0.072 −0.231** (0.073) 0.133* (0.065) 0.139* (0.065) Interaction effect EIA depth × EIA breadth 0.150** (0.051) 0.150** (0.050) 0.151*** (0.043) 0.158*** (0.042) Waldχ21296.94 1296.53 1769.45 1771.81 Observations 3400 3400 3400 3400 R20.299 0.299 0.368 0.369 Adjusted R20.216 0.215 0.293 0.293 F statistic 86.463*** 81.033*** 117.964*** 110.738*** Note: This table reports standardized coefficients; robust standard errors are in parentheses. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001.
4019 TABLE 6 | Effects of EIA based on patent stock t on accountingand valuebased performance. Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) (5) (6) (7) (8) Control variables Prior financial performance 0.778*** (0.079) 0.776*** (0.078) 0.777*** (0.078) 0.770*** (0.078) 0.532*** (0.083) 0.534*** (0.083) 0.529*** (0.083) 0.526*** (0.084) Firm size (log) −0.014 (0.044) −0.016 (0.044) −0.016 (0.044) −0.016 (0.044) 0.189** (0.063) 0.192** (0.063) 0.191** (0.062) 0.191** (0.061) R&D intensity 0.001 (0.002) 0.002 (0.002) 0.002 (0.002) 0.001 (0.002) 0.001 (0.002) 0.001 (0.002) 0.00003 (0.002) −0.0001 (0.002) Leverage 0.017 (0.015) 0.016 (0.015) 0.017 (0.015) 0.016 (0.015) 0.047*** (0.014) 0.048*** (0.014) 0.045*** (0.014) 0.045** (0.014) Capital intensity −0.075* (0.032) −0.075* (0.032) −0.075* (0.032) −0.076* (0.032) 0.040** (0.014) 0.041** (0.015) 0.039** (0.014) 0.039** (0.014) Competitive intensity −0.014 (0.027) −0.012 (0.027) −0.012 (0.027) −0.016 (0.028) 0.029 (0.022) 0.027 (0.022) 0.028 (0.022) 0.026 (0.022) Market turbulence 0.017 (0.014) 0.015 (0.014) 0.015 (0.014) 0.016 (0.014) −0.009 (0.013) −0.007 (0.013) −0.006 (0.013) −0.005 (0.013) Regular innovation assets −0.032 (0.043) −0.031 (0.042) −0.031 (0.042) −0.033 (0.041) −0.103 (0.059) −0.105 (0.059) −0.107 (0.061) −0.108 (0.060) Regular innovation scope −0.073 (0.059) −0.068 (0.059) −0.059 (0.060) −0.050 (0.060) 0.462*** (0.075) 0.459*** (0.075) 0.408*** (0.070) 0.412*** (0.070) EIA total 0.014 (0.064) −0.003 (0.067) −0.002 (0.067) −0.088 (0.086) −0.010 (0.042) 0.008 (0.044) 0.005 (0.044) −0.026 (0.059) EIA relative −0.001 (0.009) −0.007 (0.009) −0.008 (0.009) −0.006 (0.009) 0.005 (0.004) 0.013* (0.006) 0.018** (0.006) 0.019** (0.006) Independent variables EIA depth 0.034 (0.020) 0.032 (0.019) 0.045+ (0.023) −0.038* (0.018) −0.020 (0.017) −0.016 (0.021) EIA breadth 0.0005 (0.017) 0.024 (0.041) 0.020 (0.041) −0.006 (0.012) −0.147** (0.047) −0.148** (0.047) EIA breadth2−0.030 (0.059) −0.008 (0.061) 0.180** (0.063) 0.188** (0.065) Interaction effect EIA depth × EIA breadth 0.081* (0.035) 0.029 (0.032) Waldχ21226.65 1230.66 1230.77 1247.31 1283.24 1290.73 1317.90 1320.62 (Continues)
4020 Business Strategy and the Environment, 2025 portfolio management, technological diversification research suggests a nonlinear effect of EIA breadth (e.g., Kim, Lee, and Cho2016). Our findings expand on this by revealing an inverted Ushaped effect for EIA breadth on ABP and a Ushaped effect on VBP. Delineating EIA into more finegrained effects allows a more thorough accounting of the multifaceted aspects of ecological innovation. In doing so, we demonstrate that spreading ecological innovation among too many different technology fields can be harmful for firms. This finding highlights the importance of leveraging and transferring theoretical foundations from the innovation literature to explain the nuanced effects of ecological innovation, which are not always straightforward. Furthermore, considering distinct types of performance outcomes provides a more comprehensive understanding of the effects of EIA compared with studies that only consider one performance dimension. Our findings reveal essential differences in the effects of EIA depth and EIA breadth for ABP and VBP, highlighting the necessity of accounting for distinct dimensions of financial performance and of considering these differences in theoretical reasoning, because the underlying mechanisms driving these outcomes differ. The results suggest that conclusions cannot be generalized across performance outcomes because they result in different implications. Therefore, we follow Grewatsch and Kleindienst(2017) in suggesting that studies in the field should account for those differences in their theoretical considerations. Finally, from the perspective of research on technological portfolio structures, this study contributes by specifically testing the effects of the ecological part of firms' innovation assets. Most technological portfolio studies consider a firm's entire patent portfolio (Kim, Lee, and Cho 2016), without differentiating specific innovation types. Considering that earlier works have highlighted that ecological innovations substantially differ from regular innovations (Cainelli, de Marchi, and Grandinetti2015), researchers in this area should consider those remarkable differences when investigating firms' technological portfolios. Doing so could help explain the varying outcomes other researchers have observed when investigating the effect of portfolio structures (e.g., Chiu etal.2008; Huang and Chen2010; Kim, Lee, and Cho2016). 6.2 | Managerial Implications For managerial practice, the results provide important insights for firms to remain financially successful by creating a dedicated and systematic EIA portfolio management strategy. They suggest that it is favorable for firms' profits to accumulate EIA in technology fields in which the firm already has knowledge (i.e., high depth), because firms can benefit from this deep knowledge stock with the ability to create new and complex knowledge more easily. Moreover, such deep assets serve as important strategic resources because they support firms in building up specific competencies that are rare and inimitable. With regard to EIA breadth, firms profit from an increasing breadth to a certain extent, after which overdiversifying EIA has a negative impact on firms' profit. Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) (5) (6) (7) (8) Observations 3400 3400 3400 3400 3400 3400 3400 3400 R20.287 0.288 0.288 0.291 0.297 0.298 0.303 0.303 Adjusted R20.203 0.204 0.204 0.206 0.214 0.215 0.220 0.220 F statistic 111.514*** 94.667*** 87.913*** 83.154*** 116.659*** 99.287*** 94.136*** 88.042*** Note: This table reports standardized coefficients; robust standard errors are in parentheses. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001. TABLE 6 | (Continued)
4021 TABLE 7 | Effects of EIA on accountingand valuebased performance in Year t + 2. Dependent variable: ABP (t + 2) Dependent variable: VBP (t + 2) (1) (2) (3) (4) (5) (6) (7) (8) Control variables Prior financial performance 0.465*** (0.084) 0.455*** (0.084) 0.456*** (0.084) 0.438*** (0.084) 0.535*** (0.123) 0.529*** (0.122) 0.528*** (0.122) 0.512*** (0.118) Firm size (log) 0.117* (0.050) 0.117* (0.050) 0.114* (0.050) 0.104* (0.050) 0.206* (0.091) 0.215* (0.092) 0.221* (0.092) 0.212* (0.093) R&D intensity 0.0005 (0.002) 0.001 (0.002) 0.001 (0.002) 0.001 (0.002) −0.003 (0.003) −0.004 (0.003) −0.004 (0.003) −0.005 (0.003) Leverage 0.057*** (0.017) 0.056** (0.017) 0.056** (0.017) 0.055** (0.017) 0.055*** (0.016) 0.055*** (0.016) 0.054*** (0.015) 0.053*** (0.015) Capital intensity −0.023 (0.025) −0.023 (0.025) −0.024 (0.025) −0.024 (0.025) 0.036 (0.027) 0.035 (0.027) 0.035 (0.026) 0.034 (0.026) Competitive intensity −0.042 (0.037) −0.043 (0.037) −0.046 (0.038) −0.050 (0.038) 0.006 (0.031) 0.006 (0.031) 0.012 (0.030) 0.009 (0.030) Market turbulence 0.016 (0.022) 0.013 (0.022) 0.013 (0.022) 0.015 (0.022) −0.019 (0.017) −0.020 (0.017) −0.019 (0.017) −0.017 (0.017) Regular innovation assets 0.213 (0.113) 0.208 (0.110) 0.216 (0.112) 0.176 (0.109) 0.247 (0.174) 0.242 (0.173) 0.224 (0.171) 0.186 (0.173) Regular innovation scope 0.025 (0.123) 0.052 (0.126) 0.086 (0.125) 0.087 (0.124) 0.774*** (0.192) 0.825*** (0.195) 0.745*** (0.179) 0.746*** (0.178) EIA total −0.194** (0.073) −0.251** (0.076) −0.250** (0.076) −0.418*** (0.100) −0.068 (0.072) −0.090 (0.072) −0.093 (0.072) −0.252*** (0.066) EIA relative −0.003 (0.012) −0.018 (0.013) −0.018 (0.013) −0.010 (0.013) 0.001 (0.005) 0.007 (0.008) 0.008 (0.008) 0.016* (0.008) Independent variables EIA depth 0.116** (0.041) 0.106** (0.040) 0.127** (0.043) 0.031 (0.041) 0.055 (0.038) 0.075 (0.041) EIA breadth −0.013 (0.029) 0.064 (0.076) 0.138 (0.074) −0.071*** (0.020) −0.250*** (0.074) −0.180* (0.079) EIA breadth2−0.102 (0.109) −0.118 (0.107) 0.236* (0.092) 0.220* (0.092) Interaction effect EIA depth × EIA breadth 0.192** (0.059) 0.181** (0.062) Waldχ2395.64 407.32 409.25 428.78 1252.05 1261.40 1279.51 1308.54 (Continues)
4022 Business Strategy and the Environment, 2025 In contrast, our results support the notion that stock markets pay greater attention to the breadth of a portfolio and do not value moderate levels of diversification, as we find a positive Ushaped effect of EIA breadth on VBP and no effect for EIA depth. In other words, investors seem to prefer either low or high diversification of EIA across different technology fields and tend to be skeptical with respect to a medium level of diversification. Although firms can profit from a medium level of EIA breadth, they should consider that investors evaluate performance frequently, and this medium level may initially increase investors' risk perception to the extent that the potential gains of a broad portfolio are neglected. However, when diversification reaches a high level, investors seem to appreciate the benefits resulting from an extensive diversification. Consequently, managers must be careful when diversifying their firm's EIA portfolio and be aware that even if firms profit from a medium level of EIA breadth, the market value could drop. Our research shows that although firms can partially benefit from knowledge outside their core technological areas, the drawbacks of such a diverse portfolio often surpass its advantages unless the firm can rely on a highly diverse portfolio. If firms aim to diversify their EIAs across different technology fields to exploit the advantages, they should simultaneously build up deep knowledge stocks in the corresponding technology fields, as indicated by our finding of a positive interaction effect of EIA depth and EIA breadth for both performance outcomes. However, we suggest firms should expand gradually so that they can develop deep knowledge stocks in the new technology fields. That way, they can leverage the advantages of EIA breadth and depth simultaneously and consequently increase both ABP and VBP. The advantages of a deep knowledge stock in the various technology fields seem to offset the risks of a broad EIA portfolio, especially for investor perceptions. Having a deep EIA knowledge stock in various technology fields seems to increase investor trust that the firm can overcome difficulties resulting from broad portfolios and that its technological competencies are strong enough to exploit the advantages of a broad EIA portfolio. 6.3 | Limitations and Future Research Although our study provides important implications for theory and practice, it is not without limitations. The analysis of EIA portfolio structures provides important insights into the successful management of ecological innovation. However, we analyzed the distribution of EIA in isolation, without considering the distribution of firms' other innovation assets. The effectiveness of EIA portfolios might be dependent on the optimal configuration of EIA and these other innovation assets. Future research should elaborate on this interplay in greater depth, along with potential contingency factors. In this context, further theoretical development is needed on the interconnections between ecological and regular innovation. It is also important to note that we tested our hypotheses on a sample of large US firms, which limits the generalizability of our findings to other contexts. The firms we relied on operate in a specific regulatory and economic environment that may not apply to firms in other regions. For instance, investors in Dependent variable: ABP (t + 2) Dependent variable: VBP (t + 2) (1) (2) (3) (4) (5) (6) (7) (8) Observations 3359 3359 3359 3359 3359 3359 3359 3359 R20.117 0.120 0.120 0.125 0.295 0.296 0.299 0.304 Adjusted R20.011 0.014 0.014 0.019 0.210 0.211 0.215 0.220 F statistic 35.967*** 31.333*** 29.232*** 28.585*** 113.823*** 97.031*** 91.394*** 87.236*** Note: This table reports standardized coefficients; robust standard errors are in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001. TABLE 7 | (Continued)
4023 TABLE 8 | Effects of EIA on winsorized accountingand valuebased performance. Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) (5) (6) (7) (8) Control variables Prior financial performance 0.503*** (0.069) 0.494*** (0.069) 0.494*** (0.069) 0.481*** (0.070) 0.211*** (0.041) 0.206*** (0.041) 0.205*** (0.040) 0.193*** (0.040) Firm size (log) 0.077 (0.044) 0.080 (0.044) 0.078 (0.044) 0.070 (0.044) 0.120*** (0.031) 0.121*** (0.031) 0.125*** (0.030) 0.118*** (0.030) R&D intensity 0.003 (0.002) 0.003 (0.002) 0.003 (0.002) 0.003 (0.002) 0.001 (0.002) 0.001 (0.002) 0.0004 (0.002) 0.0001 (0.002) Leverage −0.004 (0.015) −0.005 (0.015) −0.005 (0.015) −0.006 (0.015) 0.016 (0.010) 0.015 (0.010) 0.014 (0.010) 0.013 (0.010) Capital intensity −0.068*** (0.020) −0.069*** (0.020) −0.069*** (0.020) −0.069*** (0.020) 0.0004 (0.008) 0.0001 (0.008) 0.0002 (0.008) −0.0002 (0.008) Competitive intensity −0.056* (0.028) −0.057* (0.028) −0.059* (0.028) −0.062* (0.028) −0.015 (0.019) −0.015 (0.019) −0.011 (0.019) −0.013 (0.019) Market turbulence 0.026 (0.016) 0.024 (0.016) 0.024 (0.016) 0.027 (0.016) 0.037** (0.012) 0.035** (0.012) 0.035** (0.012) 0.038** (0.012) Regular innovation assets 0.080 (0.103) 0.074 (0.100) 0.079 (0.100) 0.048 (0.097) 0.145 (0.103) 0.142 (0.101) 0.129 (0.100) 0.100 (0.098) Regular innovation scope −0.138 (0.079) −0.097 (0.079) −0.075 (0.082) −0.075 (0.081) 0.328*** (0.064) 0.345*** (0.065) 0.289*** (0.063) 0.288*** (0.061) EIA total −0.159* (0.072) −0.210** (0.074) −0.210** (0.074) −0.344*** (0.099) −0.078 (0.041) −0.108** (0.041) −0.109** (0.041) −0.235*** (0.048) EIA relative 0.006 (0.009) −0.002 (0.010) −0.003 (0.010) 0.004 (0.010) 0.005 (0.004) −0.002 (0.006) −0.001 (0.006) 0.005 (0.006) Independent variables EIA depth 0.099** (0.034) 0.092** (0.035) 0.107** (0.037) 0.060* (0.025) 0.077** (0.026) 0.091*** (0.025) EIA breadth −0.041 (0.025) 0.008 (0.050) 0.068 (0.053) −0.014 (0.017) −0.139*** (0.034) −0.083* (0.034) (Continues)
4024 Business Strategy and the Environment, 2025 other regions might react differently to certain portfolio structures. Moreover, large firms usually have access to substantial resources, which allows for different strategic approaches than those of smaller firms. Their complex organizational structures further differentiate them from smaller firms, potentially leading to different strategic decisions. Future studies should investigate whether the effects of EIA depth and breadth vary in Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) (3) (4) (5) (6) (7) (8) EIA breadth2−0.065 (0.072) −0.079 (0.072) 0.164*** (0.046) 0.151*** (0.045) Interaction effect EIA depth × EIA breadth 0.154** (0.060) 0.144*** (0.031) Waldχ2569.75 585.51 586.58 605.61 649.77 662.92 684.54 729.75 Observations 3400 3400 3400 3400 3400 3400 3400 3400 R20.158 0.162 0.162 0.166 0.176 0.179 0.184 0.194 Adjusted R20.058 0.062 0.062 0.067 0.079 0.082 0.087 0.097 F statistic 51.796*** 45.039*** 41.899*** 40.375*** 59.070*** 50.994*** 48.896*** 48.650*** Note: This table reports standardized coefficients; robust standard errors are in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001. TABLE 8 | (Continued) TABLE 9 | 2SLS IV regression of the effects of EIA depth and breadth on accountingand valuebased performance. Dependent variable: ABP (t + 1) Dependent variable: VBP (t + 1) (1) (2) Control variables Prior financial performance 0.747*** (0.078) 0.468*** (0.076) Firm size (log) −0.033 (0.044) 0.120* (0.048) R&D intensity 0.002 (0.002) −0.003 (0.002) Leverage 0.013 (0.015) 0.032** (0.013) Capital intensity −0.079* (0.032) 0.038** (0.014) Competitive intensity −0.027 (0.028) −0.007 (0.023) Market turbulence 0.015 (0.014) 0.007 (0.012) Regular innovation assets 0.067 (0.083) 0.264 (0.161) Regular innovation scope 0.052 (0.088) 0.612*** (0.136) EIA total −0.292*** (0.089) −0.232*** (0.059) EIA relative 0.002 (0.010) 0.016* (0.006) Independent variables EIA depth 0.095** (0.035) 0.064 (0.033) EIA breadth 0.185*** (0.055) −0.112* (0.051) EIA breadth2−0.232** (0.072) 0.133* (0.065) Interaction effect EIA depth x EIA breadth 0.150** (0.051) 0.151*** (0.043) Waldχ21296.94 1769.45 Observations 3400 3400 R20.299 0.368 Adjusted R20.216 0.293 F statistic 1296.947*** 1769.457*** Note: This table reports standardized coefficients; robust standard errors are in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001.