Environmental Sustainability of Family Firms: A Meta-Analysis of Handprint and Footprint

Abstract

Our meta-analysis investigates the environmental sustainability performance of family firms (FFs) distinguishing between environmental hand- and footprint and accounting for FF heterogeneity. Based on a sample of 87 primary studies comprising 118,538 firms, we find no significant difference between FFs and non-FFs regarding their overall environmental sustainability performance. Yet, distinguishing between environmental hand- and footprint, we show that FFs have a lower footprint than non-FFs but do not differ regarding their handprint. Firm size, being a public firm, and type of family involvement moderate the effects of FF status on environmental sustainability performance. Our research note extends prior meta-analytical evidence and contributes to a more fine-grained and nuanced understanding of the environmental sustainability performance of FFs. Theoretical and managerial implications are discussed.

Keywords

family firms environmental footprint environmental handprint environmental sustainability family firm heterogeneity meta-analysis

Introduction

Recent research shows that six out of the nine planetary boundaries are transgressed, demonstrating that human activity affects the Earth’s climate and ecosystems more than ever (Richardson et al., 2023). Consequently, consumers, employees, investors, and regulators expect firms to behave in an environmentally sustainable way and expect them to become net zero or even net positive, that is, their positive environmental impact should equal or exceed their negative environmental impact (Desai et al., 2023; Shive & Forster, 2020). To achieve this goal, firms must manage both their negative and positive impacts, referred to as their environmental hand- and footprint. The environmental footprint results from activities that consume environmental resources and damage biological systems, while the environmental handprint comprises activities that stabilize and sustain biological systems and prevent resource drain (Guillaume et al., 2020; Hoekstra & Wiedmann, 2014).

Prior research indicates that firms vary in their impact on the natural environment, and that firm ownership and governance play a significant role in this regard (Aguilera et al., 2021; Shive & Forster, 2020). Family firms (FFs) have been suggested to differ in their environmental sustainability performance compared to non-family firms (non-FFs). FFs are defined as firms where a family controls the direct and indirect decision-making processes of the company, through ownership and/or management (Chua et al., 1999). A recent meta-analysis by Miroshnychenko et al. (2022) suggests that FFs have a lower environmental sustainability performance than non-FFs. Yet, the study does not distinguish between the environmental hand- and footprint of FFs. Prior research by Dyer and Whetten (2006, p. 785) suggests that FFs are primarily motivated by concerns about their public image and reputation and “a desire to protect family assets.” Consequently, they avoid actions that harm the environment, such as generating hazardous waste and contributing to air pollution, resulting in a lower environmental footprint as compared to non-FFs. Yet, a strong desire to maintain family assets and family control can also lead FFs to be cautious in their investment and innovation behavior (Block, 2012; Chrisman & Patel, 2012). Consequently, investments into environmental sustainability activities may be reduced, in particular those that are costly and uncertain in their effects, which is true for many handprint activities. Hence, the effect of FFs on environmental sustainability may, in fact, differ with regard to environmental hand- and footprint activities and outcomes. Second, the study by Miroshnychenko et al. (2022) does not fully account for the heterogeneity that exists within the group of FFs. Prior research suggests that FFs as a group are highly heterogeneous and differ regarding their values and concerns about their image, identity, and reputation (Rau et al., 2019; Sharma & Sharma, 2011; Zellweger et al., 2013), which could influence their environmental sustainability behavior.

Our meta-analysis addresses these two gaps and sheds light on the following research questions: how do FFs differ from non-FFs regarding their overall environmental sustainability performance as well as their environmental hand- and footprint? To what extent exist differences within the group of FFs regarding environmental sustainability?

Based on a sample of 87 primary studies (m = 292 effect sizes) comprising N = 118,538 firms, we find no difference in the overall environmental sustainability performance of FFs and non-FFs. Distinguishing between environmental hand- and footprint, a more nuanced picture emerges. Our results show that FFs have a lower environmental footprint than non-FFs but do not differ regarding their environmental handprint. Investigating FF heterogeneity, our results show that the type of family involvement (ownership vs. management), firm size, and being a public firm play a significant role. The FF effect on environmental sustainability performance is negative for large and public firms and positive for small firms. Moreover, family ownership tends to have a negative effect while the effect of family management seems to be insignificant.

The results of our research note contribute to family business research in two ways. Conceptually, we enrich the family business literature on sustainability by distinguishing between environmental hand- and footprint as two distinct forms of environmental impact. While this distinction is well established in the sustainability literature (Cabernard et al., 2022; Guillaume et al., 2020; Hoekstra & Wiedmann, 2014), family business research either does not make this conceptual distinction or merges different categories of environmental impact into an aggregate category (Yu et al., 2021). Hand- and footprint, however, represent different perspectives toward environmental sustainability (Guillaume et al., 2020) and the results of our meta-analysis show that this distinction matters empirically and should be considered in the discussion about the environmental impact of FFs. Our study also shows that important differences exist within the group of FFs that need to be accounted for when assessing the environmental sustainability performance of FFs. In this regard, the family business literature on environmental sustainability resembles the literature on the financial performance (Dyer, 2018) and innovation of FFs (Block et al., 2023). As an empirical contribution, our research note follows a call for regular updates and extensions of meta-analyses (Lakens et al., 2016), which are important when a research field is growing, and new evidence becomes available. In this regard, our meta-analysis uses a substantially larger sample of primary studies than Miroshnychenko et al. (2022) (87 vs. 26 studies), which is a previous meta-analysis on this topic.

The Environmental Footprint and Handprint of a Firm

Analyzing the environmental impact of firms is beneficial for both firms and the society. Firms learn how their ethical values and competitive strategy align with their sustainability behavior. The society learns which firms and firm types really have a positive (negative) impact on environmental sustainability. In this regard, the environmental sustainability literature distinguishes between two forms of environmental impact: the environmental footprint and the environmental handprint. Both concepts originate from the field of water resource management (Hoekstra & Wiedmann, 2014), and started to receive attention in broader management research (Guillaume et al., 2020).

The environmental footprint of firms comprises activities that damage the natural environment, either by pollution, waste generation or the detrimental usage of environmental resources. Accordingly, the literature distinguishes between different forms of footprint such as waste, water, energy, and carbon footprint, each focusing on a specific environmental resource (Matuštík & Kočí, 2021). The footprint comprises the firms’ direct and indirect negative environmental impacts. The former concerns the firm’s operational processes whereas the latter covers the entire supply chain. Thus, the environmental footprint of firms is often larger than expected.

The environmental handprint of a firm is defined as the sum of positive environmental impacts that serve to protect and sustain the environment (Guillaume et al., 2020). Referring to environmental footprint as a baseline or benchmark, the environmental handprint focuses on a positive change (Norris et al., 2021) aimed at reducing the environmental footprint by simultaneously adding value to it (Guillaume et al., 2020). This is achieved when individuals and firms voluntarily and proactively enhance their own or others’ positive impact on the natural environment (Norris et al., 2021; Zhao et al., 2022). Whereas the environmental footprint of a firm is at best zero, the environmental handprint is not limited. Forms of environmental handprints include the carbon handprint (Grönman et al., 2019), the nitrogen handprint (Lakanen et al., 2022) and the water handprint (Guillaume et al., 2020). The water handprint, for example, concerns water saving and a mindful usage of water resources.

Data and Method

Sample Collection and Search for Primary Studies

The search for primary studies was conducted between November 2021 and September 2023. Four search strategies were applied. First, we searched relevant electronic databases. These databases included Scopus, Google Scholar, OATD, ResearchGate, SSRN, RePEc, JSTOR, Web of Science (WOS), Business Source Complete (BSC), and EconLit. We searched for published (and unpublished or in press) studies by applying the following combinations of search terms:

{{environment, eco*, green, sustainable, sustainability} AND {actions, management system certification, performance, practices, product, product development, supply chain management, service} OR {pollution} AND {prevention, control}} AND {{family} AND {business, control, controlled firm, controlled business, firm, firm behavior, ownership, management, influence, involvement, business group}}. These search terms were also applied in the French and Spanish language. Second, we browsed the relevant prior meta-analytical and narrative reviews in the research fields of environmental sustainability, corporate social responsibility, and FFs. Third, we conducted a manual search in relevant academic journals.¹ Fourth, we directly contacted authors and experts of family business research. In this regard, we contacted the members of the editorial boards of Family Business Review, Journal of Family Business Strategy, and Journal of Family Business Management. We asked them to provide us with any published or unpublished articles, datasets, student theses, dissertations, conference presentations that might be relevant for our research project.²

After these steps, our sample of primary studies encompassed over 900 studies. We applied the following criteria to filter relevant studies for the meta-analysis: In a first step, we screened the studies for relevant effect sizes. This way, we removed qualitative studies and literature reviews as well as studies that do not report a correlation measure (or a measure that can be transformed into one). In a second step, we only included studies which compare FFs and non-FFs. The FF variable could be either a binary (being an FF or not) or a metric variable (e.g., the extent of family ownership). In a last step, we checked the measurement of the effect sizes and only included studies, which report one of the forms of environmental sustainability performance measures listed in the search terms above. This systematic search process followed recent best practices for sampling in meta-analyses (Block et al., 2023; Stanley et al., 2013). Finally, we identified four cases (see Table A1 in the online appendix, https://osf.io/ypkna), where the exact same sample is used in two primary studies (Wood, 2008). In this case, we excluded the study with the smaller number of effect sizes.

Description of the Sample of Primary Studies

Our final sample of primary studies encompassed 87 studies, reporting 292 relevant effect sizes, and incorporating N = 118,538 firms (see Table A1 in the online appendix, https://osf.io/ypkna). Most of the studies employed samples from mixtures of industries (k = 65), or from the manufacturing industry (k = 11). The number of effect sizes in our sample was m = 292, with the largest number measuring some kind of sustainability action (m = 159).

The sample included 70 published and unpublished journal articles, 11 working papers, two theses and four dissertations (see Table A3 in the online appendix, https://osf.io/ypkna). The most prominent journals were Business Strategy and the Environment (k = 13), Sustainability (k = 8), and Technological Forecasting and Social Change (k = 4). The academic field of sustainability (k = 37) comes first, followed by general management (k = 21), and family business and entrepreneurship (k = 7). The samples in these articles were mostly drawn from European countries (k = 31), followed by Asia (k = 23) and America (k = 18). Only 13 studies were based on a cross-national sample. Our raw data can be accessed via the Open Science Foundation (OSF).³

Focal Effect Size: The Relationship Between FFs and Environmental Sustainability Performance

After retrieving the primary studies, we identified relevant correlation coefficients that represented the target relationship between a FF-indicating variable, such as the level of family ownership, and an environmental sustainability performance measure. Whenever correlation coefficients were missing, we transformed the relevant statistics (t or F statistics) into a correlation coefficient. We relied on a broad FF-definition and included effect sizes where family involvement was measured through family ownership, management, and/or control. We did not require specific thresholds of family ownership or family representation in the management or supervisory board. The operationalization of the family influence in the primary study could be done through archival data or self-reported measures. Primary studies using family succession, family employment and lone founder ownership/management as FF-indicators were not included (Chua et al., 1999; Miller et al., 2011).

The coding was done by the first three authors. For the initial round, we formulated a coding protocol to systematically extract data pertaining to key variables, encompassing both effect sizes and sample characteristics. This protocol underwent iterative adaptations and enhancements. To ensure consistency among coders and maintain data accuracy, discussions and elaborations on coded effect sizes took place with all coders regularly in each round.

Coding of Firms’ Environmental Sustainability Performance

Overall environmental sustainability performance is an aggregate category that comprises all the effect sizes of primary studies measuring a relationship between FFs and environmental sustainability performance. In a next step, we distinguished between different forms of firms’ environmental sustainability performance and coded the effect sizes according to whether they concerned some form of sustainability action, sustainability outcome and other sustainability measure.

Environmental Sustainability Action

This category comprises effect sizes reflecting the firm’s actions toward improving its impact on the natural environment irrespective of whether these actions were effective. We distinguished between three sub-categories of environmental sustainability actions. Sustainability actions reducing the firm’s negative impact, for instance, include monitoring waste monitoring and implementing environmental management practices. In contrast, sustainability actions offsetting the firm’s negative impact reflect actions which include actions related to biodiversity protection, CO₂ compensation, and recycling. Finally, we coded effect sizes as environmental innovation, when a primary study reported the relationship between FF status and the number of green patents or green innovations.

Environmental Sustainability Outcome

This category includes effect sizes that refer to the relationship between FF status and the actual environmental outcome of the firms’ operations and sustainability attempts. We thereby distinguished between environmental footprint and environmental handprint. The former category comprises environmental outcomes referring to the actual pollution level of a firm, measured, for example, by the amount of CO₂ emissions. The latter category encompasses effect sizes concerning a quantifiable reduction in pollution.

Other environmental sustainability measure is an aggregate category relating to sustainability competencies, sustainability disclosure and sustainability goals and strategy.

Table A2 in the online appendix (https://osf.io/ypkna) gives an overview of the coding of our variables.

Moderators

To investigate potential conceptual or methodological context effects, we analyzed study-level moderators typical for FF research (O’Boyle et al., 2012). As conceptual moderators, we explored firm size, firm type (public vs. private), industry characteristics and national culture, which have been shown to influence FF behavior and outcomes (Carney et al., 2015; Villalonga & Amit, 2010). As methodological moderators, we analyzed family involvement, publication status, publication year, and the journal quality.

Conceptual Moderators

Firm Size refers to whether the primary study employed a sample of large firms or a sample of SMEs. If the sample of the primary study consisted of both large firms and SMEs, we coded this as mixed firm size.

Firm type (private vs. public). The firm type was coded as public when the primary study employed a sample of publicly listed firms; it was coded as private when a sample of privately owned firms was used. If the sample of the primary study consisted of both public and private firms, we coded this as mixed firm type.

Industry characteristics. We distinguished between effect sizes from high- and low-polluting industries. An example of a high (low) polluting industry would be the chemical industry (the winery industry); the distinction between high- and low-polluting industries was done based on the samples of the primary studies.

National culture. Based on Hofstede (2001), we classified the samples in our primary studies according to the following dimensions: uncertainty avoidance, collectivism, and long-term orientation. A high uncertainty avoidance means the culture has strict rules and regulations to minimize the unknown. A culture with high collectivism places importance on the group as opposed to a focus on the individual well-being. A high long-term orientation refers to cultures who focus on the future and emphasize long-term growth.

Methodological Moderators

Family involvement. This moderator concerns the type of family involvement distinguishing between family ownership, family management and other family involvement. The former concerns effect sizes where family involvement is measured through the shares in the firm held by an owner family; the latter refers to the involvement of the family in the management of the firm. The category “other family” firm definition was used when effect sizes are measured by other family involvement measures (e.g., family control in the supervisory board). Next to these three conceptual involvement measures, we follow O’Boyle et al. (2012) and distinguish between whether multiple criteria were used and/or whether the measure was self-reported or not.

Publication status. We distinguished between effect sizes from published and unpublished papers. We further tested for potential differences in publication year and journal quality (see O’Boyle et al., 2012).

Meta-Analytical Procedure

We employed three-level random and mixed effects models (Cheung, 2019) to account for the existence of multiple effect sizes per study. Whereas traditional (two-level) random effects models estimate a mean effect size plus a systematic variance component (in addition to sampling error variance), three-level models add a third variance component that reflects differences across multiple effect sizes within studies. By doing so, the procedure addresses that multiple effect sizes within studies are nested and, hence, not statistically independent. Three-level models are an improvement over traditional methods, for instance, to create averages per study as these reduce the number of analyzed effects sizes considerably. Three-level models, in contrast, keep all available information, enabling the investigation of the extent of heterogeneity within (and between studies), while addressing the non-independence. Technically, the random effects model is a simplified version of a regression model without predictors:

y_{ij} = β_{0} + u {(2)}_{ij} + u {(3)}_{j} + ε_{ij}

(1)

with y_ij representing the effect size behavior in study j–in our case the relationship between FF status and some environmental impact variable. The intercept β₀ reflects the weighted average effect size, Var(u(2)_ij) = τ²(2) reflects the true within-study variance, Var(u(3)_j) = τ²(3) represents the true between-study variance and Var(ε_ij) reflects a random sampling error. We report the square root of both tau-squares, representing an estimate of the true (i.e., non-random) heterogeneity in the form of an easily interpretable standard deviation of the effect size. For reasons of comprehensibility, we will refer to the within-study heterogeneity as τ_w and for the between-study heterogeneity as τ_b.

When analyzing moderators, the model can be easily extended to a mixed effects regression model by including the moderators in the form of dummy variables as predictors. Mixed effects models, thus, allow adding potential explanatory variables that may predict some portion of the between-study or within-study variance.

y_{ij} = β_{0} + β_{1} x_{j} + u {(2)}_{ij} + u {(3)}_{j} + ε_{ij}

(2)

In this model, one or several predictors x_j are added as moderators. The model was a mixed effects model excluding the intercept β₀. As a result, the coefficients of the model (the β₁s) represent the weighted average in each category of the moderator. To test for the effects of specific moderators, we included the intercept, resulting in β₀ expressing the mean of the reference category and β₁ indicating the average difference between both levels and its significance. Moderators indicating mixed categories were included in the model to increase power and precision of the estimates. For comprehensibility reasons, the following tables, however, do not contain the estimates referring to these categories.⁴

Results

Table 1 shows the results referring to the relationship between FF status (FF vs. non-FF) and environmental sustainability performance. As the table indicates, we do not find any evidence for differences between FFs and non-FFs regarding their overall environmental sustainability performance ( $\hat{ρ}$ = −.013, p = .34). However, we find that FFs seem to have a lower environmental footprint than non-FFs ( $\hat{ρ}$ = −.055, p < .01), but no difference seems to exist regarding environmental handprint ( $\hat{ρ}$ = −.017, p = .56). We also find that FFs have a lower likelihood for sustainability action reducing the firm’s negative impact on the natural environment ( $\hat{ρ}$ = −.045, p = .05). Thus, FFs and non-FFs are remarkably similar regarding their overall environmental sustainability performance but differ with regard to their environmental footprint and their environmental sustainability actions.

Table 1.

Relationship Between FF Status and Environmental Sustainability.

Category	k	m	N	$\hat{ρ}$ (SE)	p	CI_lb	CI_ub	τ_w (I²_w)	τ_b (I²_b)	Publication bias	Outlier correction
Environmental sustainability performance	87	292	118,538	−.013 (.01)	.342	−.04	.01	.05 (.16)	.11 (.78)	No	Yes ( $\hat{ρ}$ = −.02, p = .11)
Environmental sustainability action	53	159	86,703	−0.022 (0.02)	.253	−.06	.02	.05 (.11)	.13 (.83)	No	Yes ( $\hat{ρ}$ = −.02, p = .12)
Reducing the firm’s negative impact	39	94	50,958	−.045* (0.02)	.048	−.09	.00	.05 (.14)	.12 (.8)	No	Yes ( $\hat{ρ}$ = −.04*, p = .002)
Offsetting the firm’s negative impact	13	58	8,501	.012 (0.03)	.721	−.05	0.08	.05 (.13)	.10 (.67)	No	Yes ( $\hat{ρ}$ = .01, p = .65)
Environmental innovation	5	7	28,471	.072* (.03)	.037	.00	0.14	.00 (.00)	.07 (.96)	No	Yes ( $\hat{ρ}$ = .04, p = .15)
Environmental sustainability outcome	12	36	25,060	.021 (.02)	.264	−.02	0.06	.03 (.26)	.05 (.65)	No	No
Environmental handprint	6	19	11,045	−.017 (.03)	.563	−.07	0.04	.04 (.33)	.05 (.61)	No	No
Environmental footprint	8	17	14,928	−.055** (.01)	.000	−.08	−0.03	.01 (.02)	.03 (.63)	No	No
Other environmental sustainability measure	35	97	25,752	−.024 (.02)	.180	−.06	.01	.05 (.23)	.09 (.67)	No	Yes ( $\hat{ρ}$ = −.03*, p = .04)

Note. k = number of independent samples; m = number of effect sizes; N = number of analyzed firms; $\hat{ρ}$ = weighted average effect size; CI_lb and CI_ub = lower and upper bound of a 95% confidence interval; τ_w and τ_b = estimate (standard deviation) of the true heterogeneity within and between studies; I²_w and I²_b = percentage of true heterogeneity in observed heterogeneity.

It should be noted that the ks across the sub-categories do not add to the overall number of studies as several studies had analyzed more than one outcome. Likewise, the ms do not add up to the overall number of effect sizes as some studies had employed an overall measure of sustainability that contributed to the overall category but could not be used in the more specific category. Finally, we performed separate analyses for sustainability competencies, sustainability disclosure, sustainability goals, and sustainability strategy; the results were not significant.

p < .05, **p < .01.

Our analysis shows a substantial degree of between-study heterogeneity (i.e., the τ_b’s) indicating the existence of potential moderator effects. Table 2 displays the results for the influence of our main conceptual and methodological moderators. First, we tested the impact of family involvement. For family ownership, we found a tendency for a negative effect on overall environmental sustainability performance ( $\hat{ρ}$ = −.02, p = .10). The effect of family management is not significant. Nevertheless, the difference between these two types of family involvement is significant (z = −2.54*, p = .01), pointing toward a moderation effect. Further, we tested for the impact of firm size. The results reveal a significant negative effect of large FFs ( $\hat{ρ}$ = −.037, p = .04), and a significant positive effect of SME FFs ( $\hat{ρ}$ = .075, p < .01) regarding their overall environmental sustainability performance. The difference between these relationships is significant (z = 3.48, p < .001). Furthermore, the results show a significant negative effect of large FFs regarding overall sustainability actions ( $\hat{ρ}$ = −.074, p = .003), whereas SME FFs seem to have a higher level of overall sustainability actions ( $\hat{ρ}$ = 0.1, p = .004) compared to non-FFs. Again, the difference between these relationships is significant (z = 3.98, p < .001). It should be emphasized that these differences concern the moderation of the effect of FF status on environmental impact by firm size—not differences in environmental impact between large versus small firm samples.

Table 2.

Influence of FF Heterogeneity on Environmental Sustainability.

Moderator	k	m	N	$\hat{ρ}$ (SE)	p	CI_lb	CI_ub	Difference tests
Overall environmental sustainability performance
Family involvement								z = −2.54*, p = .01
Family management	24	80	30,029	.005 (0.02)	.777	−.03	.04
Family ownership	66	167	97,569	−.024 (0.01)	.102	−.06	.00
Firm size								z = 3.48**, p < .001
SMEs	18	92	38,623	.075** (0.03)	.005	.02	.13
Large firms	47	117	35,147	−.037* (0.02)	.037	−.07	.00
Firm type								z = −2.44*, p = .02
Private firms	20	84	44,444	.042 (0.03)	.116	−.01	.09
Public firms	48	92	35,467	−.037* (0.02)	.042	−.07	.00
Environmental sustainability action
Family involvement								z = −2.02*, p = .04
Family management	13	44	19,563	.002 (0.02)	.916	−.04	.05
Family ownership	40	85	75,012	−.027 (0.02)	.178	−.07	.01
Firm size								z = 3.98**, p < .001
SMEs	11	41	32,809	.100** (0.04)	.004	.03	.17
Large firms	25	41	19,158	−.074** (0.02)	.003	−.12	−.03
Firm type								z = −2.91**, p = .00
Private firms	13	46	41,992	.053 (0.03)	.123	−.01	.12
Public firms	26	42	19,478	−.072** (0.03)	.005	−.12	−.02
Environmental sustainability outcomes
Family involvement								z = −0.90, p = .37
Family management	5	7	5,352	.017 (0.03)	.515	−.03	.07
Family ownership	8	24	14,735	−.007 (0.02)	.740	−.05	.03
Firm size								z = 0.02, p = .99
SMEs	2	5	807	.038* (0.02)	.090	−.01	.08
Large firms	7	16	11,984	.038 (0.04)	.388	−.05	.12
Firm type								z = −0.00, p = .99
Private firms	3	6	3,784	.041 (0.03)	.193	−.02	.10
Public firms	7	16	11,984	.041* (0.02)	.041	.00	.08

Note. k = number of independent samples, m = number of effect sizes; N = number of analyzed firms; $\hat{ρ}$ = weighted average effect size; CI_lb and CI_ub = lower and upper bound of a 95% confidence interval. Differences were tested with a z test.

p < .05, **p < .01.

Table 2 also displays the results for firm type. We find significant effects for public FFs—that is a lower level of overall sustainability performance ( $\hat{ρ}$ = −.037, p = .04), and a lower level of sustainability action ( $\hat{ρ}$ = −.072, p = .003). The differences between the respective categories (public vs. private firms) in these relationships are significant (z = −2.44, p = .02; z = −2.91, p < .01, respectively).

As robustness checks and further analyses, we tested for potential moderator effects of national culture (Peng & Lin, 2009), FF operationalization, industry characteristics (high- vs. low-polluting)⁵ and publication status (see Tables A4 and A5 in the online appendix, https://osf.io/ypkna). National culture and FF operationalization show no significant moderating effects. For industry characteristics and regarding overall environmental sustainability performance, we neither find significant FF effects in high-polluting nor in low-polluting industries. We also do not find evidence for industry-related moderation effects. The same picture emerges for environmental sustainability actions. The results are different for environmental sustainability outcomes and reveal a significantly positive FF effect for high-polluting industries ( $\hat{ρ}$ = .058, p < .001), and a significantly negative FF effect for low-polluting industries ( $\hat{ρ}$ = −.100, p < .001). Even though in line with our arguments, this result should be interpreted with great caution as the number of studies (e.g., k = 1 for low-polluting industries) and effect sizes related to environmental sustainability outcomes is too small to draw meaningful conclusions. In a further robustness check, we ran a meta regression and regressed the FF effect on the percentage of firms in a sample from high-polluting industries and found no relationship. Overall, our investigations show little evidence for industry selection and/or moderation effects. The presence of a publication bias was investigated by two means. First, we conducted both the test for funnel plot asymmetry by Egger et al. (1997) and the rank test by Begg & Mazumdar (1994) and considered publication bias to be present in case both tests were significant. Second, we coded whether a study was published in a peer-reviewed journal or an unpublished manuscript, dissertation, or student thesis and regressed the effect sizes of the studies on the binary publication status variable in a univariate three-level model. Due to the opposite directions of the effect sizes of the different variables, this was done separately. We find little evidence for a publication bias regarding published versus unpublished studies. Next, we tested for the publication year and the quality of the journal according to its Scientific Journal Ranking (SJR) quartile. No significant effects were found. Last, to investigate the presence and influence of outliers, we used a combination of the studentized residuals and their Cook’s distance (Viechtbauer & Cheung, 2010). In case of outliers, we repeated the three-level model and report the changed coefficient and its p value in the last column of Table 1.

Discussion

Summary of Main Results

Our research note investigates the environmental sustainability performance of FFs using a meta-analytical approach. We extend the meta-analysis of Miroshnychenko et al. (2022) by using a substantially larger sample of primary studies (87 vs. 26 studies), by distinguishing between the environmental hand- and footprint, and by accounting to a greater extent for FF heterogeneity. Our results reveal no difference regarding the overall sustainability performance of FFs and non-FFs. However, differences emerge when distinguishing between the environmental hand- and footprint of firms. Specifically, FFs have a lower environmental footprint than non-FFs, but they do not differ regarding their environmental handprint. Regarding sustainability actions, we find that FFs are less likely to engage in actions that reduce their negative environmental impact. Additionally, within the group of FFs, we find significant heterogeneity. The effect of FFs on environmental sustainability performance is negative for large and stock-market listed firms, but positive for SMEs. Furthermore, in line with Miroshnychenko et al. (2022) we find that family ownership tends to negatively affect overall environmental sustainability performance, whereas the effect of family management seems to be insignificant.

Implications for Theory

Our research note holds significant theoretical implications for research on the environmental sustainability performance of FFs. By distinguishing between the environmental hand- and footprint of FFs (Guillaume et al., 2020; Hoekstra & Wiedmann, 2014), we introduce an important concept from the sustainability literature into research on FF sustainability. By showing that FFs differ from non-FFs regarding environmental footprint and not regarding handprint, our study contributes to research about the motivations and drivers of FF environmental sustainability (Flores-Rivera et al., in press; Sharma & Sharma, 2011). It seems that FFs care more than other firms about avoiding a negative reputation, which drives their (sustainability) behavior (Berrone et al., 2010; Deephouse & Jaskiewicz, 2013; Dyer & Whetten, 2006).

Our findings about the environmental hand- and footprint of FFs also relate to a discussion on symbolic and substantive CSR and sustainability strategies of FFs (Schumacher, 2022). Symbolic CSR describes activities that address stakeholder concerns only in a symbolic way, either by communicating positive activities without taking meaningful actions or implementing activities that are not clearly linked to a specific concern. In contrast, substantive CSR describes holistically altered processes and activities to positively impact the environment in the long term (Combs et al., 2022). Our finding that FFs care primarily about having a low environmental footprint is in line with the notion that they follow substantial CSR rather than symbolic CSR strategies. Following Schumacher (2022) and Combs et al. (2022), FFs are guided in their CSR strategy by their long-term relationships and close social ties with important stakeholders rather than by short-term reputational gains. Somewhat surprisingly, our results show that FFs seem to employ fewer activities reducing their negative impact on the natural environment. Hence, while they have a lower footprint as an outcome, they also engage in less activities to achieve this outcome. This, at first sight, somewhat paradoxical result, could be explained in two ways. The first explanation would be that FFs select themselves into low-polluting industries, where less sustainability activities are needed to achieve a low environmental footprint. A tentative empirical analysis correlating the share of family firms in a study sample with the share of firms from polluting industries does, however, not yield a significant result. Moreover, we do not find evidence for differences in the FF effect across high- and low-polluting industries (see robustness checks section above). The second explanation is that FFs are able to achieve the same or even a lower level of environmental sustainability with less sustainability activities. They may focus to a greater extent on substantive rather than symbolic sustainability activities and use their resources in a more effective way than non-FFs, which leads to a lower footprint. This interpretation would regard FFs as being able to achieve more (output) with less (input) (Block et al., 2023).

Our research note also advances the discussion about the heterogeneity within the group of FFs (Dyer, 2018; Miller & Le Breton-Miller, 2021). Our results support the notion of FFs being a heterogeneous group. Factors such as firm size, stock market listing, and type of family involvement emerge as significant moderators when assessing the impact of FFs on the natural environment. Specifically, the positive effect of SME-FFs on environmental sustainability performance can be explained by their strong local embeddedness and visibility, which makes them particularly concerned about avoiding a negative (environmental) reputation (Baù et al., 2019; Zellweger et al., 2013). While SMEs are often associated with lagging behind regarding environmental sustainability practices due to resource constraints or a lack of specific knowledge and experience (Johnson & Schaltegger, 2016), their status as FF may make them in fact more sensitive toward stakeholder concerns about environmental sustainability. Prior research argues that small- and mid-sized FFs often have close relationships with local stakeholders beyond the business context increasing the salience and urgency of their claims and perceived legitimacy (Lähdesmäki et al., 2019, Mitchell et al., 2011). Large FFs show in our study a negative association with environmental sustainability performance. One possible interpretation, grounded in socioemotional wealth and stakeholder theory considerations is that the owners of large family firms are no longer as strongly identified with their FFs as the owners of small FFs are. Moreover, they may have lost the close social proximity to local stakeholders. Consequently, they care less about the reputation of their firms and are less responsive to demands from local stakeholders. Moreover, many large FFs also have non-family owners or are publicly listed on stock markets, which is shown to impact FF behavior (Carney et al., 2015). This explanation aligns with our finding of a negative effect of public FFs on environmental sustainability performance. In summary, our research shows how firm size and stock market listing impact the environmental sustainability performance of FFs, which contributes to a better understanding of the heterogeneity that exists within the group of FFs regarding environmental sustainability.

Finally, in line with Miroshnychenko et al. (2022), we find that the type of family involvement matters. Family ownership tends to have a negative effect on the overall environmental sustainability performance, while the influence of family management is insignificant. An interpretation would be that in their desire to protect the family’s financial wealth, family owners avoid investments in (high-risk and uncertain) environmental sustainability projects (Chrisman & Patel, 2012). The insignificant effect of family management connects our research note to a broader discussion on the (dis)advantages of family management (for a summary see, Hiebl & Li, 2020). Typically, this discussion has revolved around the economic implications of family management. However, by focusing on environmental sustainability as an outcome variable, our study brings a new perspective to this important and growing literature stream in FF research. In this regard, our study also connects to recent research by Kragl et al. (2023), who study in a multi-task setting the role of family managers in pursuing sustainability goals.

Implications for Practice

The results of our research note have practical implications for FFs and policy makers. FFs and the entrepreneurial families behind them care about their reputation and image, which seems to be a motivation for a lower environmental footprint. Policy makers should thus aim for full transparency about a firm’s environmental footprint and (small) FFs should not be excluded in this regard. FFs seem also to be particularly cautious regarding investments into activities reducing their negative impact on the environment. This suggests that governmental initiatives reducing the costs and uncertainties regarding environmental activities could have a stronger effect for FFs as compared to non-FFs. FFs, in turn, should carefully evaluate the possibilities to reduce the risks associated with sustainability activities by working together with other firms or organizations. They may also want to communicate their overall lower footprint to (potential) customers and other stakeholders to increase their reputation for environmental sustainability.

Limitations and Future Research Directions

A number of data limitations exist setting the (boundary) conditions under which our results have to be interpreted. An important limitation of our study is that the sample of primary studies with footprint outcomes is still small. Hence, we call for further research that analyses the actual footprint outcomes of FFs. Given the increased demand from stakeholders and regulators for environmental accountability and transparency (including small and private firms), and an increase in firms’ requirements to report their sustainability performance, we are confident that the number of such studies will eventually grow, also distinguishing between different types of environmental footprints such as carbon, water, waste, and energy footprint. By that, big data and artificial intelligence/machine learning approaches, such as ISTARI.AI or Chatclimate.ai,⁶ will also find their way into FF sustainability research offering new ways of measuring environmental hand- and footprints, ultimately increasing the number of primary studies available for a meta-analysis. In a related manner, big data approaches have been set up to detect environmental claims made by firms (see Stammbach et al., 2022, for a description of such a database).

A further limitation concerns the lack of primary studies from developing countries. The samples/primary studies of our meta-analysis stem only from a few countries (~75%) in Europe, Asia, and the Americas, which prevents us from comparing the effects of FFs on environmental impact across larger sets of countries or geographical and cultural regions that differ in type and extent of environmental regulation. Future research should investigate such regional, cultural, and institutional influences on the relationship between FFs and environmental sustainability. To illustrate, one might expect that environmental regulation influences the environmental impact of FFs and that this influence might be different as compared to non-FFs. As more primary studies about FFs environmental sustainability from countries with fewer environmental regulations become available, future meta-analytical research could investigate the role of environmental regulation and institutional differences on the effect of the FF status on environmental sustainability performance. Finally, a larger set of primary studies would also allow to make more fine-grained investigations into the heterogeneity of family firms regarding environmental sustainability. One might expect that family succession or the transition from a lone founder firm to a FF (Miller et al., 2011) can change the importance of environmental sustainability in FFs.

Apart from these limitations due to the lack of primary studies, future research should focus more on the how and why questions when analyzing the environmental impact of FFs. While the overall empirical evidence behind our findings is strong, the interpretation of our findings can only be regarded as tentative. More (qualitative) research is needed that evaluates both external factors, such as external barriers and enablers of FFs engagement in environmental hand- and footprint activities, as well as internal factors, such as the values or motivations of the employees and managers of FFs, which influence their engagement in environmental hand- and footprint activities. Our results suggest that a distinction between outcomes and actions and between positive and negative environmental effects could help to gain a better understanding of the environmental impact of FFs. Further, future research needs to control for potential confounding variables influencing the relationship between family status and sustainability performance. Whereas we provide some first insights on the effects of industry characteristics, firm size and national culture, further research should include additional confounding variables such as firm age. This would ensure a more robust analysis and minimize the influence of potential extraneous factors.

Conclusion

Our research note shall motivate and inspire family business and sustainability research to apply more fine-grained perspectives distinguishing between different types of environmental sustainability actions and impact and accounting for FF heterogeneity. Such more fine-grained distinctions make it possible to draw concrete conclusions for policymakers and practitioners ultimately increasing the relevance and impact of FF research.

Supplemental Material

sj-pdf-1-etp-10.1177_10422587231221799 – Supplemental material for Environmental Sustainability of Family Firms: A Meta-Analysis of Handprint and Footprint

Supplemental material, sj-pdf-1-etp-10.1177_10422587231221799 for Environmental Sustainability of Family Firms: A Meta-Analysis of Handprint and Footprint by Solvej Lorenzen, Maike Gerken, Holger Steinmetz, Joern Block, Marcel Hülsbeck and Friederike Sophie Lux in Entrepreneurship Theory and Practice

Footnotes

Correction (August 2024):

Article type has been changed from “Replication Brief” to “Original Article” after the online publication of the article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Solvej Lorenzen

Maike Gerken

Holger Steinmetz

Joern Block

Supplemental Material

Supplemental material for this article is available online.

Data Availability Statement

We uploaded all data, codes, and analyses presented in this manuscript anonymously to the Open Science Foundation. The files and the online appendix can be accessed here:

Notes

Author Biographies

Solvej Lorenzen is a Doctoral Candidate at the Witten Institute for Family Business (WIFU), Witten/Herdecke University, Witten, Germany. She received a scholarship from the WIFU-Foundation.

Maike Gerken is a Postdoc and Habilitation Candidate at the Witten Institute for Family Business (WIFU), Witten/Herdecke University, Witten, Germany.

Holger Steinmetz is a Researcher at the University of Trier, Trier, Germany.

Joern Block is a Full Professor at the University of Trier, Trier, Germany and Affiliated Professor at the Centre for Family Entrepreneurship and Ownership (CeFEO), Jönköping International Business School, Jönköping, Sweden and at the Witten Institute for Family Business (WIFU), Witten/Herdecke University, Witten, Germany.

Marcel Hülsbeck is a Full Professor at the University of Applied Sciences Munich, Munich, Germany.

Friederike Sophie Lux is a Doctoral Candidate at the Witten Institute for Family Business (WIFU), Witten/Herdecke University, Witten, Germany.

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