From Toxic to Transparent: The Effect of Greenpeace’s Detox Campaign on Market Volatility

Abstract

In the contemporary structure of political economy, one of the leading actors is Non-Governmental Organisations (NGOs). Some of these organisations, to promote their goals, often engage in public disputes with enterprises that have publicly traded shares on the stock market. Consequently, they serve as channels for negative information relevant to these enterprises that falls within their discourse. In this paper, we examine the impact on the share price volatility of these enterprises due to the public debate initiated by an NGO aiming to change the enterprise’s behaviour on a particular matter (e.g., using more eco-friendly materials). Data from Greenpeace’s Detox Campaign are used to examine its influence on several enterprises. Using GARCH, OLS, and Difference-in-Differences models, we find that volatility increased significantly during the campaign for firms like Burberry (13.71%), Adidas (5.40%), and VFC Group (3.96%). After companies complied, volatility declined, notably in Burberry (−16.84%), Marks & Spencer (−3.24%), and VFC Group (−4.88%). These results highlight how NGO activism can heighten investor uncertainty in the short term but stabilise markets once companies respond, offering key insights for policymakers on the financial impact of civil Society’s engagement.

1. Introduction

Non-Governmental Organisations, or NGOs, have been gaining an increasingly important role in the world over the past few decades and are therefore a subject of growing academic inquiry (Ferguson, 2012). The history of NGOs begins with the League of Nations in 1919, when they were defined as any international organisation that was not formed by treaty or of a governmental character. Instead, NGOs exist to work directly with the people, as opposed to states. In 1945, the United Nations was founded, and the definition of NGOs was expanded to include international organisations working with states, while maintaining the existence of NGOs working independently of states (Murazzani, 2009). Currently, the NGOs commonly recognised and analysed have either a local, national, or international status; however, more research is needed to gain a better understanding of them (Kruse & Martens, 2015).

More specifically, ‘a non-governmental organisation (NGO, also often referred to as ‘civil society organisation’ or CSO) is a not-for-profit group, principally independent from government, which is organised on a local, national or international level to address issues in support of the public good (UN law website). To better understand the scope of NGOs, a definition of the term is necessary. NGOs have been defined as organisations that aim to better the world, precisely where the world has failed to repair itself, particularly in the political arena (Wang, 2024). It is thus helpful to demarcate NGOs from the government, which they are not, as well as from corporations whose main objective is not to better the world. NGOs focus their attention on either social or environmental issues, or on social and environmental issues that have a close link with economic activities, study the intricate links between so-called sovereign states and economic globalisation, and take a close look at the market’s reach and possible power over the majority of people around the globe. They may adapt their objectives and their organisational structure to the context in which they are working (Henry & McIntosh Sundstrom, 2021). These organisations have gained considerable power due to two main reasons:

• Firstly, the information boom enabled these organisations to collect, analyse, and disseminate information effectively, allowing them to contest the market and the state.
• Secondly, the position they held in Society led most NGOs to concentrate on legitimacy due to the moral authority that prevails in Western Societies (Chandhoke, 2002).

This kind of moral legitimacy stems from the ethical stance these organisations represent, as they generally promote widely accepted ideas on how the world should operate. However, these ideas are sometimes not pervasive in the states where NGOs operate. Thus, they influence political activism in countries that lack relevant political debate or an appropriate legal framework. In addition, it should be noted that often NGOs exert pressure on a supranational level, influencing international organisations to take action against businesses that operate on a regional or global scale.

The effectiveness of these organisations is contested. Even though they have recently gained empowerment, civil Society still faces difficulties in influencing regulatory agencies and supranational institutions. Thus, the co-existence of the market and the NGOs calls for trust and cooperation rather than continuous confrontation (Fukuyama, 1995). As it is argued by Chandhoke (2002, p. 36), ‘civil society and the market, we realise, are dependent on each other; they need not provide an alternative to each other at all.’

Nowadays, the most common trend includes having NGOs monitoring firms rather than sovereign states (Tang et al., 2023). As a result, when these organisations demand reform, they use consumer boycotts to exert political pressure (Chandhoke, 2002). These organisations attempt to find the most effective way to apply the tactic of “blaming and shaming” by disseminating evidence of an organisation’s illegal or unethical behaviour. To achieve this goal, an NGO can employ tactics such as letter-writing campaigns, picketing, shareholder resolutions, boycotts, and other means that convey information (David et al., 2007; Soule, 2009). To attract media attention, NGOs employ a range of strategies. On the one hand, they criticise and put internal and external pressure on companies and their existing and potential shareholders. By doing this, they confront resistant companies with unilateral campaigns. The most recent means of disseminating a ‘blaming and shaming’ message is by employing ‘social networks’. Most NGOs utilise these networks to disseminate information about their campaigns. Due to the widespread use of social networks by millions of citizens worldwide, this modern method of disseminating news requires closer examination in terms of its effectiveness.

The policies of corporate social responsibility (CSR) are shaping the activities of companies. Consumers perceive them as a signal of quality or good behaviour (Grappi et al., 2017). The correct application of CSR policies provides competitive advantages to firms actively engaged in environmental protection, and it gives investors confidence in the long-term perspective of these enterprises. On the contrary, erratic or contradictory behaviour reduces investor confidence and can result in stock price crashes (Chiu & Kolstad, 2024). The same effect can also be caused by the pressure exercised by pro-environment NGOs on enterprises. Greenpeace, through its Detox campaign, exerts institutional pressure on polluting enterprises and encourages them to clean up their operations. These campaigns employ strategies such as symbolic politics and leverage politics to influence both public perception and corporate behaviour (Coombs & Holladay, 2015).

Different studies reveal a loss in market value for enterprises boycotted by NGOs (Spicer, 1978; Pruitt & Friedman, 1986; Epstein & Schnietz, 2002). However, there is evidence that in some cases such campaigns by NGOs increased the market value of the firms (Mahapatra, 1984; Koku et al., 1997). Generally, good environmental performance by a firm is accompanied by better economic performance (Konar & Cohen, 2001; Gupta & Goldar, 2005). In addition, Klassen and McLaughlin (1996) found significant negative abnormal returns for firms that suffered from negative environmental news. The same conclusion was drawn by Hamilton (1999) as well.

More specifically, our research will focus on the impact that campaigns, conducted through social networks, have on the share prices of the firms under attack. Environmental campaigns influence financial markets through investor reactions to environmental information and regulatory changes. Releases of environmental information, such as pollution data, impact stock prices and market perceptions. Regulatory actions, such as the Clean Air Act amendments, affect the stock performance of companies, with implications for the market valuation of firms’ environmental performance. Firms’ environmental practices and disclosures are recognised by investors, potentially affecting their market value. Reputational penalties for violations and the adoption of environmental policies play significant roles in shaping financial outcomes, highlighting the interplay between environmental initiatives and market performance (Oberndorfer & Ziegler, 2006).

Share prices represent the best estimate of the present value, taking into account all relevant information, including future profitability of the firm (Fama, 1970). Public campaigns affect the financial markets by providing information about a scenario and drawing investor attention. A supportive or opposing attitude towards this scenario affects market prices, as the voice of the campaigners on different themes influences the price dynamic. The effect is more pronounced when a price rally occurs (Ma & Cheok, 2023). Griffin and Mahon (1997) found that even formal activism at a higher level has a minimal effect on the share of firms that are under the scrutiny of NGOs.

Nevertheless, Epstein and Schnietz found that firms have ‘reputational penalties’ due to bad environmental performance. These firms suffer from losses to their share price. Many scholars argue that negative news about a firm’s environmental performance is often associated with declines in stock values (Muoghalu et al., 1990; Hamilton, 1995; Klassen & McLaughlin, 1996). On the contrary, others found that this kind of bad news has an insignificant impact on the share price (Laplante & Lanoie, 1994; Jones & Rubin, 2001). Similarly, other studies have examined the sensitivity of share prices to specific types of news on environmental performance, confirming that firms with poor environmental performance suffer losses in their share prices (Arora, 2001; Gupta & Goldar, 2005).

This research focuses on the Detox Campaign of Greenpeace and its effects on the volatility of stock prices. The Greenpeace Detox Campaign, initiated in 2011, aims to eliminate hazardous substances from leading clothing brands and industry-wide supply chains. Since its inception, numerous major companies have committed to phasing out toxic chemicals and disclosing their progress on the campaign’s website. These developments have notably caught the attention of investors, prompting investigations into the campaign’s impact on stock prices and market performance. Given the campaign’s global reach and the potential financial implications for publicly traded manufacturers, a timely review of the existing literature on its impact on stock markets is warranted.

Greenpeace exerted political influence on fashion brands to shape industry policies regarding water pollution in China. They targeted fashion brands to eliminate hazardous chemicals from their textile products, which affected Chinese textile factories serving as suppliers. Greenpeace collected documents demonstrating business commitments to remove harmful chemicals and published them as evidence of advocacy. Currently, over 30 textile companies, including global brands and manufacturers, have committed to supporting Greenpeace’s Detox campaign, and 18 firms representing 10% of the fashion industry have initiated the elimination of hazardous substances.

Previous studies investigating Greenpeace campaigns have found that the stock market time series of targeted companies are affected following the publicisation of NGO information, but with a relatively short-lived impact (Oberndorfer & Ziegler, 2006). Similarly, an in-depth analysis of the Greenpeace Detox Campaign’s impact on fashion-producing firms broadly supports the notion that it has had no lasting effect on the stock market of publicly traded companies. By contrast, consumer-goods firms that face multiple Greenpeace campaigns and operate within more controversial industries are vulnerable to a significant market reaction (Grappi et al., 2017).

While many studies have explored the effects of such activism on stock returns, less attention has been given to how these events influence stock price volatility, a critical component of market risk. This paper aims to find whether and how NGO activity causes stock price uncertainty. Brendel et al. (2024) examined 1212 allegations of NGO-led ESG-washing against 287 public firms between 2011 and 2022. They found an average abnormal return of −0.34% over a three-day window around the campaign announcement. While not directly measuring volatility, the short-term negative returns suggest a spike in perceived risk and uncertainty. Ramelli (2021) and Schuster (2023) examined the global and European stock market reactions to climate strikes organised by “Fridays For Future”. Both found significant negative abnormal returns for high-emission or ESG-exposed firms, implying heightened investor uncertainty. Dupire et al. (2022) demonstrated that negative tweets about S&P500 firms by NGOs caused abnormal stock returns, reflecting information shocks that are likely to increase volatility.

Given the role of NGOs in influencing corporate behaviour, particularly through public environmental campaigns amplified by social media, this study aims to understand the extent to which such activism affects stock price volatility. By examining the Greenpeace Detox Campaign and its impact on publicly listed companies, the research studies how such campaigns affect investor behaviour and contribute to financial uncertainty. This focus on volatility, rather than solely on stock returns, provides a more nuanced understanding of how environmental advocacy shapes market perceptions and firm risk in a globally connected and information-driven economy.

2. Data

The database contains data on share prices for 19 companies, obtained from the Thomson Reuters DataStream database. Moreover, we use the S&P500 as a control group in the methodological section for comparison purposes. While Greenpeace publicly targeted individual firms at different stages following the launch of the Detox campaign, we adopt July 2011, the date of the campaign’s official announcement, as the uniform treatment onset for all firms in the sample. This approach is grounded in the sector-wide framing of the campaign from its inception, which was explicitly aimed at transforming the global apparel and fashion industry. Employing a common treatment start date facilitates comparability across firms and captures the aggregate market reaction to the campaign’s initiation.

To account for heterogeneity in firm-level responses, we incorporate company-specific compliance dates to demarcate the transition from the campaign period to the post-compliance phase. This enables us to estimate the effects of the Detox campaign not only during the period of sustained NGO pressure but also in the aftermath of formal corporate commitments to Detox objectives. By structuring the data in this way, we can examine how stock price volatility evolves both in response to activist scrutiny and following the resolution of reputational and operational uncertainty associated with compliance.

The sample comprises weekly panel data covering the period from the first week of 2011 to the seventh week of 2015. Data on the Detox campaign are limited until 2015, as the Greenpeace Detox My Fashion campaign underwent a major strategic shift in 2015, marking a turning point in its approach. After four years of mobilising public pressure and successfully securing commitments from major global fashion brands, Greenpeace transitioned the campaign from a recruitment phase to an implementation and accountability phase. This change meant moving away from simply getting brands to sign the Detox Commitment and instead focusing on whether those brands were fulfilling their promises. The new phase emphasised supply chain transparency, wastewater testing, and the elimination of 11 groups of hazardous chemicals from wet processing facilities. Greenpeace also began pushing for greater public disclosure—such as publishing supplier lists and wastewater data—and scrutinised how brands were substituting hazardous chemicals with safer alternatives (Greenpeace, 2018).

The NGO employed in this paper is Greenpeace, and data about their campaigns are collected from their published reports. These reports are available for free on their website. The campaign used in this paper is the Detox campaign, and the enterprises we are using are directly related to the clothing industry. These enterprises include Abercrombie and Fitch, Adidas, ABF Group, Blackstone, Burberry, Dior, GAP, H&M, Inditex, L-Brands, Louis Vuitton, Marks & Spencer, Nike, Puma, PVH Group, Ralph Lauren, VFC Group, and Youngor.

The selection of firms included in the sample is based on their explicit involvement in Greenpeace’s Detox campaign. Greenpeace publicly named these companies in their reports as part of a targeted effort to eliminate the use of hazardous chemicals in the global clothing supply chain. Therefore, the firms in our sample were not chosen based on geographic location or market capitalization, but because Greenpeace directly challenged them to commit to Detox-related reforms. Despite this, the sample remains geographically diverse, including firms headquartered in North America (e.g., Nike, GAP, Abercrombie & Fitch), Europe (e.g., Adidas, H&M, Burberry, Inditex), and Asia (e.g., Fast Retailing, Youngor). Furthermore, all selected firms had sufficient weekly share price data available in the Thomson Reuters DataStream database for the whole study period. Firms not publicly named by Greenpeace or lacking complete data coverage were excluded from the sample.

Furthermore, we utilize data from Google Trends on seven different words or combinations of two words directly related to the Detox campaign to estimate the potential impact on share price volatility. Google Trends is an index that displays the volume of Google searches and queries, which are provided on a daily and weekly basis (Choi & Varian, 2012). These words are retrieved by using the text data mining process. This process allows us to collect information about the frequency of the words that appear in the additional Greenpeace reports (Coussement & Van Den Poel, 2008).

The words and pairs of words we are using after the text data mining process are Detox, Hazardous Chemicals, Hazardous Substances, Organic Chemicals, Toxic Water, Water Pollution, and, of course, Greenpeace. The Detox campaign began in July 2011 and was based on Greenpeace reports. Google Trends data have been normalised to an interval from zero (0) to one (1). We also set two dummy variables. The first dummy variable indicates the period of the Detox campaign until the period that the enterprises complied with Greenpeace’s requirements for being free of toxic clothes. This variable takes the value 0 for the period before the Detox campaign and 1 for the period during the Detox campaign. The dummy variable remains 1 for enterprises that do not comply with the regulation. The second dummy variable indicates the period after the Detox campaign for the enterprises that have complied with Greenpeace’s requirements for being free of toxic clothes. This variable takes the value 0 for the period during the Detox campaign and 1 for the period after the Detox campaign.

The aim of this paper is twofold. First, we test the hypothesis of whether the period of the Detox campaign and Google Trends have affected the volatility of share prices. Second, we aim to identify any potential differences in share price volatility between the Detox campaign period and the subsequent period.

The detailed sources for all variables, sample periods, and the period of the Detox campaign are presented in

Table 1. Variable source, sample period and Detox duration.

Variable Description	Source	Sample Period	Detox Duration
S&P500	Thomson Reuters Datastream	2011w01–2015w07
Abercrombie & Fitch	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
Adidas	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2014w25
ABF Group	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
Blackstone	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
Burberry	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2014w04
Dior	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
FAST	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2013w01
GAP	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
H&M	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2011w36
Inditex	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2012w48
L-Brands	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2013w03
Louis Vuitton	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
Marks & Spencer	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2012w40
Nike	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2011w30
Puma	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2011w28
PVH Group	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
Ralph Lauren	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
VFC Group	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2013w06
Youngor	Thomson Reuters Datastream	2011w01–2015w07	2011w26–2015w07
Detox	Google Trends	2011w01–2015w07
Hazardous Chemicals	Google Trends	2011w01–2015w07
Hazardous Substances	Google Trends	2011w01–2015w07
Organic Chemicals	Google Trends	2011w01–2015w07
Toxic Water	Google Trends	2011w01–2015w07
Water Pollution	Google Trends	2011w01–2015w07
Greenpeace	Google Trends	2011w01–2015w07

Notes: Google Trends data are obtained for the entire period, not just for the Detox Duration. The Detox Duration also denotes the duration of the dummy variables we used. From the Detox duration column, we use 11 different dummy variables. The S&P500 is used as a control group in the empirical part of this paper.

, while

Table 2. Descriptive statistics of the variables.

Variable	Count	Mean	Std	Min	Median	Max
A&F	215	0.33	6.51	−25.96	−0.44	27.72
Adidas	215	−0.19	3.43	−8.18	−0.35	19.97
Burberry	215	−0.25	4.54	−12.22	−0.59	22.18
Nike	215	−0.41	3.12	−12.31	−0.36	12.43
Gap	215	−0.35	4.02	−13.70	−0.33	18.14
H&M	215	−0.21	3.55	−11.45	−0.30	12.03
Blackstone	215	−0.52	4.80	−17.16	−0.23	19.96
L-Brands	215	−0.65	3.69	−11.92	−0.61	9.75
LouiV	215	−0.19	3.35	−7.98	0.04	10.49
M&S	215	−0.21	3.17	−13.65	−0.10	6.97
VFC group	215	−0.67	3.11	−11.31	−0.82	7.55
ABF group	215	−0.49	2.75	−7.75	−0.60	10.42
PUMA	215	0.12	3.08	−9.86	0.06	10.59
PVH group	215	−0.29	4.65	−19.42	−0.71	22.62
Ralph Lauren	215	−0.11	3.92	−11.96	−0.16	18.38
Fast	215	−0.41	4.43	−19.29	−0.46	15.08
Youngor	215	−0.15	4.13	−18.50	−0.17	11.18
Inditex	215	−0.60	15.63	−156.79	−0.62	159.53
Dior	215	−0.28	3.41	−8.73	−0.46	10.71
S&P500	215	0.23	1.99	−7.46	0.33	7.12
Detox	215	0.59	0.26	0.00	0.64	1.00
HazChem	215	0.65	0.19	0.00	0.65	1.00
HazSub	215	0.69	0.19	0.00	0.71	1.00
OrgChem	215	0.57	0.25	0.00	0.56	1.00
ToxicWat	215	0.64	0.22	0.00	0.67	1.00
WaterPol	215	0.63	0.24	0.00	0.67	1.00
Greenpeace	215	0.61	0.25	0.00	0.64	1.00

presents the descriptive statistics.

3. Methodology

In this study, we want to estimate the effect that a possible “attack” or a campaign conducted by an NGO has on the volatility of share prices of the “attacked” companies, as mentioned above. More precisely, we aim to capture and measure the impact of NGO activities on specific target companies listed on the stock exchange. In the empirical part of this paper, we employ the GARCH methodology, OLS regressions, and the Difference-in-Differences approach. We start our analysis by using the GARCH methodology (Bollerslev, 1986; S. J. Taylor, 1986) to estimate the volatility of share prices. We employ the typical form of the GARCH model, which is defined as:

$$y_t={\sigma }_t{w}_t$$

(1)

where $w_t$ is the discrete white noise with a mean of zero and unit variance.

$${\sigma }_t^2=a_0+\sum _{i=1}^q{a}_i{e}_{t-i}^2+\sum _{j=1}^p{b}_j{\sigma }_{t-j}^2+e_t$$

(2)

$$e_t~N(0,y_t)$$

(3)

where $a_i$ and $b_j$ are parameters of the model, and $a_0>0$, $a_i\ge 0$ and $b_j\ge 0.$

Before proceeding with our analysis, we test for possible correlations among the Google Trends data. We have not found any strong or high correlations among them, so we can proceed with our analysis1.

While abnormal returns are a standard measure in CSR and activism literature (e.g., Klassen & McLaughlin, 1996; Teoh et al., 1999), using stock price volatility is also justified, particularly when assessing the impact of NGO campaigns, such as Detox. Volatility captures investor uncertainty regarding potential compliance costs and reputational damage, which may not be immediately reflected in returns. It reflects the market’s reassessment of firm risk in the face of ambiguous or evolving ESG pressures, making it well-suited to detect reputational and regulatory uncertainty (Shiller, 1981; Mu, 2025). Moreover, volatility is known to exhibit persistence and clustering around informational events, making it more predictable and revealing of ongoing risk than returns alone (Engle, 1982; Bollerslev, 1986). Recent studies show that volatility can be a leading indicator of market stress and investor sentiment, particularly in ESG-sensitive contexts (Huang, 2011; Karunanayake et al., 2010). While noisier, volatility captures a broader market reaction that complements, rather than replaces, return-based analysis.

The central empirical part is divided into two sections. In the first section, we employ OLS regressions, and in the second section, we employ the DID approach. OLS regressions are used to estimate the potential effects of the Detox campaign and Google Trends on the volatility of share prices. The OLS regression model that we employ takes the following form:

$$\begin{gathered} {SPV}_{it}=a_0+a_1{D\text{\_}Company}_{it}+{\beta }_1{Detox}_{it}+{\beta }_2{HazChem}_{it}+{\beta }_3{HazSub}_{it}+{\beta }_4{OrgChem}_{it} \\ +{\beta }_5{ToxicWat}_{it}+{\beta }_6{WaterPol}_{it}+{\beta }_7{Greenpeace}_{it}+{\epsilon }_{it} \end{gathered}$$

where ${SPV}_{it}$ denotes the share price volatility,${D\text{\_}Company}_{it}$ is a dummy variable and denotes the period of the Detox campaign for each additional company ($D\text{\_}Company$ is replaced by the share price of the company we are using each time in each of our regressions), ${Detox}_{it}$, ${HazChem}_{it}$, ${HazSub}_{it}$, ${OrgChem}_{it}$, ${ToxicWat}_{it}$, ${WaterPol}_{it}$ and ${Greenpeace}_{it}$ denote the normalised Google trends, and ε denotes an error term.

In the second section, we aim to measure the reaction of firms’ share price volatility to the Detox campaign (an event) by estimating the Difference-in-Differences (hereafter referred to as DID) approach. We employ and apply the DID only to those companies that complied with the Detox campaign during our sample period. So, it is possible to estimate two different DID approaches. One that shows the effect of the Detox campaign on the volatility of share prices, and another that examines the impact of companies’ compliance with toxic-free clothing on the volatility of share prices.

The difference-in-difference approach (Rajan & Zingales, 1998) enables the measurement or estimation of the effect of a potential intervention (e.g., an NGO campaign or an attack) during a specific period (Shadish et al., 2002; Angrist & Pischke, 2008). This approach has been applied to labour economics (Card & Krueger, 1994), the economics of growth (Cetorelli & Gambera, 2001; Beck, 2003), financial economics (Brunnermeier et al., 2012), and in other social sciences (Draca et al., 2011). It uses two groups and one time period, which are separated into pre- and post-intervention. The treatment group receives the intervention, while the control group does not. Both groups should have a similar reaction, assuming that they do not receive the intervention. Moreover, it is possible to measure the post-intervention effect on the treatment group by using this methodology.

In our estimation, we use the share price volatility of the companies as dependent variables. Our treatment group consists of the share price volatility, and the control group consists of the share price volatility of the S&P500. The intervention for the first DID approach is the period of the Detox campaign policy that was implemented by Greenpeace and lasts until the companies capitulate. In this DID approach, we want to estimate the potential negative impact of the Detox campaign on the volatility of share prices. The intervention for the second DID approach is the period after the companies capitulated on toxic-free clothes. Here, we aim to determine whether the capitulation of companies has a positive impact on the volatility of share prices.

The first DID panel data regression model takes the following form:

$${SPV}_{sct}=a+{\beta }_1{Stock\text{\_}V}_{sc}+{\beta }_2{D\text{\_}Company}_t+{\beta }_3{(Stock\text{\_}V\ast D\text{\_}Company)}_{sct}+{\epsilon }_{it}$$

where ${SPV}_{sct}$ represents the share price volatility of share c in company c at time t. Stock_V is a dummy variable that refers to the period before the implementation of the Detox campaign for the treatment group (companies) compared to the control group (S&P500). D_Company indicates the Detox campaign implementation period for the control group. Parameter ${\beta }_1$ is the average difference in the average share price volatility for the companies and S&P500 before the Detox campaign, ${\beta }_2$ is the average change in S&P500 for the period of the Detox campaign, and ${\beta }_3$ is the DID interaction term, which represents the difference between the share price volatility of the companies and S&P500 after the Detox campaign. We expect that ${\beta }_3>0$, showing an increase in share price volatility.

The second DID panel data regression model takes the following form:

$${SPV}_{sct}=a+{\beta }_1{Cap\text{\_}V}_{sc}+{\beta }_2{D\text{\_}CompanyCap}_t+{\beta }_3{(Cap\text{\_}V\ast D\text{\_}CompanyCap)}_{sct}+{\epsilon }_{it}$$

where ${SPV}_{\mathrm{s}\mathrm{c}\mathrm{t}}$ represents the share price volatility of share c in company c at time t. Cap_V is a dummy variable that refers to the period before the capitulation of the companies to the Detox campaign for the treatment group (companies) relative to the control group (S&P500). D_CompanyCap indicates the capitulation period of companies for the control group. Parameter ${\beta }_1$ is the average difference in the average share price volatility for the companies and S&P500 before the capitulation, ${\beta }_2$ is the average change in S&P500 for the period of capitulation, and ${\beta }_3$ is the DID interaction term, which represents the difference between the share price volatility of the companies and S&P500 after the capitulation period. We expect that ${\beta }_3<0$, showing a decrease in share price volatility.

To strengthen the DID analysis, we estimate a placebo test. We re-estimated the DID on (i) the pre-Detox only sample using two placebo dates (14 February 2011; 28 March 2011), (ii) a treated-label flip where the S&P500 is assigned treatment (pre-period), and (iii) the full sample with a post-event placebo date (25 June 2012). Across all placebos, the Treated × Post coefficient is statistically indistinguishable from zero (e.g., −0.868, SE 0.620, p = 0.178 on 14 February 2011; −0.978, SE 0.605, p = 0.123 on 28 March 2011; +0.978, SE 0.605, p = 0.123 for the S&P500-treated flip; +3.116, SE 3.995, p = 0.445 for 25 June 2012). These falsification checks indicate that our main results are not driven by spurious timing breaks or by idiosyncrasies in the control index.

4. Empirical Results

In this section, we present and discuss the estimation results for our Google Trends variables and the Detox campaign, both before and after its implementation, on the share price volatility of companies active in the clothing industry. We begin our analysis with the OLS regression results, which include the Detox period and Google trends for all 19 clothing companies, to examine their impact on the volatility of their share prices. Then, we proceed with our analysis using the estimation results of the DID regressions, including the Detox period for 10 out of 19 companies, to test whether it has positively or negatively affected share price volatility before and after its implementation. We include only 10 companies in the DID regression, as these companies have committed to producing toxic-free clothes during our sample period. We can estimate the impact that the capitulation has on the share price volatility. We have also included 11 different Detox dummy variables in our analysis, as the capitulation period differs from company to company.

Table 3. Regression Results for the Detox Period with Google Trends Variables (Part 1).

	Abercrombie & Fitch	GAP	Blackstone	Louis Vuitton	ABF Group	PVH Group	Ralph Lauren	Youngor	DIOR
Variables	1	2	3	4	5	6	7	8	9
D_All	3.22 *	1.49	2.72	2.32 **	0.66	2.18	−1.79	1.34	−0.38
	(2.54)	(1.63)	(1.25)	(4.63)	(1.02)	(0.72)	(−1.08)	(1.44)	(−0.55)
Detox	3.63 +	0.03	5.80 *	−0.21	0.53	4.04	2.85 +	6.23 **	−0.28
	(1.65)	(0.06)	(2.20)	(−0.29)	(0.67)	(1.03)	(1.85)	(2.95)	(−0.33)
Hazchem	−0.02	1.47 +	4.97	1.5901	0.47	2.03	5.31	0.74	−1.46
	(−0.01)	(1.78)	(0.93)	(1.38)	(0.43)	(0.20)	(1.20)	(0.35)	(−1.07)
Hazsub	−3.00	−1.50	−8.69	4.01 **	1.80	−12.49	1.29	0.46	3.97 *
	(−1.20)	(−1.29)	(−1.43)	(2.79)	(1.38)	(−1.22)	(0.32)	(0.23)	(2.29)
Orgchem	−1.38	1.33 *	8.72 *	−1.04	2.04 **	−3.87	6.11 **	0.81	2.28 *
	(−0.63)	(2.17)	(2.50)	(−1.06)	(2.65)	(−0.81)	(2.81)	(0.53)	(1.98)
Toxicwat	3.37 +	1.23 *	3.79	1.89 *	2.63 **	6.45	7.95 **	0,13	2.22 *
	(1.86)	(2.35)	(1.25)	(2.06)	(3.45)	(1.64)	(3.56)	(0.10)	(2.01)
Waterpol	2.11	0.53	1.74	0.84	2.41 **	4.79	4.35 *	0.96	1.73
	(0.89)	(0.78)	(0.44)	(0.91)	(2.73)	(0.93)	(2.20)	(0.57)	(1.53)
Greenpeace	−0.47	0.20	0.76	1.91 **	6.48 **	0.66	−2.52	−0.97	1.41
	(−0.25)	(0.22)	(0.25)	(2.76)	(5.83)	(0.19)	(−1.42)	(−0.41)	(1.48)
Constant	36.98 **	16.96 **	20.82 **	10.65 **	8.26 **	19.87 **	15.15 **	19.44 **	13.54 **
	(12.29)	(19.37)	(4.16)	(7.02)	(6.10)	(2.78)	(5.03)	(6.64)	(8.00)
Observations	215	215	215	215	215	215	215	215	215
R-squared	0.04	0.11	0.08	0.13	0.30	0.03	0.13	0.09	0.08

Notes: Bold figures indicate statistically significant coefficients, ** denotes statistical significance at the 1% level (p < 0.01), * denotes statistical significance at the 5% level (p < 0.05), + denotes statistical significance at the 10% level (p < 0.1). D_All, D_Adidas, D_Burberry, D_Nike, D_H&M, D_L-Brands, D_M&S, D_VFC Group, D_PUMA, D_FAST and D_Inditex are dummy variables that are equal to 1 for the periods of the campaign. Detox, Hazchem, Hazsub, Orgchem, Toxicwat, Waterpol, and Greenpeace are trends identified via Google and downloaded from Google Trends.

and

Table 4. Regression Results for the Detox Period with Google Trends Variables (Part 2).

	ADIDAS	Burberry	NIKE	H&M	L-Brands	Marks & Spencer	VFC Group	PUMA	FAST	INDITEX
Variables	10	11	12	13	14	15	16	17	18	19
D_Adidas	4.53 **
	(3.1)
D_Burberry		14.94 **
		(10.24)
D_Nike			1.74 +
			(1.93)
D_H&M				1.21
				(1.01)
D_L-Brands					1.44 **
					(7.09)
D_M&S						−0.03
						(−0.28)
D_VFC Group							6.99 **
							(12.95)
D_PUMA								0.79 +
								(1.83)
D_FAST									0.30
									(1.12)
D_Inditex										−56.40
										(−1.32)
Detox	0.95	−2.59	0.48	−0.87	0.82 *	0.34 *	2.02 **	0.75 +	0.05	43.98
	(0.98)	(−0.90)	(1.52)	(−0.77)	(2.39)	(2.00)	(3.04)	(1.90)	(0.19)	(0.85)
Hazchem	4.02 *	9.67 **	0.30	−2.12	0.23	−0.42	−0.21	1.09	−0.87	98.23
	(2.06)	−2.67	(0.41)	(−1.15)	(0.55)	(−1.44)	(−0.17)	(1.39)	(−1.29)	(1.08)
Hazsub	0.85	−5.16	0.26	−2.56	−0.52	0.01	0.21	0.04	1.18 *	−152.28
	(0.36)	(−1.23)	(0.34)	(−1.19)	(−1.04)	(0.06)	(0.14)	(0.04)	(2.08)	(−1.21)
Orgchem	3.56 *	−3.73	0.60 +	2.92 *	0.63 *	−0.09	−0.09	−0.66	−0.07	118.23
	(2.43)	(−1.30)	(1.71)	(2.06)	(2.09)	(−0.48)	(−0.11)	(−1.37)	(−0.13)	(1.19)
Toxicwat	4.40 +	1.77	1.81 **	4.43 **	0.25	−0.14	1.83 *	0.71 +	1.12 **	−350.97
	(1.68)	(0.76)	(4.71)	(3.50)	(0.85)	(−0.85)	(2.29)	(1.76)	(3.28)	(−1.37)
Waterpol	−1.38	9.39 **	2.32 **	−1.15	0.71 +	−0.09	0.87	−0.57	0.34	−132.49
	(−0.86)	(2.96)	(4.33)	(−0.74)	(1.94)	(−0.39)	(0.82)	(−1.16)	(0.68)	(−1.19)
Greenpeace	−0.77	4.41 +	0.27	4.07 **	0.12	0.64 *	2.33 *	0.62	1.14 *	98.45
	(−0.49)	(1.71)	(0.61)	(3.23)	(0.37)	(2.06)	(2.46)	(0.85)	(2.44)	(1.17)
Constant	18.04 **	15.18 **	9.54 **	13.04 **	12.05 **	10.32 **	5.42 **	9.83 **	20.42 **	395.57 *
	(6.77)	(3.61)	(20.00)	(6.56)	(26.98)	(38.68)	(5.20)	(17.78)	(46.84)	(1.99)
Observations	215	215	215	215	215	215	215	215	215	215
R-squared	0.17	0.44	0.25	0.12	0.35	0.05	0.56	0.06	0.07	0.05

Notes: Bold figures indicate statistically significant coefficients, ** denotes statistical significance at the 1% level (p < 0.01), * denotes statistical significance at the 5% level (p < 0.05), + denotes statistical significance at the 10% level (p < 0.1). D_All, D_Adidas, D_Burberry, D_Nike, D_H&M, D_L-Brands, D_M&S, D_VFC Group, D_PUMA, D_FAST and D_Inditex are dummy variables that are equal to 1 for the periods of the campaign. Detox, Hazchem, Hazsub, Orgchem, Toxicwat, Waterpol, and Greenpeace are trends identified via Google and downloaded from Google Trends.

show the OLS regression results for all the clothing companies in our sample. In this estimation, we included the Detox dummy variables and our Google Trends data. This estimation yields some initial, insightful results about the effects that Detox and Google Trends have on share price volatility. The Detox dummy variables are positive and significant for 8 out of 19 companies, showing us that after the Detox campaign, the volatility of their share prices increases. The most affected was Burberry, experiencing a 14.94% increase in volatility, followed by VFC Group (6.99%), Adidas (4.54%), and Abercrombie & Fitch (3.23%). Other companies with notable effects include Louis Vuitton (2.33%), Nike (1.74%), L-Brands (1.44%), and Puma (0.79%). These findings suggest that the Detox campaign, which publicly pressured companies on environmental grounds, significantly heightened investor uncertainty for specific brands, particularly those that were more directly targeted or had higher environmental reputational risk.

In addition, Google Trends data, used as a proxy for public attention, reinforced the pattern of increased volatility. Specific search terms like “Detox”, “Toxic Water”, and “Organic Chemicals” had broad impacts across multiple firms. For instance, “Detox” searches were linked to increased volatility in Abercrombie & Fitch, Blackstone, Ralph Lauren, VFC Group, and others. Environmental concerns, including “Hazardous Chemicals”, “Hazardous Substances”, and “Water Pollution”, have significantly affected firms such as Burberry, Adidas, Louis Vuitton, Dior, and ABF Group. The “Greenpeace” variable also proved impactful, with significant effects on Louis Vuitton, Burberry, H&M, and ABF Group. According to the above results, we conclude that the Detox campaign initially has adverse and significant effects on 8 out of 19 companies, which increases share price volatility. We have also shown that the Google trends variables have adverse and significant effects on our dependent variables, increasing their share price volatility. The results of Google Trends are briefly presented in Figure 1.

The DID estimation results for each company that capitulated on the Detox campaign during our sample period are presented in

Table 5. DID estimation results for the Detox campaign period.

	ADIDAS	Burberry	FAST	INDITEX	H&M	L-Brands	Marks & Spencer	NIKE	PUMA	VFC Group
Variables	1	2	3	4	5	6	7	8	9	10
Stock_V	12.32 **	8.29 **	16.75 **	199.58 **	8.67 **	9.89 **	7.17 **	5.88 **	5.61 **	4.22 **
	(10.42)	(15.72)	(112.15)	(4.10)	(21.18)	(102.08)	(73.06)	(25.09)	(23.57)	(19.37)
D_Adidas	1.12 **
	(3.75)
D_Burberry		1.50 **
		(4.68)
D_FAST			2.92 **
			(6.36)
D_Inditex				3.05 **
				(6.32)
D_HM					6.24 **
					(4.92)
D_L-Brands						2.90 **
						(6.42)
D_MS							3.30 **
							(6.32)
D_Nike								2.28 **
								(3.95)
D_PUMA									2.27 **
									(3.66)
D_VFC Group										2.79 **
										(6.36)
DID	5.39 **	13.70 **	2.86 **	70.00	3.47 *	1.44 **	3.26 **	0.6723	1.31 +	3.96 **
	(4.36)	(11.76)	(5.68)	(1.44)	(2.17)	(2.99)	(6.16)	(0.63)	(1.76)	(6.06)
Constant	3.05 **	2.92 **	2.78 **	2.80 **	3.55 **	2.76 **	2.84 **	3.81 **	3.84 **	2.76 **
	(23.29)	(28.09)	(38.77)	(39.49)	(20.35)	(39.21)	(40.77)	(18.60)	(18.83)	(37.92)
Observations	430	430	430	430	430	430	430	430	430	430
R-squared	0.50	0.67	0.93	0.07	0.55	0.85	0.75	0.62	0.57	0.64

Notes: Bold figures indicate statistically significant coefficients, ** denotes statistical significance at the 1% level (p < 0.01), * denotes statistical significance at the 5% level (p < 0.05), + denotes statistical significance at the 10% level (p < 0.1). The DID variable refers to the average treatment effect. The DID variable refers to the additional Detox campaign period for each of our variables and takes the value 1 for that specific period. The Stock_V dummy variable refers to the period before the Detox campaign period for the treatment group relative to the control group. The D_Adidas, D_Burberry, D_FAST, D_Inditex, D_HM, D_L-Brands, D_MS, D_Nike, D_PUMA, and D_VFC Group dummy variables refer to the Detox campaign period for the control group. In all the estimations, the standard errors are block-bootstrapped, and the t-statistics are reported in parentheses. The results are given in percentage points.

and

Table 6. DID estimation results after the Detox campaign period.

	ADIDAS	Burberry	FAST	INDITEX	H&M	L-Brands	Marks & Spencer	NIKE	PUMA	VFC Group
Variables	1	2	3	4	5	6	7	8	9	10
Cap_V	6.92 **	21.99 **	13.88 **	129.58 **	5.19 **	8.42 **	3.90 **	8.40 **	4.30 **	8.38 **
	(18.47)	(21.37)	(27.72)	(162.28)	(3.33)	(17.43)	(7.07)	(8.37)	(6.19)	(13.39)
D_AdidasCap	−0.90 **
	(−2.66)
D_BurberryCap		−1.42 **
		(−4.15)
D_FASTCap			−2.91 **
			(−6.08)
D_InditexCap				−3.04 **
				(−6.02)
D_HMCap					−6.13 **
					(−4.62)
D_L-BrandsCap						−2.91 **
						(−6.25)
D_MSCap							−3.28 **
							(−5.98)
D_NikeCap								−0.24
								(−0.46)
D_PUMACap									−2.12 **
									(−3.32)
D_VFC GroupCap										−2.91 **
										(−6.08)
DID	8.29 **	−16.83 **	−2.91 **	84.69	−3.43 *	−1.31 **	−3.23 **	−2.71 **	−1.30 +	−4.88 **
	(4.29)	(−15.94)	(−5.56)	(1.44)	(−2.11)	(−2.67)	(−5.77)	(−2.62)	(−1.76)	(−7.52)
Constant	4.18 **	4.43 **	5.70 **	5.86 **	3.66 **	5.70 **	6.14 **	4.26 **	6.11 **	5.70 **
	(15.05)	(14.07)	(12.03)	(11.72)	(18.24)	(12.47)	(11.27)	(8.77)	(10.26)	(12.03)
Observations	380	380	380	380	380	380	380	380	380	380
R-squared	0.52	0.68	0.93	0.07	0.52	0.84	0.72	0.59	0.55	0.68

Notes: Bold figures indicate statistically significant coefficients, ** denotes statistical significance at the 1% level (p < 0.01), * denotes statistical significance at the 5% level (p < 0.05), + denotes statistical significance at the 10% level (p < 0.1). The DID variable refers to the average treatment effect. The DID variable refers to the additional period after the Detox campaign for each of our variables and takes the value 1 for that specific period. The Cap_V dummy variable refers to the period during which the Detox campaign was implemented for the treatment group compared to the control group. The D_AdidasCap, D_BurberryCap, D_FASTCap, D_InditexCap, D_HMCap, D_L-BrandsCap, D_MSCap, D_NikeCap, D_PUMACap, and D_VFCCap Group dummy variables refer to the period after the Detox campaign for the control group. In all the estimations, the standard errors are block-bootstrapped, and the t-statistics are reported in parentheses. The results are given in percentage points.

. Table 5 presents the estimation results when the Detox campaign period is used as an intervention. The DID interaction term is positive and significant for Adidas (5.39%), Burberry (13.70%), Fast (2.86%), H&M (3.47%), L-Brands (1.44%), Marks and Spencer (3.26%), Puma (1.31%) and VFC Group (3.96%) showing that the Detox campaign affects negatively the share prices by increasing their volatility in contrast to that of S&P500. For Inditex and Nike, the results are insignificant. For the period before the Detox campaign Inditex (199.58%) shows to have the highest value, followed by Fast (16.75%), Adidas (12.32%), L-Brands (9.89%), H&M (8.67%), Burberry (8.29%), Marks and Spencer (7.17%), Nike (5.88%), Puma (5.61%) and VFC Group (4.22%). The Volatility of the S&P500 after the Detox campaign is positive and significant for all companies.

Table 6 presents the DID estimation results when the capitulation period of each company is used as an intervention. The DID interaction term is negative and significant for Burberry (−16.83%), Fast (−2.91%), H&M (−3.43%), L-Brands (−1.31%), Marks and Spencer (−3.23%), Nike (−2.71%), Puma (−1.30%) and VFC Group (−4.88%) showing that the capitulation affects positively the share prices by decreasing their volatility in contrast to that of S&P500. Only Adidas (8.29%) is expected to be positively affected after its capitulation, indicating that its volatility remains high. For Inditex, the results are insignificant. For the period before the capitulation Inditex (129.58%) shows to have the highest value, followed by Burberry (31.99%), Fast (13.88%), L-Brands (8.42%), Nike (8.40%), VFC Group (8.38%), Adidas (6.92%), H&M (5.19%), Puma (4.30%) and Marks and Spencer (3.90%). The Volatility of the S&P500 after the capitulation is negative and significant for 9 out of 10 companies. The only exception is Nike, whose volatility is negative but insignificant.

Regarding the above DID estimation results, we conclude that during the Detox campaign, companies are negatively affected, resulting in increased volatility. After the companies capitulated to toxic-free clothes, their volatility decreased, indicating that the capitulation had a positive impact on them. If we take a step further, we can compare the results in Table 3 and Table 4 to assess the final impact on the companies’ volatility between the Detox and capitulation periods. For example, Burberry experienced a change of 30.54% during the two periods, indicating that the final impact of the capitulation between the two periods decreased its share price volatility by 30.51%.

5. Conclusions

This paper belongs to a small but growing body of research exploring the intersections between civil society action and financial market performance, with a special focus on digital campaigns by NGOs and their impact on firm-level volatility. It evaluates the effect of Greenpeace’s Detox campaign on the share price volatility of 19 international apparel firms by employing both conventional econometric (OLS) and causal inference (Difference-in-Differences) methods.

The empirical results show two key findings. First, the Detox campaign significantly increased the stock price volatility for several targeted companies. This effect impacted firms such as Burberry, Adidas, and VFC Group, suggesting that NGO led campaigns, especially when widely disseminated through social media and public platforms, can influence investor perceptions and increase stock market uncertainty. Second, the volatility stabilizes post-compliance: For companies that complied with Greenpeace’s demands for toxic-free products, a notable decrease in volatility followed. That suggests that investor concerns may ease once firms demonstrate responsiveness to NGO criticism, reflecting restored confidence or reputational repair. Furthermore, by introducing data on Google Trends into the regressions, this paper reveals that share price volatility spikes are related to environmental-related digital interest (measured by search volumes), validating the dissemination channels of information on market reactions.

Moreover, the empirical findings reveal that NGO-led campaigns, such as Greenpeace’s Detox initiative, serve as external shocks to financial markets, increasing firm-level stock price volatility. From an economic standpoint, this heightened volatility reflects the introduction of non-traditional risks—specifically reputational and regulatory risks—into market valuations. Investors interpret activist campaigns as signals of potential future costs: these may include shifts in consumer demand, costly operational overhauls, or anticipated regulatory scrutiny. In this context, volatility serves as a proxy for uncertainty around firms’ future cash flows and the credibility of their ESG commitments. The reaction of investors to NGO pressure aligns with theories of informational asymmetry and market efficiency: when new, credible information is introduced by a third-party actor, such as Greenpeace, markets rapidly adjust, even if the information is not immediately financially quantifiable.

Furthermore, the decline in volatility observed after companies complied with Detox demands can be interpreted through the lens of signaling theory and stakeholder theory. Compliance acts as a costly signal to the market, indicating that the firm is responsive to societal expectations and willing to internalize previously externalized environmental costs. This reduction in perceived risk stabilizes investor sentiment and restores predictability to stock performance. The post-compliance stability also suggests that markets reward proactive corporate governance and environmental stewardship, thus lowering a firm’s cost of capital over time. In this sense, civil society organizations play an informal regulatory role by reshaping firm incentives and indirectly influencing capital allocation decisions in the economy. As environmental and social risks become increasingly priced into markets, NGOs emerge as powerful intermediaries that bridge the gap between societal values and investor behavior.

Importantly, our Difference-in-Differences estimates, benchmarked against the S&P500 index, are further validated through a series of placebo tests, as described in the methodology section. These tests include pre-campaign falsifications, treatment reassignment, and post-event timing shifts, all of which yielded statistically insignificant interaction terms. These falsification checks support the internal validity of our results and help rule out spurious trends or confounding macroeconomic events such as the 2011–2012 Eurozone crisis.

While the S&P500 index does not provide an industry-matched control, its inclusion allows us to benchmark against broader market behavior. Moreover, the heterogeneous firm-level approach, focusing on treatment timing aligned with individual compliance dates, adds nuance to our estimates and supports our conclusion that volatility increases during activist scrutiny and stabilizes after firms meet environmental demands.

At a macro-policy level, this paper examines how the expansion of civil Society, enabled by digital networking, shapes firm conduct and financial markets. As such, those who govern and regulate should appreciate NGOs’ two-fold strength: as a supervisory authority accountable for firms and environmental or ethical issues with measurable market impacts. E-activism legitimacy and facilitation mechanisms are essential for future policy frameworks to consider in international government architecture. This paper detects dynamic shifts among NGOs, data flows, and stock market performance.

To conclude, our findings should be interpreted with caution. While the results suggest a causal link between the Detox campaign and changes in volatility among targeted firms, the absence of a sector-specific control group and the limited dynamic modelling restrict the scope of generalizability. Moreover, the unique visibility and intensity of the Detox campaign may not reflect the typical influence of NGO activism across other sectors. Future research, utilizing more granular industry controls and extended time-series modelling, could help validate and extend these findings to other domains of financial activism.