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The Impact of Perceived Greenwashing on Customer Satisfaction and the Contingent Role of Capability Reputation

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Abstract

We investigate the impact of perceived greenwashing on customer satisfaction. Unlike prior research that largely examines customer perceptions associated with irresponsible behavior, we focus on cases where firms overcommit and/or do not deliver on promised socially responsible actions. We theorize that this type of greenwashing is associated with lower customer satisfaction because customers perceive greenwashing through the lens of corporate hypocrisy. Using data from the American Customer Satisfaction Index (ACSI) for U.S. companies during the period 2008–2016, we document a negative link between perceived greenwashing related to green product innovation (GPI) and the ACSI index. We demonstrate that this effect is primarily triggered by corporate policies exceeding the corresponding implementation actions and not by lower levels of implementation. We also show that a firm’s capability reputation mitigates the negative effect of greenwashing on customer satisfaction. Moreover, we conduct an experiment and provide evidence confirming that GPI greenwashing is in fact perceived by customers as corporate hypocrisy.

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Notes

  1. Survey available at: https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/consumer-and-employee-esg-expectations.html (last accessed October 11th, 2021).

  2. In this study, we refer to greenwashing as “perceived” because we remain agnostic as to whether a company intentionally greenwashes or appears to greenwash due to other factors, such as the lack of capability to implement. Provided that our main focus is the effect on customers, what matters for our theory and empirics is how customers perceive a gap between objectives and implementation. For brevity, therefore, when we talk about greenwashing in the rest of this study, we always refer to greenwashing as perceived by customers and not as intentioned by firms. To illustrate the distinction between CSI and greenwashing, consider the following example: CSI would be when a company is directly harming a local community by damping toxic waste in the local river while greenwashing would occur when a company states that it will achieve the highest percentage reduction in carbon emissions in its industry, but in reality, it merely gets to be middle of the pack.

  3. Companies adopt a variety of environmental policies (e.g., carbon emissions reduction, etc.). Even though the gap of policy and implementation in these areas could influence how customers evaluate a company overall, we argue that it is less likely to directly influence the level of customer satisfaction. This is because customer satisfaction is primarily driven by the experience of the actual consumption of a firm’s products or services. Thus, greenwashing around issues that influence a product is more likely to be detected by customers and have an impact on their satisfaction.

  4. Chernev and Blair (2015) find that CSR engagement may alter product perceptions, such that products of companies engaged in prosocial activities are even perceived as performing better.

  5. See https://247wallst.com/energy-business/2009/04/02/the-%E2%80%9Cgreen%E2%80%9D-hypocrisy-america%E2%80%99s-corporate-environment-champions-pollute-the-world/; accessed April 25th, 2018.

  6. More information about the ACSI index can be found at http://www.theacsi.org/

  7. Provided that ASSET4 rates companies on ESG metrics, and that such data is often sold to investors who integrate ESG in their investment decisions, there is little reason to believe that a company that has set a particular (environmental) policy will avoid or neglect disclosing how it did so (doing so may in fact lead to lower ratings and rankings, and insufficient disclosure to investors). As such, we assume that non-disclosure on performance-related items likely reflects lack of implementation of related policies.

  8. We measure GPI policy and implementation in the same year provided that customers are likely to form their contemporaneous perceptions based on the information they have available at a particular point in time. It is, thus, unlikely that they would have the time or be willing to exert the effort or have proper access to critical information so as to form perceptions based on a more nuanced analysis (e.g., by considering a firm’s previous year’s policy commitments and current year’s performance at the issue level); we expect this to more likely take place for analysts or investors (e.g., Hawn and Ioannou, 2016).

  9. We also ran our main models after dropping the negative values of greenwashing. That is, we excluded observations where firms’ implementation exceeded their stated policy. The results of this analysis (not tabulated) supported our main results with even stronger and significant effects.

  10. The Environmental score accounts for a long list of items, including those used to construct Greenwashing. Note that the items related to greenwashing have been manipulated (i.e., their values have been aggregated, normalized, and used in a formula) in ways that minimize any direct relationship with the overall Environmental score (indicatively, the correlation coefficient between the two variables is -0.11). In addition, we repeated our analyses using a 1-year-lagged Environmental score and our results did not change.

  11. In unreported results, and as a robustness check, we relax this assumption and run a model in which we use the greenwashing of the current year (t) and the customer satisfaction score of the following year (t + 1). The coefficient on greenwashing, although somewhat smaller in size, as expected, remains negative and statistically significant at a p value of 0.057 (b = -0.660, s.e. = 0.27).

  12. As an input, the delta computation requires setting the maximum r-squared (Rmax) that would result if all unobservables were included in the regression. This value can be determined using r-squared values of prior empirical studies as a guide or taking the r-squared from the controlled models and multiplying it by 1.3. Using a strict approach, we set Rmax equal to 1, an extremely optimistic value given the existence of measurement errors in the outcome variable. Given that the higher the Rmax, the lower the δ coefficient, we report a very conservative value of the δ coefficient. For example, if Rmax were set to 0.70, the δ coefficient would be 1.48.

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Acknowledgements

We thank Editor Shuili Du for her insightful guidance and three anonymous reviewers for their constructive comments throughout the review process. This paper benefited from discussions at the Strategic Management Society Conference 2018, and the Sustainability, Ethics, and Entrepreneurship (SEE) Conference 2019. We are grateful to Donal Crilly, Caroline Flammer, Olga Hawn, and participants at London Business School’s Work-In-Progress seminar 2019 for their helpful comments on earlier drafts. We also acknowledge generous financial support by London Business School for conducting the experiment of this study. The authors are solely responsible for any remaining errors in this paper. The authors have equally contributed and are listed in alphabetical order.

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  1. Strategy and Entrepreneurship Area, London Business School, Regent’s Park, London, UK

    Ioannis Ioannou

  2. Department of Business and Public Administration, University of Cyprus, 1 University Avenue, 2109, Aglantzia, Cyprus

    George Kassinis

  3. Department of Economics, University of Peloponnese, 22100, Tripolis, Greece

    Giorgos Papagiannakis

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Ioannou, I., Kassinis, G. & Papagiannakis, G. The Impact of Perceived Greenwashing on Customer Satisfaction and the Contingent Role of Capability Reputation. J Bus Ethics 185, 333–347 (2023). https://doi.org/10.1007/s10551-022-05151-9

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