Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is a good encyclopedia entry that addresses a critical gap in the literature on corporate accountability. The article successfully synthesizes a broad range of contemporary scholarship to define "social washing" within the larger "washing" phenomenon. Recommendations for Improvement:
1.    Consider Re-structuring Sections: The manuscript may include the full text of Sections 6 and 7, which appear to be cut off. Also, consider merging Sections 4 and 6 into a single section, which may be titled: "Implications and Pathways for NGOs and Social Development Professionals".
2.    Expand Section 5: The application of the "Ten Worlds of Welfare Regime Theory" is a promising analytical lens. The article could explicitly discuss how social washing might manifest differently across these regimes. For example, is social washing more likely to take the form of performative public-private partnerships in a "Neoliberal" regime, or as a tool for state-led image management in a "Communist/Socialist" regime? 
3.    Enhance the "Pathways" Section: The recommendations for NGOs are sound but could be made more actionable. The section would benefit from a concrete example of how an NGO might apply these tools in practice to vet a potential corporate partner or evaluate a social program. 

Author Response

Comment 1: Consider re-structuring sections: The manuscript may include the full text of Sections 6 and 7, which appear to be cut off. Also, consider merging Sections 4 and 6 into a single section, which may be titled: “Implications and Pathways for NGOs and Social Development Professionals”.
Response 1: Thank you for this helpful recommendation. I have restructured the manuscript accordingly. The revised paper now follows a clearer sequence from conceptual background, to market relevance, to ESG measurement, to welfare-regime variation, and then to a consolidated section titled “Implications and Pathways for NGOs and Social Development Professionals.” This section now gathers together the discussion of organisational vulnerability, governance safeguards, analytical tools, and practical application, thereby improving both coherence and readability.

Comment 2: Expand Section 5: The application of the “Ten Worlds of Welfare Regime Theory” is a promising analytical lens. The article could explicitly discuss how social washing might manifest differently across these regimes. For example, is social washing more likely to take the form of performative public-private partnerships in a “Neoliberal” regime, or as a tool for state-led image management in a “Communist/Socialist” regime?
Response 2: Thank you for this insightful suggestion. I have expanded and refined the welfare-regime section in precisely this direction. Table 2 now specifies likely manifestations of social washing across different regime contexts, and the prose following the table makes the comparison more explicit. In particular, the revised manuscript now discusses neoliberal regimes in terms of performative public-private partnerships, branded community investment, employability rhetoric, and highly visible service initiatives that symbolically compensate for welfare retrenchment. It also explicitly addresses Communist/Socialist regimes, where social washing may function as a form of state-led or state-aligned image management through visible claims of inclusion, poverty reduction, or community development that may obscure implementation gaps, localised exclusion, or limited independent scrutiny.

Comment 3: Enhance the “Pathways” Section: The recommendations for NGOs are sound but could be made more actionable. The section would benefit from a concrete example of how an NGO might apply these tools in practice to vet a potential corporate partner or evaluate a social program.
Response 3: Thank you for this excellent recommendation. I have revised Section 5 to make the pathways discussion substantially more practice-facing. Section 5.2 now explains more clearly how NGOs can use internal ESG safeguards, Stakeholder Engagement and Materiality Assessment (SEMA), Theory of Change, SMART indicators, and Bayesian analysis to assess the credibility of social claims. Section 5.3 then provides a concrete case illustration involving a community-based NGO working in children’s well-being and youth development. In that example, the NGO is approached by a property developer seeking partnership on a youth empowerment initiative, and the revised manuscript shows step by step how SEMA, Theory of Change, SMART indicators, and Bayesian reasoning can be used to distinguish authentic accountability from reputational shielding.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript at hand attempts to provide a conceptual overview of "social washing" within ESG frameworks. While the topic is highly relevant to contemporary sustainable finance, the current draft lacks the academic rigor and empirical depth expected of a scientifically sound encyclopedia entry. It reads more like a descriptive essay than a structured, critical review of the literature.

 I have major reservations that must be addressed:

The definition and mechanisms of social washing are discussed in purely qualitative and sociological terms. However, ESG is fundamentally a corporate finance and investment framework. The paper fails to critically examine how social washing manipulates capital market allocations or affects firm valuation.

In Section 3, Table 1 outlines risks of manipulation but remains overly simplistic. The entry must delve deeper into the statistical and econometric challenges of quantifying the "S" pillar. How do qualitative indicators translate into risk premiums?

Section 5 introduces the "Ten Worlds of Welfare Regime Theory". This sudden shift to macro-welfare regimes feels highly disconnected from the corporate governance focus of the preceding sections. The linkage between corporate ESG data manipulation and state welfare typologies must be significantly tightened or removed entirely.

The manuscript ignores critical comparative literature regarding ESG performance measurement across different financial sectors. To enrich the scientific soundness of the S-pillar measurement discussion, the author must incorporate recent empirical perspectives comparing ESG performance evaluations in traditional finance versus new financial technologies (FinTech).

Author Response

Comment 1: The definition and mechanisms of social washing are discussed in purely qualitative and sociological terms. However, ESG is fundamentally a corporate finance and investment framework. The paper fails to critically examine how social washing manipulates capital market allocations or affects firm valuation.
Response 1: Thank you for this important observation. I agree that the earlier version did not make the market and valuation implications of social washing sufficiently explicit. In the revised manuscript, the Definition now frames social washing as relevant not only to social policy and sustainable development debates, but also to corporate governance, ESG evaluation, reputational signalling, and the interpretation of social performance in market settings. I have also revised Section 2 so that the cross-sector discussion culminates in a clearer account of how selective or inflated social claims may affect ESG ratings, investor perceptions of governance quality, the allocation of legitimacy and capital, and partnership opportunities. In Section 3, I further clarify that distorted social disclosures may influence perceived risk premia, cost of capital, and broader firm-valuation judgements where such signals are treated as proxies for governance quality or long-term social risk.

Comment 2: In Section 3, Table 1 outlines risks of manipulation but remains overly simplistic. The entry must delve deeper into the statistical and econometric challenges of quantifying the “S” pillar. How do qualitative indicators translate into risk premiums? Response 2: Thank you. I agree that the earlier treatment of the “S” pillar required greater analytical depth. Section 3 has been revised to clarify how social performance translates into ESG signals using policy indicators, numerical data, controversy overlays, weighting systems, benchmarking, and assumptions about disclosure quality. The revised section now makes clear that the problem lies not only in the qualitative nature of social data, but also in the interpretive chain through which such data become usable as reputational, governance, and risk signals. I have also added an explicit sentence connecting distorted social disclosures to perceived risk premia, cost of capital, and firm-valuation judgements. Table 1 has been strengthened through the addition of a “Market Interpretation” column, so that the table now shows not only risks of manipulation but also how those manipulations may shape ESG evaluation and market understanding.

Comment 3: Section 5 introduces the “Ten Worlds of Welfare Regime Theory”. This sudden shift to macro-welfare regimes feels highly disconnected from the corporate governance focus of the preceding sections. The linkage between corporate ESG data manipulation and state welfare typologies must be significantly tightened or removed entirely.
Response 3: Thank you for this perceptive comment. I agree that, in the earlier version, the welfare-regime discussion was insufficiently integrated into the main analytical thread. In the revised manuscript, I have retained this perspective but substantially reframed it. Section 4 now presents welfare-regime analysis explicitly as a contextual lens for understanding how social washing may take different forms across institutional environments, rather than as a detached macro-theoretical excursus. The revised section explains how relationships among state provision, market dependence, civil-society capacity, and regulatory enforcement shape both the incentives for social washing and the kinds of social claims most likely to gain legitimacy. Table 2 has likewise been redesigned so that it directly links regime context to likely manifestations of social washing and its implications for NGOs and social development professionals.

Comment 4: The manuscript ignores critical comparative literature regarding ESG performance measurement across different financial sectors. To enrich the scientific soundness of the S-pillar measurement discussion, the author must incorporate recent empirical perspectives comparing ESG performance evaluations in traditional finance versus new financial technologies (FinTech). Response 4: Thank you for this constructive recommendation. In response, I have added a distinct comparative paragraph in Section 3 contrasting traditional finance and emerging FinTech contexts. The revised manuscript now notes that, in traditional finance, ESG evaluation is more often mediated through established ratings providers, annual reporting architectures, and relatively standardised disclosure frameworks, whereas in FinTech environments, social performance may increasingly be assessed through digital infrastructures, alternative data, automated analytics, and platform-based scoring logics. I also note that these developments may broaden access to ESG information while introducing new concerns regarding data provenance, algorithmic opacity, explainability, and reproducibility. This addition is intended to strengthen the scientific and contemporary relevance of the S-pillar discussion.

Comment 5: The English could be improved to more clearly express the research.
Response 5: The author has entirely proofread the manuscript for a clearer expression. 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The revised version is clearly improved. The author has taken feedbacks seriously and the manuscript is now better connected to corporate finance and ESG evaluation concerns.

The manuscript grew from 11 to 15 pages after revision. For an encyclopedia entry, this feels too long. The case illustration in Section 5.3 is interesting but takes up a lot of space, it could probably be condensed into one paragraph. Some passages in Sections 4–5 also repeat similar points and could be tightened. Table 2 is much better than the original version. But 10 regime types with three columns makes it quite heavy to read. The author could think about whether some closely related regimes (e.g., Slightly Universal and Selective Rudimentary) might be grouped together without losing the main argument.

FinTech paragraph is a good addition, but it stays at a general level. Mentioning algorithmic opacity and data provenance is fine, but one or two concrete examples or empirical references would make this paragraph more convincing. Market Interpretation column (Table 1) works well and directly addresses the earlier reviewer's concern. The Workforce and Human Rights rows are stronger; the Community and Product Responsibility rows could say a bit more about actual financial or screening consequences.

The revision now mentions risk premia and cost of capital, which is a step forward. But even a brief reference to empirical studies attempting to quantify the financial effects of S-pillar manipulation would add more weight. If such studies are scarce, it would be useful to say so explicitly. The expanded reference list is relevant. However, quite a few sources are grey literature or practitioner websites (refs [25], [26], [50], [51], [53]). Where possible, peer-reviewed alternatives would strengthen the entry.

Author Response

Comment 1: The manuscript grew from 11 to 15 pages after revision. For an encyclopedia entry, this feels too long. The case illustration in Section 5.3 is interesting but takes up a lot of space, it could probably be condensed into one paragraph.
Response: I entirely agree. I have heavily condensed the case illustration in Section 5.3 to improve the flow and maintain the concise nature of an encyclopedia entry. It has been condensed from a lengthy multi-part example into a single paragraph that effectively demonstrates Stakeholder Engagement and Materiality Assessment (SEMA), Theory of Change (ToC), and Bayesian analysis.

Comment 2: Some passages in Sections 4–5 also repeat similar points and could be tightened.
Response: Thank you for pointing the issue out. I have carefully reviewed Sections 4 and 5 and tightened the text. Specifically, I removed repetitive explanations regarding "mission drift" and "reputational shields" that overlapped between the sections, streamlining the narrative flow.

Comment 3: Table 2 is much better than the original version. But 10 regime types with three columns makes it quite heavy to read. The author could think about whether some closely related regimes (e.g., Slightly Universal and Selective Rudimentary) might be grouped together without losing the main argument.
Response: This is an excellent suggestion. To make Table 2 more digestible for the reader while preserving Aspalter's theoretical nuance, I have grouped the original 10 welfare regimes into 5 broader, cohesive categories:

1. Universalist (Social Democratic)
2. Conservative & Moralised (Christian Democratic & Pro-Welfare)
3. Market-Oriented & Residual (Neoliberal & Anti-Welfare)
4. Transitional & Rudimentary (Slightly Universal, Selective Rudimentary, & Ultra Rudimentary)
5. State-Centric & Exclusionary (Communist/Socialist & Exclusion-Based)

The preceding text in Section 4 has also been updated to reflect and introduce these newly grouped categories clearly.

Comment 4: FinTech paragraph is a good addition, but it stays at a general level. Mentioning algorithmic opacity and data provenance is fine, but one or two concrete examples or empirical references would make this paragraph more convincing. Response: I have revised the FinTech paragraph in Section 3 to include a concrete empirical example. The text now explicitly illustrates how a "black-box" algorithm might inflate a firm's social score by scraping positive social media sentiment regarding a local charity drive, while systematically failing to "see" systemic labor violations buried in non-digitized legal filings.

Comment 5: Market Interpretation column (Table 1) works well and directly addresses the earlier reviewer's concern. The Workforce and Human Rights rows are stronger; the Community and Product Responsibility rows could say a bit more about actual financial or screening consequences.
Response: I have updated the "Market Interpretation" column in Table 1 to explicitly outline the financial consequences for the Community and Product Responsibility categories.

• For Community, I added that controversy-based screens "can materially associate with higher cost of equity and tighter financing."
• For Product Responsibility, I noted that vague policies obscure "significant litigation risk and 'red-flag' status in risk-premia assessments, directly impacting long-term valuation."

Comment 6: The revision now mentions risk premia and cost of capital, which is a step forward. But even a brief reference to empirical studies attempting to quantify the financial effects of S-pillar manipulation would add more weight. If such studies are scarce, it would be useful to say so explicitly.
Response: Thank you for this guidance. I have added a sentence to Section 3 explicitly acknowledging this gap in the literature. I note that "empirical research specifically quantifying the financial penalties of S-pillar manipulation remains scarce compared to the environmental pillar."

Comment 7: The expanded reference list is relevant. However, quite a few sources are grey literature or practitioner websites (refs. [25], [26], [50], [51], [53]). Where possible, peer-reviewed alternatives would strengthen the entry.
Response: I have conducted a thorough review of our bibliography and purged the grey literature and practitioner websites you identified (including sources from Dalberg, ESG The Report, PwC, Protiviti, and Policy&). To strengthen the academic weight of the entry, these have been replaced with rigorous, peer-reviewed alternatives, including Farooq et al. (2021), Farneti et al. (2019), and Cardoni et al. (2022).

Last but not least, I have further proofread the entire manuscript to enhance the clarity for presenting the arguments.