Mobilizing private capital toward sustainable development requires understanding how sustainability-related information influences individual investment decisions. While prior research has examined investors’ sustainable preferences, less attention has been paid to how such information is mediated in practice. This study aims to investigate the role of financial advisors as key informational intermediaries between corporate environmental, social, and governance (ESG) disclosures and private wealth allocation.
Using survey data from 800 Italian high-net-worth individuals (HNWIs), the study analyses actual portfolio holdings across five asset classes, including sustainable investment products. Logistic regression models examine the determinants of SI ownership, complemented by mediation analyses assessing how advisor interactions and sustainability-related information shape trust and perceived effectiveness.
Sustainable investment adoption among HNWIs depends not only on sustainability orientation, but critically on perceptions of credibility and effectiveness of sustainability claims. Financial advisors play a central role in shaping these perceptions by mediating sustainability information. Regulatory-driven disclosures increase exposure to sustainable products, while richer and discretionary sustainability communication is more strongly associated with sustainable portfolio allocation.
Enhancing advisors’ ESG competencies and improving the clarity and transparency of sustainability ratings can enhance effective communication with private investors.
By clarifying how sustainability information is translated into investment decisions, the study informs efforts to reorient private wealth toward sustainable development goals.
The study contributes to sustainability accounting, management and sustainable finance research by highlighting financial advisors as organizational intermediaries linking ESG disclosure to private capital allocation.
1. Introduction
Achieving the United Nations Sustainable Development Goals (SDGs) requires a reorientation of financial resources across economic activities. While firms are increasingly expected to improve their environmental and social performance, these efforts depend partly on whether financial systems channel private capital toward genuinely sustainable activities. In financial markets, this process increasingly operates through sustainable investment products and allocation decisions based on ESG-related criteria and disclosures. Financial markets – particularly asset and wealth management – therefore play a pivotal role in translating sustainability objectives into economic outcomes (Schramade, 2017). Accordingly, sustainability accounting and management research increasingly views financial institutions as organizational actors whose practices shape accountability, incentives and the impact of corporate sustainability initiatives on the SDGs (Folqué et al., 2023).
From an accounting and management perspective, sustainable finance is not simply about offering “green” products, but about building organizational and informational infrastructures that integrate sustainability into financial decision-making. This requires accountability mechanisms, performance metrics and disclosure systems that translate environmental and social objectives into investable criteria. Although non-financial reporting has expanded, its effectiveness depends on whether information is credible, decision-relevant and usable in practice (Dilla et al., 2019; Schwertner and Sohn, 2024). Thus, sustainable finance depends not only on what information is produced, but also on how it is interpreted and mobilized in organizational decision processes (Hutchings, 2025; Pilaj, 2017; Sun and Xia, 2025).
Despite this expansion, the informational infrastructure of sustainable finance remains fragile. Sustainability disclosures are often complex, fragmented and difficult to interpret, particularly for non-professional investors (Bazrafshan, 2023). A growing literature documents investors’ difficulties in distinguishing among ESG signals, assessing their credibility and evaluating their real-world impact (Li et al., 2025). These challenges are compounded by concerns about greenwashing [1] and by limited transparency in sustainability data (Treepongkaruna et al., 2024). As a result, the potential of sustainability accounting to steer capital toward sustainable development remains only partially realized.
In this context, financial advisors emerge as key informational intermediaries between firms’ sustainability information and capital allocation. Individual investors rarely engage directly with corporate disclosures or ESG metrics, relying instead on advisors to interpret, filter and frame sustainability information (Paetzold et al., 2015). In the European Union (EU), advisors represent the primary source of financial information for many individual investors (European Commission, 2022), effectively acting as organizational gatekeepers of sustainability by shaping which ESG signals become salient, credible and actionable. Advisory practices thus form a crucial – yet underexamined – layer of sustainability governance linking corporate reporting, regulation and private capital allocation.
Recognizing this intermediation role, EU regulators have sought to strengthen the sustainability information infrastructure of financial markets. The 2022 update of Markets in Financial Instruments Directive II (MiFID II) [2] requires advisors to elicit clients’ sustainability preferences and disclose product characteristics. However, formal compliance does not guarantee meaningful integration of sustainability into investment decisions, as regulatory effectiveness depends on how sustainability information is communicated within advisory relationships and perceived by investors. Recent evidence therefore calls for a better understanding of whether and how EU sustainable finance policies reorient private capital toward sustainable activities (Hoerler et al., 2025).
Despite the growing relevance of these dynamics, sustainability accounting and management research has paid limited attention to the micro-level processes through which sustainability information is mediated in private wealth management. Little is known about how advisors and investors interpret ESG information, how trust and perceived effectiveness form and how these perceptions translate into portfolio allocations (Li et al., 2025). This gap is particularly evident for high-net-worth individuals (HNWIs), whose investment decisions can significantly influence financial markets yet remain underexplored (Paetzold et al., 2022).
Drawing on survey data from 800 HNWIs collected by the Italian Private Banking Association (AIPB) in 2023, this study addresses these gaps by examining how sustainability information and advisory practices influence private wealth allocation toward sustainable investments (SI) following the EU MiFID amendments. Using self-reported portfolio holdings to capture actual behavior rather than intentions, we analyze how advisor-provided sustainability information shapes trust in ESG claims, perceived effectiveness of SI and portfolio choices. In doing so, the study connects to sustainable development by examining a specific organizational mechanism through which sustainability-related information may become decision-relevant in private wealth allocation. Rather than treating sustainable investment as a purely financial or behavioral choice, the paper shows how advisory relationships, disclosure practices and regulatory requirements jointly shape the conditions under which private capital can be oriented toward sustainability objectives (Folqué et al., 2023; Hoerler et al., 2025; Li et al., 2025).
Positioned at the intersection of sustainable finance, management and sustainability accounting, this study makes three contributions to an emerging shared field of inquiry (Lin et al., 2025). First, it conceptualizes financial advisors as organizational actors within the sustainability information infrastructure linking firms, markets and private investors. Second, it shows how mandated and discretionary sustainability information affect investments, trust and perceived effectiveness in private wealth decision-making. Third, it provides policy-relevant evidence on how EU sustainable finance regulation, particularly MiFID II sustainability requirements, can support sustainable development by making regulatory disclosure understandable, credible and actionable within advisory relationships.
The remainder of the paper proceeds as follows. Section 2 reviews the literature. Section 3 describes the data and methodology. Section 4 presents the results, Section 5 discusses the findings and Section 6 concludes with limitations and future research directions.
2. Literature review
This section reviews the interdisciplinary literature underpinning the study’s conceptual framework, integrating insights from sustainability accounting, management and finance.
2.1 Sustainable development, firm-level outcomes and investor preferences
SI are increasingly viewed as a key channel through which financial markets can contribute to the achievement of the SDGs. Given the scale and complexity of these goals, their attainment requires coordinated action across public and private actors, with private capital playing a complementary role to public policy and corporate sustainability initiatives (Betti et al., 2018; Scheyvens et al., 2016). Prior research emphasizes that sustainable development strategies are effective only if investors are willing to allocate capital in line with sustainability objectives (Folqué et al., 2023).
More specifically, SI may contribute to sustainable development through a demand-side transmission mechanism. By demanding sustainability-oriented products, individual investors can influence financial intermediaries, asset managers and firms to integrate ESG criteria into investment, lending and corporate decision-making processes (Pilaj, 2017). This does not imply an automatic link between SI ownership and sustainable development outcomes; rather, investor demand can strengthen the market and organizational conditions under which sustainability-related criteria become financially relevant. Private capital allocation may thus affect firms’ access to capital, encourage corporate social responsibility (CSR) practices and support innovation trajectories aligned with environmental and social objectives (Tan, 2022). Consistently, evidence that sustainable finance instruments can stimulate green innovation both within issuing firms and among industry peers points to a broader diffusion mechanism through which sustainable finance may support sustainability-oriented change (Tang et al., 2025). This channel is particularly relevant for HNWIs, whose financial capacity may amplify the influence of private investor preferences on sustainable finance markets.
Beyond societal outcomes, sustainability-oriented capital allocation can generate firm-level effects relevant to management and governance, including lower cost of capital (Gillan et al., 2021), higher valuation (Bouslah et al., 2023) and stronger performance (Erhemjamts et al., 2013). These effects are often attributed to improved transparency, lower information asymmetries and accountability mechanisms embedded in sustainability reporting and governance.
Against this backdrop, investor preferences are essential for understanding the demand side of sustainable finance and its implications for firm management and sustainable development outcomes (Hutchings, 2025; Li et al., 2025). While classical asset pricing models focus on risk–return considerations, recent research incorporates values and ESG considerations into financial decision-making (Pedersen et al., 2021; Starks, 2023). Empirical studies document how sustainability preferences can be shaped by individuals’ psychological, social and sociodemographic factors (Gutsche et al., 2021; He and Shi, 2023; Nilsson, 2008; Riedl and Smeets, 2017). However, these preferences are also context dependent: they vary across institutional environments and depend on the availability, quality and framing of sustainability-related information (Pasquino and Lucarelli, 2025). Sustainability preferences therefore become actionable only within specific informational and organizational contexts, shifting attention to the credibility and interpretation of non-financial disclosures and to the actors who mediate them.
In light of the above, the research question guiding this study is the following:
What factors and motivations shape HNWIs’ allocation of private wealth toward sustainable financial products rather than traditional alternatives?
2.2 Sustainability information, credibility and trust
A substantial literature shows that sustainability information influences individual investors’ decisions (Barreda-Tarrazona et al., 2011; Hafenstein and Bassen, 2016). Sustainability accounting and disclosure practices translate environmental and social objectives into decision-relevant information and investors increasingly rely on ESG disclosures when allocating capital (Amel-Zadeh and Serafeim, 2018; Cohen et al., 2011). This has driven the diffusion of reporting frameworks, ESG ratings and product disclosures over the past two decades (KPMG, 2022; Rouen et al., 2024).
Research shows investors use sustainability information for screening, engagement and portfolio construction (Daugaard et al., 2024). Empirical evidence confirms these effects: retail sustainability screening is associated with higher SI allocation (Dilla et al., 2016) and simplified disclosures increase investment in firms with stronger sustainability profiles (Hutchings, 2025). Investors also incorporate signals related to climate exposure and adaptation into decisions, particularly when they inform long-term risk and resilience assessments (Alam et al., 2023).
Despite this expansion, sustainability disclosures remain complex, heterogeneous and difficult to compare, especially for private investors (Bazrafshan, 2023; Nazari et al., 2017). Information overload, inconsistent metrics and diverse standards increase cognitive burden and reduce the usability of sustainability information for investment decision-making (Filippini et al., 2024; Pombinho et al., 2024). Greenwashing concerns further undermine the effectiveness of sustainability information. When sustainability claims appear weakly substantiated, trust in non-financial disclosures declines, reducing their usefulness (Treepongkaruna et al., 2024). Accounting research shows disclosure effects on investment behavior depend strongly on perceived credibility: transparent information reduces asymmetries and fosters trust, whereas low credibility hinders sustainable investment (Cohen et al., 2011; Shahid et al., 2024). Consistently, Azmat et al. (2022) document growing concerns about “impact washing”.
When credibility is low, sustainability accounting’s ability to steer capital toward sustainable development weakens. Preferences for SI are therefore conditional on the informational environment in which decisions occur. This shifts attention from disclosure alone to the organizational and relational contexts in which sustainability information is interpreted and mobilized, highlighting the importance of intermediaries who filter and communicate such information. This view aligns with research conceptualizing accounting as an organizational practice shaping accountability and decision relevance, rather than merely a reporting output (Li et al., 2025).
2.3 Financial advisors as organizational intermediaries
Investors’ difficulty in interpreting sustainability information contributes to persistent asymmetries, limiting their ability to independently integrate sustainability considerations into decisions (Anderson and Robinson, 2022; Gutsche and Zwergel, 2020) and weakening firms’ efforts to communicate sustainability performance through ESG disclosures. In this context, financial advisors emerge as critical informational intermediaries, supporting the exchange and use of information to increase the value of the information for its end user or to reduce the cost of information acquisition (Lee and Cho, 2005). Research highlights that advisors can guide investors through complex sustainability information and support SI adoption (Heinemann et al., 2018; Pilaj, 2017; Strauß, 2021). Practitioner evidence similarly indicates that although many investors express interest in sustainable investing, they often lack the knowledge needed to translate intentions into portfolio choices (Morgan Stanley, 2024; OECD, 2017).
Within advisory relationships, advisors actively filter, frame and simplify financial and non-financial information, shaping the salience and perceived credibility of sustainability claims. By selecting which ESG attributes to emphasize and linking sustainability considerations to clients’ financial objectives, advisors influence how sustainability information becomes decision relevant. Despite some contributions on advisory practices in sustainable finance (Laudi et al., 2022; Linciano et al., 2020; Paetzold et al., 2015; Valor et al., 2009), their role as intermediaries between corporate sustainability disclosures and private investors remains underexplored.
Regulatory initiatives have sought to address this gap. In the European Union, the 2022 amendments to the MiFID II framework require advisors to elicit clients’ sustainability preferences and disclose sustainability characteristics of financial products. However, compliance with formal disclosure requirements does not necessarily ensure meaningful integration of sustainability information into investment decisions. Recent research highlights that it remains unclear how EU sustainable finance regulation affects the reorientation of private capital and through which mechanisms it shapes investor behavior (Hoerler et al., 2025).
This role is particularly salient in private wealth management and among HNWIs. HNWIs manage complex portfolios and rely on personalized advisory services integrating asset structures, tax considerations and behavioral factors (Driga et al., 2009; Jennings et al., 2011). Given their substantial financial resources, their investment choices can disproportionately influence markets and corporate behavior. In Italy, advisors serving HNWIs oversaw more than EUR 1,257bn in assets in 2024, underscoring the segment’s relevance [3]. However, HNWIs remain underrepresented in sustainable finance research, largely due to limited behavioral data (Paetzold et al., 2022).
While prior studies examine preferences and disclosure effects, less is known about how advisory practices shape trust in sustainability information, perceived effectiveness and actual portfolio allocations, particularly in wealth management. Consequently, the organizational processes through which sustainability accounting influences private capital allocation remain insufficiently understood, a gap that is especially salient following the 2022 MiFID II amendments. Building on the importance of accountability in sustainability information and non-financial disclosure, this study conceptualizes advisors as organizational gatekeepers mediating between investor preferences, sustainability information and investment outcomes. By examining how advisor-provided sustainability information influences HNWIs’ perceptions and choices of SI, the study contributes to understanding how private wealth management can support the mobilization of private capital toward sustainable development under evolving EU regulations.
Building on this discussion, we test the following hypothesis:
Sustainability-related information within advisory relationships is positively associated with HNWIs’ sustainable investment choices.
To further unpack the mechanisms through which sustainability-related information may influence investment behavior, we focus on the perceptual conditions under which such information becomes decision-relevant. As discussed above, the literature suggests that sustainability-related information matters when it is perceived as credible and as capable of producing meaningful real-world effects. In our empirical setting, these dimensions are captured by perceived effectiveness of sustainable investments and trust in sustainability disclosures. Accordingly, we test the following additional hypotheses:
The relationship between sustainability-related information within advisory relationships and HNWIs’ sustainable investment choices is mediated by investors’ perceptions of the effectiveness of sustainable investments.
The relationship between sustainability-related information within advisory relationships and HNWIs’ sustainable investment choices is mediated by investors’ trust in sustainability disclosures.
3. Data collection and methodology
The data used in this study were collected through a survey conducted by the AIPB, a non-profit professional organization established in 2004 to represent and promote Italy’s private banking sector. Private banking refers to a service model focused on managing and protecting family wealth, tailored to a specific client segment with complex needs who seek access to advanced advisory services for a personalized, dynamic and high-performing approach to wealth management.
This section describes the AIPB survey, the empirical models used in the analyses and the variables included in the research.
3.1 AIPB survey data
This paper draws on the 2023 edition of AIPB’s annual survey of private banking clients, conducted between June 21 and July 10, 2023. The sample includes 800 private banking clients – i.e. individuals or households with financial assets exceeding EUR 500,000. Italy represents a relevant case because it is a major European market with strong reliance on financial advisors for investment decisions (CONSOB, 2024). Access to the AIPB data set provides novel empirical evidence on HNWIs’ portfolio choices and advisor interactions, representing an invaluable opportunity to gather detailed insights otherwise unavailable
The survey reports the financial products held by respondents, allowing us to analyze actual self-reported portfolio allocations, with particular attention to SI ownership. The primary advantage of this data set lies in its foundation on actual decisions and behaviors, given this information is collected indirectly by the advisors in charge of their financial planning.
3.2 The empirical model: what explains HNWI’s choices
Some studies infer investors’ revealed preferences for SI from fund flows and performance data (Bansal et al., 2022). While this approach captures actual investment behavior at scale, it does not allow for the identification of individual non-pecuniary motives, which cannot be directly observed through indirect measures (Gutsche and Ziegler, 2019). Survey-based approaches, despite being subject to recall bias, enable a more granular examination of the factors shaping investors’ sustainable investment choices and have therefore been widely used in the literature (Cucinelli and Soana, 2023; Junkus and Berry, 2010; Linciano et al., 2020; Nilsson, 2008; Rossi et al., 2019).
Equation (1) is set to study the impact of different factors on investors’ holdings among distinct financial instruments, estimated though logistic regressions due to the binary nature of the dependent variables considered:
where Yi denotes a set of binary indicators capturing respondents’ ownership of five distinct financial products (equal to 1 if the individual holds a given asset and 0 otherwise). The vector Xi includes independent variables grouped into four conceptual clusters, each analyzed through separate model specifications. The vector Ci includes control variables capturing respondents’ sociodemographic and baseline characteristics, while εi denotes the error term. The full list of variables, together with the corresponding survey question and coding procedure, is reported in Table 1, while the survey questions are described in detail in Section 1 of the Supplementary Material.
Overview of the study variables, survey questions and coding
| Cluster | Variable | Question | Measurement/coding |
|---|---|---|---|
| Dependent variable | Ownership of financial product (list of five options) | / | Dummy: selected = 1; otherwise = 0 |
| Control variables – socio-demographic characteristics | Age | Q1 | Continuous numerical variable |
| Gender | Q2 | Dummy: Female = 1; Male = 0 | |
| Resp 1 | Q3 | Dummy: main responsible = 1; otherwise = 0 | |
| Resp 2 | Q4 | Ordinal variable (1–5) | |
| Geographical area | Q5 | Categorical variable | |
| Education | Q6 | Ordinal variable (1–10) | |
| 1 – ESG profile | Sustainability engagement | Q7 | Dummy: Yes = 1; No = 0 |
| Perceived effectiveness | Q8 | Ordinal variable (1–5) | |
| Trust | Q9 | Ordinal variable (−1–1) | |
| 2 – Financial motivations | Financial motivation (list of 8 options) | Q10 | Dummy: selected among top three motivations = 1; otherwise = 0 |
| LT horizon | Q11 | Dummy: Yes = 1; No = 0 | |
| 3 – Alternativeness | Alternative investments (list of 3 options) | Q12 | Dummy: selected = 1; otherwise = 0 |
| 4 – The role of financial advisors | Advisor years | Q13 | Ordinal variable (1–5) |
| Advisor frequency | Q14 | Ordinal variable (1–6) | |
| Advisor reliance | Q15 | Ordinal variable (1–5) | |
| Info SI | Q16 | Dummy: Yes = 1; No = 0 | |
| Info climate change | Q17 | Dummy: Yes = 1; No = 0 |
| Cluster | Variable | Question | Measurement/coding |
|---|---|---|---|
| Dependent variable | Ownership of financial product (list of five options) | / | Dummy: selected = 1; otherwise = 0 |
| Control variables – socio-demographic characteristics | Age | Q1 | Continuous numerical variable |
| Gender | Q2 | Dummy: Female = 1; Male = 0 | |
| Resp 1 | Q3 | Dummy: main responsible = 1; otherwise = 0 | |
| Resp 2 | Q4 | Ordinal variable (1–5) | |
| Geographical area | Q5 | Categorical variable | |
| Education | Q6 | Ordinal variable (1–10) | |
| 1 – | Sustainability engagement | Q7 | Dummy: Yes = 1; No = 0 |
| Perceived effectiveness | Q8 | Ordinal variable (1–5) | |
| Trust | Q9 | Ordinal variable (−1–1) | |
| 2 – Financial motivations | Financial motivation (list of 8 options) | Q10 | Dummy: selected among top three motivations = 1; otherwise = 0 |
| Q11 | Dummy: Yes = 1; No = 0 | ||
| 3 – Alternativeness | Alternative investments (list of 3 options) | Q12 | Dummy: selected = 1; otherwise = 0 |
| 4 – The role of financial advisors | Advisor years | Q13 | Ordinal variable (1–5) |
| Advisor frequency | Q14 | Ordinal variable (1–6) | |
| Advisor reliance | Q15 | Ordinal variable (1–5) | |
| Info | Q16 | Dummy: Yes = 1; No = 0 | |
| Info climate change | Q17 | Dummy: Yes = 1; No = 0 |
Questions (Qs) are reported in the Supplementary Material (S1)
Because logistic regression coefficients are expressed in log-odds and are not directly comparable across model specifications due to non-collapsibility, we report average marginal effects (AMEs), following Howell‐Moroney (2024). AMEs express the average change in predicted probability associated with a one-unit change in each explanatory variable, holding other covariates constant. Regression tables reporting the estimated coefficients of the logistic regression are provided in Section 2 of the Supplementary Material.
3.2.1 Dependent variables- ownership of financial products.
The dependent variables are five binary indicators capturing whether respondents report owning each financial product: SI, equity stocks, funds, corporate bonds and government bonds. The variables capture product ownership, not invested amounts. While SI ownership is the main outcome, the other asset classes allow us to compare whether the determinants of SI differ from those of conventional financial products.
3.2.2 Independent variables – four model specifications.
The independent variables are organized into four conceptual clusters, each capturing a complementary set of factors identified in the literature as relevant for sustainable investment decisions. This structure allows us to examine the determinants associated with HNWIs’ allocation toward sustainable financial products relative to other assets (RQ). At the same time, it enables us to test H1 by assessing the role of financial advisors and sustainability-related information in shaping portfolio choices. Sociodemographic characteristics are included as controls, namely, age, gender, responsibility for household financial decisions, geographic area of residence and educational attainment. The independent variables are as follows:
3.2.3 Cluster 1 – investors’ ESG profile.
The first cluster captures investors’ sustainability orientation and their perceptions of the credibility and effectiveness of SI. Prior research shows that sustainable investment behavior is shaped by heterogeneous motivations, ranging from ethical concerns to financial considerations, often combined in hybrid approaches (Nilsson, 2008; Starks, 2023). To capture this orientation within the advisory context of private banking, we construct the variable sustainability engagement, reflecting respondents’ preference for discussing climate-related issues with their financial advisor.
Beyond underlying orientation, sustainable investment decisions depend on how sustainability-related claims are perceived in terms of credibility and real-world impact. As discussed in Section 2, sustainability disclosures and ESG claims are often complex and ambiguous, which can raise concerns about greenwashing and limit investors’ ability to evaluate sustainability information autonomously (Dilla et al., 2019; Treepongkaruna et al., 2024). In addition, doubts about whether sustainability initiatives translate into meaningful corporate outcomes may undermine the perceived integrity of sustainable investments (Azmat et al., 2022). Accordingly, we include two additional variables: trust and perceived effectiveness.
Trust captures investors’ confidence in the credibility of sustainability claims made by firms and financial institutions, while perceived effectiveness reflects the belief that sustainable investments can generate real improvements in corporate sustainability practices, drawing on the concept of perceived consumer effectiveness (Straughan and Roberts, 1999). Together, these variables capture how sustainability accounting and ESG disclosures become decision-relevant for investors by shaping the perceived credibility and effectiveness of sustainability-related information.
3.2.4 Cluster 2 – financial motivations.
As previously noted, sustainable investment decisions are shaped by both financial and non-financial motivations. While Cluster 1 addresses the non-financial dimension of “values”, this model focuses on financially material factors (“values” and “value” perspective of Starks, 2023) influencing sustainable investment behavior. Participants selected their top three investment motivations from a predefined list:
saving progression (Savings);
avoiding cash holdings (No Cash);
risk diversification (Diversification);
achieving life goals (Life Goals);
wealth accumulation (Accumulate Wealth);
capital preservation (Preserve Value);
leaving a legacy (Legacy); and
profit opportunities (Profits).
In addition, the model includes a variable for long-term investment orientation (LT Horizon). Time preferences are critical in sustainable investing, as socially responsible firms are often viewed as better suited for long-term strategies (Sethi, 2005). However, evidence on time-horizon on individual investors’ ESG preferences remains mixed (Gutsche et al., 2021; Riedl and Smeets, 2017).
3.2.5 Cluster 3 – propensity to alternative investments (alternativeness).
The third cluster captures investors’ propensity toward alternative investments, allowing us to examine whether SI holdings are associated with broader portfolio patterns rather than isolated choices. Specifically, this cluster includes binary indicators for ownership of cryptocurrencies and digital assets, as well as participation in crowdfunding investments.
The literature on the relationship between SI and alternative asset classes is still limited and inconclusive. In the case of cryptocurrencies, prior studies document substantial environmental concerns related to mining and blockchain infrastructures (Tang et al., 2019), while empirical evidence on the link between ESG orientation and crypto ownership remains mixed (Baeckström et al., 2025; Zeb et al., 2025). Similarly, research on sustainability-oriented crowdfunding suggests heterogeneous investor motivations and outcomes across contexts (Hornuf et al., 2022; Mendoza et al., 2023; Vismara, 2019). However, to date, no academic studies have specifically examined the relationship between individual investors’ ownership of SI and their participation in crowdfunding projects.
Rather than advancing new theoretical claims, this cluster is included to control for alternative investment behavior and to assess whether sustainable investment holdings are embedded within broader patterns of portfolio diversification and innovation-oriented investing.
3.2.6 Cluster 4 – the role of financial advisors.
Financial advisors play a central role as informational intermediaries between sustainability disclosures and individual investment decisions, especially given private investors’ lack of expertise (ESMA, 2023). As discussed in Sections 1 and 2, sustainability accounting and disclosure practices influence investment behavior primarily through how information is interpreted, framed and communicated within organizational contexts, making the client–advisor relationship a critical locus of analysis.
Based on survey responses, this cluster captures three structural dimensions of the advisory relationship:
the duration of the client–advisor relationship (measured in years);
the frequency of interactions between the investor and the advisor; and
the degree of reliance placed on the advisor in investment decision-making.
Together, these variables reflect the intensity and embeddedness of the advisory relationship through which sustainability-related information is mediated.
Given the centrality of information in sustainable investment decisions, the analysis further examines the content of advisory interactions with respect to sustainability. In particular, we distinguish between (i) proposals of sustainable financial products made by advisors, reflecting compliance with minimum sustainability-related disclosure requirements under the EU MiFID II framework and (ii) broader, discretionary discussions on sustainability-related topics, such as climate change, during advisory meetings. This distinction allows us to capture both regulatory-driven disclosure and advisors’ proactive engagement in sustainability communication, which may differentially shape investors’ choices. Taken together, these variables capture the main dimensions through which sustainability-related information is conveyed within advisory relationships and therefore provide the empirical basis for testing H1.
3.3. A mediation analysis: the role of financial advisors and information
To examine the mechanisms through which sustainability-related information influences sustainable investment decisions, we conducted a mediation analysis testing whether the relationship between sustainability-related information within advisory relationships and SI ownership operates indirectly through investors’ perceptions of the effectiveness of SI (H2a) and their trust in sustainability disclosures (H2b). This approach allows us to assess whether advisory-mediated information shapes portfolio allocation by strengthening the cognitive and informational conditions under which sustainability claims are acted upon.
Following established approaches in recent empirical research (Daniel et al., 2023; Hasford et al., 2022), we use Hayes (2017) PROCESS macro for R (Model 6) to estimate a serial mediation model, with bootstrap 95% confidence intervals for indirect effects.
3.4 Description of sample characteristics
Table 2 reports descriptive statistics for the sample, including frequencies for binary variables and mean, standard deviation, minimum and maximum values for numerical variables.
Descriptive statistics
| Cluster | Variable | % on N = 800 | Mean | SD | Min. | Max. |
|---|---|---|---|---|---|---|
| Dependent variables – ownership of financial products | Sustainable investments | 29.38 | ||||
| Equity stocks | 73.25 | |||||
| Funds | 94.63 | |||||
| Corporate bonds | 77.88 | |||||
| Government bonds | 52.62 | |||||
| Control variables – socio – demographic characteristics) | Age | 55.44 | 13.51 | 21 | 86 | |
| Gender female | 26.13 | |||||
| Resp 1 | 77.12 | 0 | 1 | |||
| Resp 2 | 3.90 | 1.26 | 1 | 5 | ||
| Area North-West | 48.25 | |||||
| Area North-East | 28.88 | |||||
| Area South | 8.25 | |||||
| Area Centre | 14.62 | |||||
| Education | 8.12 | 1.26 | 3 | 10 | ||
| Cluster 1 – ESG profile | Sustainability engagement | 21.00 | ||||
| PE | 2.72 | 0.77 | 1 | 4 | ||
| Trust | 0.025 | 0.82 | −1 | 1 | ||
| Cluster 2 – Financial motivations | Saving | 36.13 | ||||
| No cash | 39.50 | |||||
| Diversification | 36.75 | |||||
| Life goals | 32.12 | |||||
| Accumulate wealth | 17.5 | |||||
| Preserve capital | 52.88 | |||||
| Legacy | 28.75 | |||||
| Profits | 47.75 | |||||
| LT horizon | 59.38 | |||||
| Cluster 3 – Alternativeness | Crypto investm. | 13.25 | ||||
| DA investm. | 7.88 | |||||
| CF investm. | 12.00 | |||||
| Cluster 4 – The role of financial advisors | Advisor years | 4.03 | 1.04 | 1 | 5 | |
| Advisor frequency | 3.60 | 1.08 | 1 | 6 | ||
| Advisor reliance | 3.32 | 1.13 | 1 | 5 | ||
| Info SI | 56.13 | |||||
| Info climate change | 17.25 |
| Cluster | Variable | % on N = 800 | Mean | Min. | Max. | |
|---|---|---|---|---|---|---|
| Dependent variables – ownership of financial products | Sustainable investments | 29.38 | ||||
| Equity stocks | 73.25 | |||||
| Funds | 94.63 | |||||
| Corporate bonds | 77.88 | |||||
| Government bonds | 52.62 | |||||
| Control variables – socio – demographic characteristics) | Age | 55.44 | 13.51 | 21 | 86 | |
| Gender female | 26.13 | |||||
| Resp 1 | 77.12 | 0 | 1 | |||
| Resp 2 | 3.90 | 1.26 | 1 | 5 | ||
| Area North-West | 48.25 | |||||
| Area North-East | 28.88 | |||||
| Area South | 8.25 | |||||
| Area Centre | 14.62 | |||||
| Education | 8.12 | 1.26 | 3 | 10 | ||
| Cluster 1 – | Sustainability engagement | 21.00 | ||||
| 2.72 | 0.77 | 1 | 4 | |||
| Trust | 0.025 | 0.82 | −1 | 1 | ||
| Cluster 2 – Financial motivations | Saving | 36.13 | ||||
| No cash | 39.50 | |||||
| Diversification | 36.75 | |||||
| Life goals | 32.12 | |||||
| Accumulate wealth | 17.5 | |||||
| Preserve capital | 52.88 | |||||
| Legacy | 28.75 | |||||
| Profits | 47.75 | |||||
| 59.38 | ||||||
| Cluster 3 – Alternativeness | Crypto investm. | 13.25 | ||||
| 7.88 | ||||||
| 12.00 | ||||||
| Cluster 4 – The role of financial advisors | Advisor years | 4.03 | 1.04 | 1 | 5 | |
| Advisor frequency | 3.60 | 1.08 | 1 | 6 | ||
| Advisor reliance | 3.32 | 1.13 | 1 | 5 | ||
| Info | 56.13 | |||||
| Info climate change | 17.25 |
3.4.1 Investments in different financial products.
This section presents a tetrachoric correlation analysis of the five binary financial product ownership variables. Tetrachoric correlation is appropriate given the dichotomous nature of the data (Brown, 1977). Table 3 shows that SI ownership is moderately correlated with mutual fund ownership and weakly correlated with corporate bonds, while correlations among other products are negligible. These patterns suggest that sustainable investors tend to hold diversified portfolios combining sustainable and conventional instruments.
4. Empirical findings
The following sections provide a detailed examination according to the different model specifications. Every model specification analysis also includes sociodemographic features as control variables [4]. For each model specification, we graphically report the AMEs of the independent variables on the probability of owning each financial product. In the graphs, bar colors indicate the statistical significance of the effects (p < 0.01; p < 0.05; p < 0.1; or non-significant).
4.1 Model specification 1 – investors’ ESG profile
Table 4 shows how investors’ sustainability orientation and their perceptions of SI shape portfolio allocation decisions. Sustainability engagement is positively associated with ownership of SI, increasing the probability of holding them by 16.1 percentage points (p < 0.01), while it is largely unrelated to most other asset classes. This confirms that a favorable orientation toward sustainability represents an important precondition for sustainable investment choices.
Investors’ ESG profile
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | SI | Stocks | Funds | Corp. bonds | Gov. bonds |
| Sustainability engagement | 0.161*** (0.034) | −0.036 (0.039) | 0.014 (0.022) | 0.002 (0.037) | 0.084* (0.043) |
| PE | 0.141*** (0.021) | 0.037* (0.021) | 0.028*** (0.010) | 0.052*** (0.019) | −0.061*** (0.023) |
| Trust | 0.068*** (0.018) | 0.013 (0.019) | −0.005 (0.010) | 0.003 (0.018) | 0.026 (0.021) |
| AIC | 854.4 | 929.2 | 332.9 | 839.1 | 1067.9 |
| BIC | 910.6 | 985.4 | 389.1 | 895.3 | 1124.1 |
| RMSE | 0.42 | 0.44 | 0.22 | 0.41 | 0.48 |
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | Stocks | Funds | Corp. bonds | Gov. bonds | |
| Sustainability engagement | 0.161 | −0.036 (0.039) | 0.014 (0.022) | 0.002 (0.037) | 0.084 |
| 0.141 | 0.037 | 0.028 | 0.052 | −0.061 | |
| Trust | 0.068 | 0.013 (0.019) | −0.005 (0.010) | 0.003 (0.018) | 0.026 (0.021) |
| 854.4 | 929.2 | 332.9 | 839.1 | 1067.9 | |
| 910.6 | 985.4 | 389.1 | 895.3 | 1124.1 | |
| 0.42 | 0.44 | 0.22 | 0.41 | 0.48 | |
The table shows average marginal effects (AMEs) from logistic regression. Control variables include age, gender, level of responsibility for household financial decisions, geographic origin and educational attainment. Full tables with control variables are available upon request. Statistical significance levels: p < 0.1 (*), p < 0.05 (**), p < 0.01 (***)
Beyond underlying orientation, perceptions of credibility and effectiveness emerge as key determinants of SI ownership. Perceived effectiveness (PE) – the belief that SI can generate meaningful environmental or social outcomes – shows a strong positive association with SI ownership (AME = 0.141, p < 0.01). Unlike sustainability engagement, PE is also related to holdings of equities, funds and corporate bonds, while being negatively associated with government bonds. Trust in sustainability claims is likewise positively associated with SI ownership (AME = 0.068, p < 0.01), but does not affect other financial instruments, highlighting the specific importance of credibility for sustainability-labelled investments. Figure 1 reports these results graphically.
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across sustainability engagement, perceived effectiveness and trust. For sustainability engagement, S I has the largest positive effect at about 0.16, government bonds are about 0.08, funds are about 0.01, corporate bonds are near 0, and stocks are negative at about negative 0.04. For perceived effectiveness, S I is about 0.14, corporate bonds about 0.05, stocks about 0.04, funds about 0.03 and government bonds negative at about negative 0.06. For trust, S I is about 0.07, government bonds about 0.03, stocks about 0.01, corporate bonds are near 0 and funds are slightly negative. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of independent variables on the probability of owning each financial product (Model 1 – investors’ ESG profile)
Source: Authors’ own work
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across sustainability engagement, perceived effectiveness and trust. For sustainability engagement, S I has the largest positive effect at about 0.16, government bonds are about 0.08, funds are about 0.01, corporate bonds are near 0, and stocks are negative at about negative 0.04. For perceived effectiveness, S I is about 0.14, corporate bonds about 0.05, stocks about 0.04, funds about 0.03 and government bonds negative at about negative 0.06. For trust, S I is about 0.07, government bonds about 0.03, stocks about 0.01, corporate bonds are near 0 and funds are slightly negative. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of independent variables on the probability of owning each financial product (Model 1 – investors’ ESG profile)
Source: Authors’ own work
Taken together, these findings indicate that sustainability engagement alone is not sufficient to translate pro-sustainability attitudes into investment decisions, as these also depend on whether sustainability-related information is perceived as credible and effective. For example, although government bond ownership is positively associated with sustainability engagement, its negative relationship with PE and lack of association with trust suggest that doubts about SI can override favorable orientations. By contrast, investments in corporate bonds, funds and equities are positively associated with PE but not with sustainability engagement or trust, highlighting the distinct role of impact-related beliefs relative to ethical predispositions.
Overall, these results show that HNWIs’ SI choices depend on both sustainability orientation (He and Shi, 2023; Kaur et al., 2024) and informational conditions. The decision usefulness of sustainability accounting and ESG disclosures depends critically on perceived credibility and effectiveness; greenwashing concerns and doubts about real-world impact may therefore weaken the translation of sustainability orientations into capital allocation (Azmat et al., 2022; Dilla et al., 2019; Schwertner and Sohn, 2024).
4.2 Model specification 2 – financial motivations
Table 5, together with Figure 2, represents the relationship between investors’ financial motivations and their portfolio allocations. Among the various motivations, Diversification shows the strongest and most significant association with SI ownership, increasing the probability of owning these products by 18.0 percentage points (p < 0.01). Other motivations exhibit more limited and asset-specific effects. Life goals are weakly associated with SI and mutual funds (p < 0.1), while wealth accumulation and profit-seeking motivations are primarily linked to equity and mutual fund ownership, consistent with their return-oriented nature. A long-term investment horizon is positively and significantly associated with SI ownership (AME = 0.107, p < 0.01), supporting the view that SI are more likely to be considered within long-term portfolio strategies (Riedl and Smeets, 2017).
Financial motivations
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | SI | Stocks | Funds | Corp. bonds | Gov. bonds |
| Savings | 0.103 (0.069) | 0.016 (0.059) | 0.025 (0.026) | −0.034 (0.059) | 0.030 (0.069) |
| No cash | 0.086 (0.068) | 0.054 (0.059) | 0.045* (0.027) | −0.069 (0.059) | −0.072 (0.069) |
| Diversification | 0.180*** (0.067) | 0.121** (0.058) | 0.029 (0.025) | 0.062 (0.059) | 0.045 (0.067) |
| Life goals | 0.133* (0.069) | 0.031 (0.060) | 0.044* (0.027) | 0.029 (0.060) | 0.019 (0.069) |
| Accumulate wealth | 0.083 (0.075) | 0.178*** (0.068) | 0.012 (0.028) | 0.008 (0.065) | −0.046 (0.075) |
| Preserve capital | 0.114* (0.065) | 0.119** (0.056) | 0.068*** (0.026) | −0.060 (0.056) | 0.081 (0.065) |
| Legacy | 0.107 (0.069) | 0.007 (0.059) | 0.045* (0.026) | 0.012 (0.060) | 0.084 (0.070) |
| Profits | 0.051 (0.066) | 0.063 (0.056) | 0.075*** (0.026) | −0.036 (0.056) | 0.036 (0.066) |
| LT horizon | 0.107*** (0.033) | 0.035 (0.032) | 0.030* (0.017) | −0.051 (0.031) | 0.005 (0.036) |
| AIC | 936.2 | 920.1 | 334.7 | 837.9 | 1072.7 |
| BIC | 1020.5 | 1004.4 | 419.1 | 922.2 | 1157.1 |
| RMSE | 0.44 | 0.43 | 0.22 | 0.40 | 0.48 |
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | Stocks | Funds | Corp. bonds | Gov. bonds | |
| Savings | 0.103 (0.069) | 0.016 (0.059) | 0.025 (0.026) | −0.034 (0.059) | 0.030 (0.069) |
| No cash | 0.086 (0.068) | 0.054 (0.059) | 0.045 | −0.069 (0.059) | −0.072 (0.069) |
| Diversification | 0.180 | 0.121 | 0.029 (0.025) | 0.062 (0.059) | 0.045 (0.067) |
| Life goals | 0.133 | 0.031 (0.060) | 0.044 | 0.029 (0.060) | 0.019 (0.069) |
| Accumulate wealth | 0.083 (0.075) | 0.178 | 0.012 (0.028) | 0.008 (0.065) | −0.046 (0.075) |
| Preserve capital | 0.114 | 0.119 | 0.068 | −0.060 (0.056) | 0.081 (0.065) |
| Legacy | 0.107 (0.069) | 0.007 (0.059) | 0.045 | 0.012 (0.060) | 0.084 (0.070) |
| Profits | 0.051 (0.066) | 0.063 (0.056) | 0.075 | −0.036 (0.056) | 0.036 (0.066) |
| 0.107 | 0.035 (0.032) | 0.030 | −0.051 (0.031) | 0.005 (0.036) | |
| 936.2 | 920.1 | 334.7 | 837.9 | 1072.7 | |
| 1020.5 | 1004.4 | 419.1 | 922.2 | 1157.1 | |
| 0.44 | 0.43 | 0.22 | 0.40 | 0.48 | |
The table shows average marginal effects (AMEs) from logistic regression. Control variables include age, gender, level of responsibility for household financial decisions, geographic origin and educational attainment. Full tables with control variables are available upon request. Statistical significance levels: p < 0.1 (*), p < 0.05 (**), p < 0.01 (***)
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across savings, no cash, diversification, goals, wealth, capital, legacy, profits and long term horizon. The vertical axis ranges from negative 0.1 to 0.2. S I has positive effects throughout, peaking near 0.18 for diversification and about 0.13 for goals. Stocks peak near 0.18 for wealth and 0.12 for diversification and capital. Funds have smaller positive effects, reaching about 0.08 for profits and 0.07 for capital. Corporate bonds have negative effects for savings, no cash, capital, profits and long term horizon, reaching about negative 0.07 for no cash. Government bonds are negative for no cash and wealth, at about negative 0.07 and negative 0.05, but positive for capital and legacy at about 0.08. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of independent variables on the probability of owning each financial product (Model 2 – financial motivations)
Source: Authors’ own work
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across savings, no cash, diversification, goals, wealth, capital, legacy, profits and long term horizon. The vertical axis ranges from negative 0.1 to 0.2. S I has positive effects throughout, peaking near 0.18 for diversification and about 0.13 for goals. Stocks peak near 0.18 for wealth and 0.12 for diversification and capital. Funds have smaller positive effects, reaching about 0.08 for profits and 0.07 for capital. Corporate bonds have negative effects for savings, no cash, capital, profits and long term horizon, reaching about negative 0.07 for no cash. Government bonds are negative for no cash and wealth, at about negative 0.07 and negative 0.05, but positive for capital and legacy at about 0.08. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of independent variables on the probability of owning each financial product (Model 2 – financial motivations)
Source: Authors’ own work
These results support the view that sustainable investors combine financial and non-financial motives (Pedersen et al., 2021; Starks, 2023). Financial motivations shape howSI are incorporated into portfolios, while SI adoption remains strongly associated with sustainability orientation and perceived credibility and effectiveness.
4.3 Model specification 3 – alternativeness
Table 6 and Figure 3 examine the relationship between SI ownership and holdings of alternative investment products. The results indicate that HNWIs who hold cryptocurrency assets are significantly more likely to invest in SI (AME = 0.129, p < 0.01) and corporate bonds (AME = 0.146, p < 0.05), while displaying a weaker negative association with government bonds (AME = −0.098, p < 0.1).
Alternativeness
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | SI | Stocks | Funds | Corp. bonds | Gov. bonds |
| Crypto investm. | 0.129*** (0.045) | 0.050 (0.055) | 0.024 (0.033) | 0.146** (0.062) | −0.098* (0.056) |
| DA investm. | 0.221*** (0.055) | 0.044 (0.071) | 0.006 (0.040) | 0.116 (0.083) | 0.022 (0.070) |
| CF investm. | 0.148*** (0.046) | 0.094 (0.058) | 0.013 (0.032) | 0.104* (0.060) | 0.067 (0.057) |
| AIC | 886.5 | 927.5 | 341.5 | 826.2 | 1075.2 |
| BIC | 942.8 | 983.7 | 397.7 | 882.4 | 1131.4 |
| RMSE | 0.42 | 0.44 | 0.22 | 0.40 | 0.48 |
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | Stocks | Funds | Corp. bonds | Gov. bonds | |
| Crypto investm. | 0.129 | 0.050 (0.055) | 0.024 (0.033) | 0.146 | −0.098 |
| 0.221 | 0.044 (0.071) | 0.006 (0.040) | 0.116 (0.083) | 0.022 (0.070) | |
| 0.148 | 0.094 (0.058) | 0.013 (0.032) | 0.104 | 0.067 (0.057) | |
| 886.5 | 927.5 | 341.5 | 826.2 | 1075.2 | |
| 942.8 | 983.7 | 397.7 | 882.4 | 1131.4 | |
| 0.42 | 0.44 | 0.22 | 0.40 | 0.48 | |
The table shows average marginal effects (AMEs) from logistic regression. Control variables include age, gender, level of responsibility for household financial decisions, geographic origin and educational attainment. Full tables with control variables are available upon request. Statistical significance levels: p < 0.1 (*), p < 0.05 (**), p < 0.01 (***)
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across crypto, D A investment and C F investment. The vertical axis ranges from negative 0.15 to 0.25. For crypto, corporate bonds have the largest positive effect at about 0.145, followed by S I at about 0.13, stocks at 0.05 and funds at 0.025, while government bonds are negative at about negative 0.10. For D A investment, S I is highest at about 0.22, followed by corporate bonds at about 0.115, stocks at 0.045, government bonds at 0.02 and funds near 0.005. For C F investment, S I is about 0.15, corporate bonds about 0.105, stocks about 0.095, government bonds about 0.07 and funds about 0.01. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of independent variables on the probability of owning each financial product (Model 3 – alternativeness)
Source: Authors’ own work
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across crypto, D A investment and C F investment. The vertical axis ranges from negative 0.15 to 0.25. For crypto, corporate bonds have the largest positive effect at about 0.145, followed by S I at about 0.13, stocks at 0.05 and funds at 0.025, while government bonds are negative at about negative 0.10. For D A investment, S I is highest at about 0.22, followed by corporate bonds at about 0.115, stocks at 0.045, government bonds at 0.02 and funds near 0.005. For C F investment, S I is about 0.15, corporate bonds about 0.105, stocks about 0.095, government bonds about 0.07 and funds about 0.01. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of independent variables on the probability of owning each financial product (Model 3 – alternativeness)
Source: Authors’ own work
Similarly, ownership of digital assets and participation in crowdfunding investments are positively and exclusively associated with SI ownership (p < 0.01), with no significant relationships observed for other financial products. These findings suggest that SI tend to co-occur with a broader propensity toward alternative asset classes within HNWIs’ portfolios.
These findings suggest that SI ownership is embedded within diversified and innovation-oriented portfolio structures, consistent with prior evidence on the coexistence of sustainability orientation and alternative investment behaviors (Baeckström et al., 2025; Ciaian et al., 2024; Mendoza et al., 2023; Vismara, 2019), rather than representing an isolated ethical choice. Overall, the model contextualizes HNWIs’ sustainable investment choices within broader allocation patterns involving complex and less standardized financial products.
4.4 Model specification 4 – financial advisors as informational intermediaries
Table 7 and Figure 4 report the results used to test H1 on the role of financial advisors in shaping HNWIs’ portfolio allocation. The analysis considers three dimensions of the advisor–client relationship: relationship duration (advisor years), interaction frequency (advisor frequency) and reliance on the advisor for investment decisions (advisor reliance). Relationship duration shows no significant association with SI ownership, while being negatively related to equity holdings and weakly positively associated with government bonds.
The role of financial advisors
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | SI | Stocks | Funds | Corp. bonds | Gov. bonds |
| Advisor years | 0.020 (0.016) | −0.047*** (0.016) | 0.003 (0.008) | −0.015 (0.015) | 0.029* (0.017) |
| Advisor frequency | 0.058*** (0.014) | 0.040*** (0.015) | 0.006 (0.008) | 0.035** (0.014) | 0.035** (0.016) |
| Advisor reliance | −0.043*** (0.014) | −0.016 (0.014) | −0.010 (0.007) | −0.006 (0.013) | 0.062*** (0.015) |
| Info SI | 0.163*** (0.031) | 0.103*** (0.031) | 0.054*** (0.019) | 0.044 (0.030) | 0.003 (0.035) |
| Info climate change | 0.126*** (0.036) | −0.091** (0.041) | 0.009 (0.028) | −0.024 (0.041) | 0.062 (0.047) |
| AIC | 870.3 | 904.0 | 330.3 | 839.6 | 1057.8 |
| BIC | 935.9 | 969.5 | 395.8 | 905.2 | 1123.4 |
| RMSE | 0.42 | 0.43 | 0.22 | 0.41 | 0.48 |
| Dependent variable | |||||
|---|---|---|---|---|---|
| Independent variable | Stocks | Funds | Corp. bonds | Gov. bonds | |
| Advisor years | 0.020 (0.016) | −0.047 | 0.003 (0.008) | −0.015 (0.015) | 0.029 |
| Advisor frequency | 0.058 | 0.040 | 0.006 (0.008) | 0.035 | 0.035 |
| Advisor reliance | −0.043 | −0.016 (0.014) | −0.010 (0.007) | −0.006 (0.013) | 0.062 |
| Info | 0.163 | 0.103 | 0.054 | 0.044 (0.030) | 0.003 (0.035) |
| Info climate change | 0.126 | −0.091 | 0.009 (0.028) | −0.024 (0.041) | 0.062 (0.047) |
| 870.3 | 904.0 | 330.3 | 839.6 | 1057.8 | |
| 935.9 | 969.5 | 395.8 | 905.2 | 1123.4 | |
| 0.42 | 0.43 | 0.22 | 0.41 | 0.48 | |
The table shows average marginal effects (AMEs) from logistic regression. Control variables include age, gender, level of responsibility for household financial decisions, geographic origin and educational attainment. Full tables with control variables are available upon request. Statistical significance levels: p < 0.1 (*), p < 0.05 (**), p < 0.01 (***)
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across adviser years, adviser frequency, adviser reliance, information on S I and information on climate change. The vertical axis ranges from negative 0.15 to 0.2. For adviser years, stocks have the largest negative effect at about negative 0.05, while government bonds are positive at about 0.03. For adviser frequency, all effects are positive, led by S I at about 0.06 and stocks at about 0.04. For adviser reliance, S I, stocks, funds and corporate bonds are slightly negative, while government bonds are positive at about 0.06. For information on S I, all effects are positive, led by S I at about 0.16 and stocks at about 0.10. For information on climate change, S I is positive at about 0.13, stocks are negative at about negative 0.09, funds are near 0.01, corporate bonds are about negative 0.02 and government bonds are about 0.06. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of the independent variables on the probability of owning each financial product (Model 4 – the role of financial advisors)
Source: Authors’ own work
The grouped bar chart compares average marginal effects for S I, stocks, funds, corporate bonds and government bonds across adviser years, adviser frequency, adviser reliance, information on S I and information on climate change. The vertical axis ranges from negative 0.15 to 0.2. For adviser years, stocks have the largest negative effect at about negative 0.05, while government bonds are positive at about 0.03. For adviser frequency, all effects are positive, led by S I at about 0.06 and stocks at about 0.04. For adviser reliance, S I, stocks, funds and corporate bonds are slightly negative, while government bonds are positive at about 0.06. For information on S I, all effects are positive, led by S I at about 0.16 and stocks at about 0.10. For information on climate change, S I is positive at about 0.13, stocks are negative at about negative 0.09, funds are near 0.01, corporate bonds are about negative 0.02 and government bonds are about 0.06. Bar styles indicate p less than 0.01, p less than 0.05, p less than 0.1 and not significant.AMEs of the independent variables on the probability of owning each financial product (Model 4 – the role of financial advisors)
Source: Authors’ own work
By contrast, advisor frequency is positively associated with ownership across most asset classes, with the strongest effect observed for SI (AME = 0.058, p < 0.01). This pattern indicates that more frequent interactions increase exposure to investment opportunities in general, while being particularly relevant for sustainability-oriented products characterized by higher informational complexity. Conversely, advisor reliance is negatively associated with SI ownership (AME = −0.043, p < 0.01) and positively associated with government bonds. Consistent with the framework developed in Sections 1 and 2, these results suggest that SI adoption is linked to informational engagement rather than passive delegation of decision-making.
The findings further highlight the importance of sustainability-related information within advisory interactions. Info SI, indicating whether respondents received a sustainability-related investment proposal, is positively associated with ownership of SI, equities and funds. This result is consistent with the objectives of the 2022 MiFID II amendments, which require advisors to elicit sustainability preferences and integrate ESG considerations into suitability assessments, providing empirical support for the effectiveness of regulation-driven disclosures in increasing exposure to ESG-labelled products. Importantly, discretionary and content-rich sustainability communication plays an even more decisive role. Info climate change, capturing broader discussions on the impacts of the climate crisis, is strongly and positively associated with SI ownership (AME = 0.126, p < 0.01), while being negatively associated with equity holdings and unrelated to other asset classes.
These results suggest that regulatory compliance facilitates exposure to sustainable products, but deeper sustainability communication within advisory relationships is more strongly associated with SI ownership. They also show that advisors act as organizational intermediaries of sustainability information, shaping how ESG disclosures are interpreted and acted upon by private investors (Heinemann et al., 2018; Pilaj, 2017). By distinguishing mandatory disclosure from discretionary sustainability communication, the study extends prior research on advisory influence (Laudi et al., 2022; Linciano et al., 2020; Paetzold et al., 2015) and responds to calls to examine how MiFID II-related sustainability information infrastructures affect investor behavior (Hoerler et al., 2025). Overall, these findings support H1 and suggest that sustainability accounting can reorient private capital when disclosure is effectively mediated within advisory relationships.
4.5 Robustness check
As a robustness check, we re-estimate all models using a probit specification. The results closely mirror the logistic regressions in coefficient signs and statistical significance, with differences only for a few marginally significant variables. Full probit results are reported in Section 3 of the Supplementary Material.
4.6 Findings of the indirect path analysis: PROCESS mediation models estimation
To examine the mechanisms through which advisor interactions influence SI ownership, we estimate a serial mediation model using Hayes (2017) PROCESS Macro. Based on the results in Table 6, Advisor Frequency is selected as the independent variable, while Info TOT combines Info SI (mandatory information aligned with MiFID II) and info climate change (broader discussions on climate-related issues) to capture the overall intensity of sustainability-related information within advisory interactions.
In line with the conceptual framework developed in Sections 1 and 2, the analysis focuses on two perceptual dimensions that condition the decision usefulness of ESG disclosures: perceived effectiveness (PE) and Trust. Figure 5 illustrates the two serial mediation pathways examined.
The flowchart begins with frequency of exchanges with a financial adviser, which leads directly to Info T O T. Two dashed paths extend from Info T O T: the upper path leads to perceived effectiveness, and the lower path leads to trust. Perceived effectiveness and trust then connect through dashed arrows to ownership of S I.Flowchart of mediation pathways
Source: Authors’ own work
The flowchart begins with frequency of exchanges with a financial adviser, which leads directly to Info T O T. Two dashed paths extend from Info T O T: the upper path leads to perceived effectiveness, and the lower path leads to trust. Perceived effectiveness and trust then connect through dashed arrows to ownership of S I.Flowchart of mediation pathways
Source: Authors’ own work
The first model (Figure 6) tests H2a by examining whether sustainability-related information within advisory relationships affects SI ownership indirectly through investors’ perceptions of the effectiveness of SI (PE). Results show that advisor frequency significantly increases sustainability information provision (β = 0.11, p < 0.01), which in turn enhances perceived effectiveness of SI (β = 0.09, p < 0.05), ultimately increasing the likelihood of SI ownership (β = 0.80, p < 0.01). Table 8 provides a detailed output, showing a significant serial mediation effect (IND3) based on 95% bootstrap confidence intervals.
The path diagram presents positive relationships among frequency of exchanges with a financial adviser, Info T O T, perceived effectiveness and ownership of S I. Frequency of exchanges leads to Info T O T with a coefficient of 0.11 marked by 3 asterisks, perceived effectiveness with 0.09 marked by 3 asterisks, and ownership of S I with 0.33 marked by 3 asterisks. Info T O T leads to perceived effectiveness with 0.09 marked by 2 asterisks and to ownership of S I with 0.91 marked by 3 asterisks. Perceived effectiveness leads to ownership of S I with 0.80 marked by 3 asterisksConceptual model of mediation analysis: the influence of financial advisors on perceived effectiveness
Source: Authors’ own work
The path diagram presents positive relationships among frequency of exchanges with a financial adviser, Info T O T, perceived effectiveness and ownership of S I. Frequency of exchanges leads to Info T O T with a coefficient of 0.11 marked by 3 asterisks, perceived effectiveness with 0.09 marked by 3 asterisks, and ownership of S I with 0.33 marked by 3 asterisks. Info T O T leads to perceived effectiveness with 0.09 marked by 2 asterisks and to ownership of S I with 0.91 marked by 3 asterisks. Perceived effectiveness leads to ownership of S I with 0.80 marked by 3 asterisksConceptual model of mediation analysis: the influence of financial advisors on perceived effectiveness
Source: Authors’ own work
PROCESS Macro estimates of mediation analysis: the influence of financial advisors on perceived effectiveness
| Model terms/effects | Coefficient | SE | t | p |
|---|---|---|---|---|
| Dependent variable: Info TOT | ||||
| Constant | 0.341 | 0.083 | 4.101 | 0.000 |
| Advisor frequency | 0.109 | 0.022 | 4.918 | 0.000 |
| Dependent variable: PE | ||||
| Constant | 2.350 | 0.095 | 24.662 | 0.000 |
| Advisor frequency | 0.086 | 0.025 | 3.377 | 0.000 |
| Info TOT | 0.086 | 0.040 | 2.146 | 0.032 |
| Dependent variable: ownership of SI | ||||
| Constant | −5.097 | 0.510 | −9.982 | 0.000 |
| Advisor frequency | 0.328 | 0.081 | 4.050 | 0.000 |
| Info TOT | 0.908 | 0.127 | 7.137 | 0.000 |
| PE | 0.796 | 0.127 | 6.252 | 0.000 |
| Indirect effects of advisor on ownership of SI | ||||
| Effect | BootSE | BootLLCI | BootULCI | |
| IND1 (AF → INFOT → SI) | 0.099 | 0.025 | 0.055 | 0.156 |
| IND2 (AF → PE → SI) | 0.069 | 0.023 | 0.026 | 0.116 |
| IND3 (AF → INFOT → PE → SI) | 0.008 | 0.004 | 0.001 | 0.016 |
| Model terms/effects | Coefficient | t | p | |
|---|---|---|---|---|
| Dependent variable: Info | ||||
| Constant | 0.341 | 0.083 | 4.101 | 0.000 |
| Advisor frequency | 0.109 | 0.022 | 4.918 | 0.000 |
| Dependent variable: | ||||
| Constant | 2.350 | 0.095 | 24.662 | 0.000 |
| Advisor frequency | 0.086 | 0.025 | 3.377 | 0.000 |
| Info | 0.086 | 0.040 | 2.146 | 0.032 |
| Dependent variable: ownership of | ||||
| Constant | −5.097 | 0.510 | −9.982 | 0.000 |
| Advisor frequency | 0.328 | 0.081 | 4.050 | 0.000 |
| Info | 0.908 | 0.127 | 7.137 | 0.000 |
| 0.796 | 0.127 | 6.252 | 0.000 | |
| Indirect effects of advisor on ownership of | ||||
| Effect | BootSE | BootLLCI | BootULCI | |
| IND1 ( | 0.099 | 0.025 | 0.055 | 0.156 |
| IND2 ( | 0.069 | 0.023 | 0.026 | 0.116 |
| IND3 ( | 0.008 | 0.004 | 0.001 | 0.016 |
The effects have been estimated through bootstrap technique with 95% confidence intervals. Indirect effects (IND1 IND2 and IND3) can be considered statistically significant when the range between BootLLCI and BootULCI does not contain 0. In the bottom part of the table: AF = advisor frequency; INFOT = Info TOT; PE = perceived effectiveness; SI = ownership of SI
The second model (Figure 7) tests H2b by examining whether sustainability-related information within advisory relationships affects SI ownership indirectly through investors’ trust in sustainability disclosures. As in the previous model, advisor frequency positively affects Info TOT (β = 0.11, p < 0.01), which increases trust in sustainability communications (β = 0.13, p < 0.01), thereby raising SI ownership (β = 0.50, p < 0.01). The serial mediation effect (IND3) is again statistically significant, as reported in Table 9.
The path diagram presents relationships among frequency of exchanges with a financial adviser, Info T O T, trust and ownership of S I. Frequency of exchanges leads to Info T O T with a coefficient of 0.11 marked by 3 asterisks, trust with 0.02 marked not significant, and ownership of S I with 0.35 marked by 3 asterisks. Info T O T leads to trust with 0.13 marked by 3 asterisks and to ownership of S I with 0.85 marked by 3 asterisks. Trust leads to ownership of S I with 0.50 marked by 3 asterisks.Conceptual model of mediation analysis: the influence of financial advisors on trust
Source: Authors’ own work
The path diagram presents relationships among frequency of exchanges with a financial adviser, Info T O T, trust and ownership of S I. Frequency of exchanges leads to Info T O T with a coefficient of 0.11 marked by 3 asterisks, trust with 0.02 marked not significant, and ownership of S I with 0.35 marked by 3 asterisks. Info T O T leads to trust with 0.13 marked by 3 asterisks and to ownership of S I with 0.85 marked by 3 asterisks. Trust leads to ownership of S I with 0.50 marked by 3 asterisks.Conceptual model of mediation analysis: the influence of financial advisors on trust
Source: Authors’ own work
PROCESS Macro estimates of mediation analysis: the influence of financial advisors on trust
| Model terms/effects | Coefficient | SE | t | p |
|---|---|---|---|---|
| Dependent variable: info TOT | ||||
| Constant | 0.341 | 0.083 | 4.101 | 0.000 |
| Advisor frequency | 0.109 | 0.022 | 4.918 | 0.000 |
| Dependent variable: trust | ||||
| Constant | −0.127 | 0.102 | −1.244 | 0.214 |
| Advisor frequency | 0.016 | 0.027 | 0.596 | 0.551 |
| Info TOT | 0.127 | 0.043 | 2.964 | 0.003 |
| Dependent variable: ownership of SI | ||||
| Constant | −2.919 | 0.323 | −9.035 | 0.000 |
| Advisor frequency | 0.351 | 0.079 | 4.432 | 0.000 |
| Info TOT | 0.851 | 0.125 | 6.810 | 0.000 |
| Trust | 0.501 | 0.102 | 4.928 | 0.000 |
| Indirect effects of advisor on ownership of SI | ||||
| Effect | BootSE | BootLLCI | BootULCI | |
| IND1 (AF → INFOT → SI) | 0.093 | 0.023 | 0.054 | 0.169 |
| IND2 (AF → TR → SI) | 0.008 | 0.014 | −0.018 | 0.036 |
| IND3 (AF → INFOT → TR → SI) | 0.007 | 0.003 | 0.002 | 0.015 |
| Model terms/effects | Coefficient | t | p | |
|---|---|---|---|---|
| Dependent variable: info | ||||
| Constant | 0.341 | 0.083 | 4.101 | 0.000 |
| Advisor frequency | 0.109 | 0.022 | 4.918 | 0.000 |
| Dependent variable: trust | ||||
| Constant | −0.127 | 0.102 | −1.244 | 0.214 |
| Advisor frequency | 0.016 | 0.027 | 0.596 | 0.551 |
| Info | 0.127 | 0.043 | 2.964 | 0.003 |
| Dependent variable: ownership of | ||||
| Constant | −2.919 | 0.323 | −9.035 | 0.000 |
| Advisor frequency | 0.351 | 0.079 | 4.432 | 0.000 |
| Info | 0.851 | 0.125 | 6.810 | 0.000 |
| Trust | 0.501 | 0.102 | 4.928 | 0.000 |
| Indirect effects of advisor on ownership of | ||||
| Effect | BootSE | BootLLCI | BootULCI | |
| IND1 ( | 0.093 | 0.023 | 0.054 | 0.169 |
| IND2 ( | 0.008 | 0.014 | −0.018 | 0.036 |
| IND3 ( | 0.007 | 0.003 | 0.002 | 0.015 |
The effects have been estimated through bootstrap technique with 95% confidence intervals. Indirect effects (IND1 IND2 and IND3) can be considered statistically significant when the range between BootLLCI and BootULCI does not contain 0. In the bottom part of the table: AF = advisor frequency; INFOT = info TOT; TR = trust; SI = ownership of SI
Overall, the mediation analyses indicate that advisors influence HNWIs’ SI ownership primarily through informational mechanisms rather than passive delegation. More frequent advisory interactions increase exposure to sustainability-related information, which shapes perceived effectiveness and trust.
These findings align with sustainability accounting research showing that ESG disclosures affect investment behavior when perceived as credible and decision-relevant (Cohen et al., 2011; Shahid et al., 2024). Advisory interactions help investors interpret complex sustainability information that is difficult to assess autonomously (Bazrafshan, 2023; Pombinho et al., 2024). Specifically, advisor-mediated information is associated with higher perceived effectiveness of SI and greater trust in sustainability claims, addressing concerns about real-world impact and greenwashing highlighted in prior research (Azmat et al., 2022; Nilsson, 2008; Treepongkaruna et al., 2024). Overall, these results support H2a and H2b and show that sustainability accounting shapes private investment behavior partly through the mediation and interpretation of sustainability information within advisory relationships.
5. Discussion
Scholars widely recognize that mobilizing private capital from private individual investors is essential for advancing sustainable economies and supporting socially responsible development (Tan, 2022). This study examines how private capital can be connected to sustainable development by analyzing the informational and organizational conditions under which HNWIs allocate wealth to sustainable investment products. Building on sustainability accounting and management research that views financial institutions as organizational actors shaping accountability and capital allocation (Folqué et al., 2023; Lin et al., 2025), our findings show that disclosure effectiveness depends not only on information availability but on how it is mediated, interpreted and trusted within advisory relationships.
Across models, investors’ sustainability orientation is a necessary but insufficient condition for SI adoption. While stronger engagement increases the likelihood of holding SI, this effect also depends on perceived credibility and effectiveness. Consistent with research on the decision usefulness of non-financial disclosures (Dilla et al., 2019; Shahid et al., 2024), trust in sustainability claims and beliefs about SI’s real-world impact are crucial for translating ethical orientations into portfolio choices. These results reinforce concerns about greenwashing and impact washing, which weaken sustainability accounting’s ability to steer capital toward sustainable development (Azmat et al., 2022).
Financial motivations and portfolio composition show that SI ownership is associated with diversification, long-term horizons and alternative assets rather than short-term profit seeking. This suggests that HNWIs treat SI as part of broader and more complex portfolio strategies (Pedersen et al., 2021; Starks, 2023), increasing the relevance of informational intermediaries who can help investors interpret sustainability-related claims. Collectively, the results help answer the RQ by showing that HNWIs’ allocation toward sustainable investment products is shaped not only by sustainability orientation, but also by broader portfolio motivations and investment strategies.
The study’s central contribution emerges most clearly in the analysis of financial advisors. Consistent with literature identifying advisors as informational intermediaries (Heinemann et al., 2018; Pilaj, 2017), advisory relationships influence SI adoption mainly through informational and relational mechanisms rather than passive delegation. Frequent interactions increase SI ownership, whereas full reliance on advisors is negatively related to it, suggesting that sustainability-oriented investors remain actively involved and use advisors as sources of information and discussion.
Crucially, the depth of sustainability communication matters more than formal disclosure alone. While sustainability-related product proposals introduced under the 2022 MiFID II amendments increase SI exposure, discretionary and contextualized discussions – especially on climate change – are more strongly associated with SI ownership. This responds to calls to examine how regulatory-driven sustainability information infrastructures operate in practice (Hoerler et al., 2025), suggesting that compliance creates necessary conditions, but information mediation determines whether sustainability accounting becomes decision relevant. These results therefore provide support for H1.
Mediation analysis further shows that advisor interactions affect SI adoption indirectly by shaping trust and perceived effectiveness. This aligns with research showing that non-financial information influences investment behavior only when seen as credible, understandable and impactful (Dilla et al., 2019; Hutchings, 2025; Schwertner and Sohn, 2024). Since investors rarely engage directly with corporate sustainability reports, advisors form part of the sustainability information infrastructure that translates complex ESG disclosures into actionable knowledge. Overall, the mediation results support H2a and H2b by showing that advisor-mediated sustainability information operates through trust and perceived effectiveness.
Overall, our findings suggest that research focusing exclusively on corporate sustainability disclosure risks overlooking a critical layer of mediation. Sustainability accounting can contribute to the achievement of the SDGs only when embedded within organizational and relational processes that render information usable for investors, and when financial systems are able to translate sustainability objectives into actual capital allocation decisions (Folqué et al., 2023; Schramade, 2017). By empirically showing how financial advisors shape the interpretation and credibility of sustainability information, this study reinforces the view that the effectiveness of sustainability accounting depends not only on what firms disclose, but on how such disclosure is mobilized within financial decision-making practices (Li et al., 2025).
5.1 Scientific contribution
This paper makes two scientific contributions at the intersection of sustainability accounting, management and sustainable finance. First, it advances sustainability accounting and management research by conceptualizing financial advisors as organizational actors within the sustainability information infrastructure linking firms, financial markets and private investors. While existing research has largely focused on the production and effects of corporate sustainability disclosures (Sun and Xia, 2025), this study shows that their influence on capital allocation depends on how such information is mediated within organizational and relational settings, responding to calls to examine how sustainability accounting becomes decision-relevant in practice (Folqué et al., 2023; Lin et al., 2025).
Second, the paper contributes to sustainable investment behavior research by showing how mandated and discretionary sustainability information shape private wealth decision-making. SI adoption among HNWIs depends not only on sustainability orientation, but also on trust in sustainability disclosures and perceived real-world effectiveness. The study therefore clarifies the mechanisms through which ESG information affects investment behavior and how greenwashing and impact-washing concerns constrain sustainability accounting’s capacity to guide private capital (Azmat et al., 2022; Dilla et al., 2016; Li et al., 2025). By focusing on HNWIs, it also advances knowledge on an underrepresented yet influential investor segment (Paetzold et al., 2022).
Overall, this work shows that the effectiveness of non-financial disclosure depends not only on reporting standards and regulatory frameworks, but also on the organizational and relational contexts in which sustainability information is used. By highlighting the role of financial advisors as informational gatekeepers between corporate ESG disclosure and private wealth, the paper contributes to a more integrated understanding of accounting, management and capital allocation processes.
5.2 Managerial and policy implications
The findings offer implications for practitioners and policymakers in sustainable finance. For private banks and wealth managers, SI adoption among HNWIs appears to depend less on product availability than on the quality of sustainability-related communication within advisory relationships. Strengthening advisors’ ESG literacy and interpretive capabilities can help translate complex sustainability disclosures into credible and decision-relevant information, reinforcing trust and perceived effectiveness among clients.
This evidence provides further support to the ongoing debate on the effectiveness of recent EU sustainable finance regulations, particularly the 2022 amendments to MiFID II. By distinguishing between regulatory-driven sustainability disclosures and discretionary, value-oriented sustainability communication within advisory interactions, our study offers empirical insights into how regulatory information requirements translate into investor behavior. This directly addresses gaps identified in recent reviews of EU sustainable finance initiatives regarding how regulation reorients private capital toward sustainable development goals (Hoerler et al., 2025).
From a general perspective, the development of credible, transparent and standardized sustainability rating systems emerges as a key policy priority. Clearer and more consistent ESG ratings would not only strengthen investor confidence in sustainability claims, but also facilitate advisors’ intermediary role by enhancing effective communication of sustainability characteristics. Recent EU initiatives, such as the Omnibus Package aimed at streamlining corporate sustainability reporting, might represent a step in this direction. Better alignment between reporting standards, rating systems and advisory practices would strengthen the sustainability information infrastructure through which private banks, advisors and regulators can support more credible and effective capital allocation toward sustainable activities and ultimately support sustainable development goals.
6. Conclusive remarks
This study examined how sustainability-related information influences private wealth allocation toward sustainable financial products, focusing on financial advisors as intermediaries between corporate ESG disclosures and individual investment decisions. Using survey data from 800 Italian HNWIs, the findings show that SI adoption depends not only on sustainability orientation, but also on the credibility, perceived effectiveness and advisory mediation of sustainability information. By addressing our RQ and supporting H1, H2a and H2b, the results indicate that HNWIs’ sustainable investment choices depend not only on investors’ sustainability orientation, but also on how sustainability-related information is communicated and perceived within advisory relationships. Overall, our findings suggest that the capacity of sustainability accounting to reorient private capital depends not only on disclosure requirements, but also on the organizational context in which such information is used. This reinforces the idea that financial intermediation plays a crucial role in translating sustainability objectives into investment decisions and capital allocation processes that may support sustainable development (Schramade, 2017).
Despite its contributions, this study has several limitations. First, the data are drawn from a survey designed by the AIPB, reflecting industry practices rather than standardized academic measurement frameworks. While this limits comparability, it offers valuable insights into real-world decision-making among HNWIs. Second, reliance on self-reported data may introduce biases. Recall bias is mitigated by focusing on product ownership rather than portfolio shares, consistent with prior studies (Lanciano et al., 2025). Social desirability bias is reduced through anonymization, which has been shown to improve response accuracy (Gnambs and Kaspar, 2015; Joinson, 1999). Although hypothetical bias may arise in non-incentivized settings, recent evidence suggests that it does not materially affect conclusions on SI preferences (Engler et al., 2025). Overall, self-declared revealed preferences remain a well-established approach in this literature (Cucinelli and Soana, 2023; Junkus and Berry, 2010; Nilsson, 2008; Rossi et al., 2019). A third limitation concerns the link between SI ownership and sustainable development outcomes. While the data allow us to examine how sustainability-related information shapes HNWIs’ allocation toward SI, they do not allow us to assess the environmental or social impact of the underlying products. The study should therefore be interpreted as evidence on an informational and organizational mechanism supporting sustainable capital allocation, rather than as a direct assessment of sustainable development impact. Finally, this study focuses on a single-country context. While Italy represents a relevant case due to the strong reliance of HNWIs on financial advisors, cross-country differences in institutional and regulatory settings may shape both sustainable investment demand and advisory practices.
Future research could build on this study by adopting standardized measures of ESG attitudes, as well as longitudinal or experimental designs to examine how sustainable investment behavior evolves in response to informational and regulatory changes. Extending the analysis to other countries, investor segments and advisory models – including robo-advisors – would further enhance understanding. Given the central role of financial advisors identified here, exploring how different communication styles and informational strategies affect investors’ trust and perceived effectiveness represents a particularly promising avenue for future research.
The author gratefully acknowledges Italian Private Banking Association (AIPB) for providing access to the survey data used in this study. Their support was essential for the completion of this research. The views and interpretations expressed in this paper are solely those of the author and do not necessarily reflect those of AIPB.
Notes
The European Securities and Markets Authority (ESMA) defines greenwashing as “a practice where sustainability-related statements, declarations, actions or communications do not clearly and fairly reflect the underlying sustainability profile of an entity, a financial product or financial service. This practice may be misleading to consumers, investors or other market participants” (ESMA, 2023).
MiFID II (Markets in Financial Instruments Directive II) is a European Union regulatory framework, introduced in 2018, that strengthens investor protection, increases transparency in financial markets, and sets requirements for financial institutions on the provision of investment advice and product governance.
AIPB press release, 24 March 2025. Available at: Linl to aipbLink to the cited article
For the sake of brevity, sociodemographic variables are not reported in the output tables but are available from the authors upon request.
References
Supplementary material
The Supplementary material for this article can be found online

