A Quantitative Analysis of Sustainable Finance Preferences: Choice Patterns, Personality Traits and Gender in SDG 7 Investments

Abstract

The analysis carried out in this work shows that sustainable investment decisions aimed at SDG 7 are mainly driven by objective financial attributes, especially the level of risk and the type of providing institution. The empirical analysis is based on 873 valid responses, balanced by gender and income levels, which enables us to capture heterogeneity in sustainable investment preferences. This study contributes to the literature by jointly examining personality traits and gender as explanatory factors of willingness to pay for investments aligned with SDG 7. In the general model, strong risk aversion—particularly to high risk—and a positive valuation of cooperatives stand out over factors such as explicit reference to SDG 7 or personality traits, which are not significant. Gender segmentation reveals substantial differences: women display a much higher risk aversion and a greater willingness to pay for investing in cooperatives and, to a lesser extent, in sustainable institutions; in this group, extraversion is negatively associated with the choice of SDG 7 funds. For men, risk remains key but with lower penalization, and provider type carries more moderate weight; no relevant link with personality traits is detected. Thus, the gender effect hypothesis is fully confirmed, while the personality hypothesis is partially supported. These results suggest that the design of sustainable financial products should be a WTP adapted to differentiate demographic and behavioral profiles in order to mobilize private capital toward the energy transition.

1. Introduction

Investment in clean energy projects, aligned with Sustainable Development Goal (SDG) 7, has become critically relevant on the global agenda due to growing concerns about global warming and the need for an energy transition. However, the mobilization of capital towards these initiatives depends not only on objective economic factors but also on the preferences and intrinsic characteristics of individual investors. Behavioral finance theory has challenged the assumption that investors are purely rational, revealing that investment decisions are significantly influenced by various psychological, emotional, and cognitive factors.

Among the most studied psychological factors are personality traits, often categorized under the Big Five model: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (Jiang et al. 2024; Tauni et al. 2020; Sarwar et al. 2020; Shaheen et al. 2025; Isidore and Arun 2022; Muñoz-Muñoz et al. 2025). This framework is considered the most comprehensive and has demonstrated its validity and generalizability in various cultures and languages, as previous research has established correlations between personality traits and investment decisions, risk perception, and social interaction trends (Brown and Taylor 2014; Von Gaudecker 2015; Jiang et al. 2024; Isidore and Arun 2022; Moore and McElroy 2012; Tauni et al. 2020; Fraj and Martinez 2006). For example, high neuroticism is related to risk aversion and not investing or saving (Gambetti and Giusberti 2019), while extraversion and awareness are positively associated with futures trading (Tauni et al. 2020). Personality not only directly influences portfolio decisions, but also through standard channels (beliefs and preferences) and non-standard (non-pecuniary factors such as social interaction or a “target portfolio”) (Jiang et al. 2024; Müser et al. 2024; Shaheen et al. 2025).

In the context of sustainable investments, agreeableness and conscientiousness have been positively correlated with pro-environmental behavior, while neuroticism and extraversion have shown negative or no relationships with pro-environmental attitudes or green consumption intentions (Soutter et al. 2020; Fachrudin et al. 2022). Attitudes towards green consumption, in turn, can mediate the relationship between personality traits and the decision to invest sustainably (Fachrudin et al. 2022).

In addition to personality, gender differences are a key determinant of investor behavior (Friedl et al. 2020; Lawrenson and Dickason-Koekemoer 2020; Tauni et al. 2020). Numerous studies have documented that women tend to be more risk-averse than men, valuing the possibility of loss and ambiguity more (Canikli and Aren 2018; Durmaz Bodur et al. 2023; Lawrenson and Dickason-Koekemoer 2020; Friedl et al. 2020; Holden and Tilahun 2022; Lepinteur et al. 2022; Davydov et al. 2017; Babalos et al. 2015; Bayyurt et al. 2013; Jacobsen et al. 2014). It has also been suggested that women spend more effort gathering information before making investment decisions, unlike men who may rely on more obvious signals (Tauni et al. 2020).

However, the magnitude of these gender differences can vary and be influenced by factors such as financial literacy, initial resource endowments, or cultural context (Marinelli et al. 2017; Friedl et al. 2020). Even though demographic characteristics, including gender, are commonly used as control variables, some studies still find a significant “gender gap” in investment decisions that is not fully explained by these factors (Friedl et al. 2020; Holden and Tilahun 2022; Lawrenson and Dickason-Koekemoer 2020; Tauni et al. 2020).

Although research has explored the influence of personality and gender on individual financial decisions, there are important limitations in the generalizability of the findings due to the particularities of the samples and cultural contexts (e.g., the United States, China, Germany, Ethiopia, South Africa, Turkey) (Friedl et al. 2020; Holden and Tilahun 2022; Lawrenson and Dickason-Koekemoer 2020; Schmitt et al. 2017; Tauni et al. 2020; Rao and Lakkol 2022). This residual ‘gender gap’—observable even after accounting for socioeconomic controls—supports the inclusion of gender as a key explanatory dimension rather than a mere control. Recent studies confirm persistent gender differences in sustainable investment preferences, even after controlling for income and financial literacy (Friedl et al. 2020; Lawrenson and Dickason-Koekemoer 2020; Durmaz Bodur et al. 2023). Therefore, gender is included alongside personality traits to capture behavioral heterogeneity in sustainable finance.

Understanding how these factors interact to quantify investor preferences toward specific types of assets, such as clean energy projects, remains an area with research opportunities. In addition, the application of choice models that allow these preferences to be analyzed in a detailed and systematic way is essential to obtain a deep understanding of investment decision-making in this area.

Therefore, the present scientific work aims to quantify the preferences of Spanish investors towards clean energy projects (SDG 7), analyzing the role of personality traits and gender through choice models. This research seeks to contribute to existing knowledge by providing a specific insight into investor behavior in a little-explored cultural context for this type of investment, and by simultaneously integrating the influence of personality and gender in shaping their sustainable investment preferences.

2. Theoretical Framework and Literature Review

2.1. Importance of SDG 7 and Sustainable Energy

Investing in financial products that promote the development of the United Nations Sustainable Development Goals (SDGs), especially SDG 7 (Affordable and Clean Energy), is of crucial importance for global progress and sustainability (Rampasso et al. 2022). Investment on SDG 7—affordable, reliable, sustainable, and modern energy for all—it is not just a sectoral goal: it is the accelerator that unlocks health, education, equality, and employment, and without which the rest of the 2030 Agenda moves forward with a handbrake (Fernando Oña Bravo and Molina 2023).

Studies on thematic sustainable energy investment funds in China reveal a financial performance comparable to conventional funds, showing that it is possible to promote SDG projects without sacrificing profitability (Martí-Ballester 2021). However, a financing deficit of close to USD 50 billion per year persists, which requires mobilizing public and private capital with impact criteria, elimination of inefficient subsidies and coherent regulations (Fernando Oña Bravo and Molina 2023; Eduardo Reyes Nieto 2018).

Energy is considered the backbone of socio-economic growth, development, and environmental sustainability, being fundamental for poverty eradication and environmental protection (Rampasso et al. 2022). In addition, access to reliable and affordable energy services is key to combating energy poverty, improving health and education: more than 733 million people still lack electricity in their homes.

The transition to clean energy is vital to reducing carbon emissions, mitigating global warming, and decreasing reliance on imported fossil fuels, whose price and supply swings can slow economic growth. The transition to clean energy is vital to reducing carbon emissions, mitigating global warming, and reliance on expensive imported fossil fuels, negatively impacting economic growth. The (Phdungsilp et al. 2017) same authors point out that achieving SDG 7 involves improving energy efficiency, replacing fossil fuels with renewable energy sources (such as wind, solar, hydroelectric, geothermal, and biomass), strengthening international cooperation in clean energy research and technology, and expanding infrastructure and improving technology in developing countries (Phdungsilp et al. 2017). SDG 7 also encourages countries to diversify their energy sources and increase the share of renewable energy in their energy mix (Chundi et al. 2024).

2.2. Role of Financial Institutions in Achieving the SDGs

Transformations towards sustainability require structural reforms of the financial system. Ryszawska (2018) He argues that sustainable investment is part of a paradigm shift where finance moves from maximizing private profits to guiding public missions. Based on the multi-level transition model (Geels 2011), This vision integrates interactions between innovative niches, established financial regimes, and socioeconomic landscapes.

Kemfert and Schmalz (2019) complement this view by proposing an active role for the State as a driver of responsible investment, highlighting the need for common taxonomies to avoid greenwashing and consolidate standards. Bettinazzi et al. (2020) They also provide a critical dimension: less competitive companies use sustainability as a strategic leap tool, confirming its usefulness in reshaping business models.

The role of financial institutions, especially banks, is paramount in implementing measures aimed at achieving the development of SDG 7. Banks, as financial intermediaries, have a fundamental role in shaping the future of sustainable energy globally by financing projects aligned with SDG 7 (Curea et al. 2025). In turn, regulatory and supervisory authorities require banks to integrate sustainability dimensions, including SDG 7, into their risk management and governance frameworks (Curea et al. 2025). The banking sector plays a crucial and multifaceted role in achieving the United Nations Sustainable Development Goals (SDGs), which serve as a comprehensive framework for addressing global economic, social, and environmental challenges.

Banks play a crucial role in financing development projects and have sufficient liquidity to do so (Weber 2018). It is estimated that between USD 5 and 7 trillion are needed annually to achieve the SDGs by 2030 (Weber 2018), and a significant portion of this financing gap needs to be filled by the private sector, including financial institutions (Phdungsilp et al. 2017; Weber 2018). The growing focus on the relationship between finance and sustainability, driven by the end of the Millennium Development Goals (MDGs) and the transition to the SDGs, underscores the importance of the banking sector’s contribution (Amidu and Issahaku 2018). Banks are considered directly and indirectly responsible for various economic, environmental and social problems, underlining their central role in promoting sustainable development (Stauropoulou et al. 2023); A new institutional framework, along with European Union regulations that drive the inclusion of ESG and climate-related information in their disclosures (Coleton et al. 2020), and voluntary initiatives such as the Principles for Responsible Banking (PRBs), which seek to refine banks’ contribution to the SDGs (Avrampou et al. 2019; Coleton et al. 2020), encourage financial institutions to adopt sustainability and social responsibility practices, aligning their business decisions with broader societal goals (Coleton et al. 2020; Stauropoulou et al. 2023).

2.2.1. Sustainable Bank

Sustainable banking, defined as the integration of environmental, social, and governance (ESG) factors into financial decisions (Edmans and Kacperczyk 2022), contributes to the SDGs by integrating these factors into its lending operations and decisions (Coleton et al. 2020; Habib et al. 2024). These ESG factors can be transformed into risks for banks, which require proper management (Coleton et al. 2020). This includes financing sustainable projects such as renewable energy and infrastructure, through products such as green bonds (for SDGs 6, 7, 9, 11, 13), offering microfinance (for SDGs 1, 2, 3, 5, 8, 9,10) and sustainable credit risk assessment (Weber 2018; Coleton et al. 2020). Technologies such as online banking also support green banking by reducing air pollution, for example, through less use of vehicles (Burhanudin et al. 2019). In addition, sustainable banking has proven effective in reducing income inequality and combating money laundering, especially in environments with weak rule of law, accelerating the achievement of SDG 10 (Habib et al. 2024; Burhanudin et al. 2019).

Sustainable banking can channel funds in ways that create economic opportunities to combat income inequalities (Habib et al. 2024). The adoption of the SDGs can also improve customers’ image, trust, and loyalty towards banks; the implementation of the SDGs and sustainable practices in banks positively influences their corporate image, customer trust and reputation, which in turn drives loyalty (Elansari et al. 2024; Stauropoulou et al. 2023). This integration also offers a competitive advantage, attracting new customers (Stauropoulou et al. 2023). However, in some cases, the importance of the SDGs has not been confirmed as a factor that has a significant effect on customer satisfaction, as consumers may assume that the banking system should integrate its negative externalities (Stauropoulou et al. 2023).

While bank support to the SDGs may result in lower credit risk and therefore lower loan loss provisions (LLPs) in some cases, results vary by SDG and region, with some SDGs increasing LLPs due to increased risk exposure. Specifically, support for SDGs 7 (clean and affordable energy) and 10 (reduced inequalities) can lead to lower LLPs. However, in some regions or institutional contexts, such as support for SDG 6 (clean water and sanitation) or 7 in countries with strong institutions, or SDGs 8 (decent work and economic growth) and SDG 10 in Asian countries, it may result in an increase in LLPs. Challenges in this integration include a lack of available data, methodological convergence, and the need for more robust approaches to climate risk management and ESG (Coleton et al. 2020; Elansari et al. 2024). The lack of available data and the absence of standardized methodological convergence among banks for disclosures are key challenges (Elansari et al. 2024).

Ultimately, the SDGs represent a clear strategic tool for banks to identify sustainability needs and align their business strategies effectively (Stauropoulou et al. 2023; Weber 2018). While banks typically prioritize SDGs that align with their existing business strategies (Avrampou et al. 2019), Identifying these needs is crucial. To enhance its contribution, it is recommended that the banking sector improve its codes of conduct and standardize SDG accounting and reporting (Avrampou et al. 2019; Coleton et al. 2020), which allows us to identify strengths and weaknesses, risks and opportunities (Avrampou et al. 2019). In addition, the banking sector must develop innovative financial products that address these objectives (Weber 2018). Regulators and governments have a responsibility to support the industry through risk mitigation mechanisms, such as first-loss guarantees or public–private partnerships (Coleton et al. 2020; Weber 2018), and integrating sustainability aspects into oversight activities. In addition, they should incentivize the adoption of sustainable banking practices and monitor the relationship between SDG activities and provisions for loan losses (Habib et al. 2024). By doing so, sustainable banking can achieve a macro-level impact on sustainable development, contributing to the reduction in inequalities and the overall progress of the 2030 Agenda (Habib et al. 2024).

In sum, the active involvement of banks in promoting SDG 7 can generate significant financial and social benefits, provided that sustainable strategies are combined with effective risk management and a long-term approach.

2.2.2. Influence of Cultural and Institutional Context

In various studies, investors’ risk preferences and their valuation of sustainable financial institutions are shown to differ markedly across regulatory and cultural contexts. Cross-national experiments by (Hsee and Weber 1999) reveal that Chinese participants exhibit greater risk aversion than their American counterparts in comparable monetary gambles. (Soegiarto and Ratnawati 2024) demonstrate that countries with stronger legal systems and investor protections—proxied by higher “regulatory quality”—tend to have more developed capital markets and investors who are more willing to bear risk.

An aggregate meta-analysis by (Friede et al. 2015) finds that ESG factors positively influence financial performance in diverse institutional settings. Finally, Lins et al. (2017) show that firms operating in high-trust environments—where social capital and institutional confidence are elevated—delivered better stock performance during the financial crisis, underscoring how institutional strength can shape investor valuation of CSR and ESG attributes.

2.3. Sustainable Investment: A Behavioral Insight

Sustainable investing has become a dynamic field of study, with a growing interest in understanding the factors that drive investors to prioritize environmental and social considerations. Investment decisions in “green” funds or sustainable projects can be influenced by emotions and the propensity for proactivity (Spitzmuller et al. 2015). In fact, it has been proposed that attitudes towards green consumption may mediate the relationship between personality traits and the decision to invest sustainably (Fachrudin et al. 2022). Studies suggest that efforts to improve social norms around sustainability can significantly impact consumption and investment decisions, especially in younger generations (Tokgöz 2024) and (Gónzalez Gónzalez et al. 2025).

Financial sustainability has become particularly relevant in the contemporary debate on systemic reform of the economic sector. In line with this transition, Ryszawska (2018) It proposes a multi-scale approach to financial transformation, where innovation niches such as green finance interact with traditional regimes and the institutional landscape in which they are inscribed.

In a complementary way (Kemfert and Schmalz 2019), warn about the role of the State as a key investment agent in the promotion of financial models aligned with climate commitments. This vision involves including ESG criteria in budget decisions and fiscal mechanisms, as well as strengthening accountability through regulatory taxonomies that prevent greenwashing.

From a behavioral perspective (Bettinazzi et al. 2020), they argue that less competitive companies can use sustainability as a vector of strategic transformation, especially in crisis contexts, generating positive medium-term impacts on their economic performance.

In a study of Behavioral Economics Beerbaum and Puaschunder (2019), they insist on the need for financial architecture based on transparent and useful taxonomies for decision-making, highlighting the use of technologies such as XBRL in the disclosure of physical, transition and legal risks associated with climate change.

2.3.1. Behavioral Approach: Personality, Proactivity and Green Decision

The behavioral literature argues that sustainable investing is not explained solely by the rational information available, but is mediated by psychological factors such as proactivity, emotions, and personal values (Fachrudin et al. 2022). They highlight that personality traits can influence attitudes towards green consumption and, therefore, the willingness to invest in sustainable projects (Spitzmuller et al. 2015). It adds that proactivity facilitates greater attention to ESG funds, as action-oriented individuals tend to make decisions that incorporate social and environmental considerations. These decisions are also influenced by ecological empathy, locus of control, and internalized social norms.

Consolidating a sustainable financial architecture requires a multidimensional approach that combines behavioral economics, digital finance, and mathematical market modeling. From this perspective (Beerbaum and Puaschunder 2019), develop a Sustainability Taxonomy focused on investor decision-making utility, technological transparency through XBRL and climate risk disclosure. His proposal challenges the classical rationalist paradigm, recognizing that individuals tend to make decisions through mental shortcuts (heuristics) and a limited view of the long term, which (Salgado 2020) denominate “The tragedy of the horizon”.

2.3.2. Theory of Perspectives

Prospect Theory: Investors do not evaluate risks and returns based on expected profit but rather contrast gains and losses against a benchmark (“status quo”) and suffer greater disutility for each loss than utility for each equivalent gain (Kahneman and Tversky 1979). This loss aversion bias explains the reluctance to allocate capital to ESG funds that, even if they show comparable returns, are perceived as more volatile. Recent studies even show that corporate sustainability practices can attenuate this aversion in individual investors (Elahi et al. 2023) and that low-probability overweight helps to understand why high-impact clean energy projects receive little private investment (Fortin et al. 2025).

Mental Accounting: According to (Thaler 2015), People mentally structure their wealth into thematic “accounts” that are not completely fungible. When resources are labeled as “green” or “earmarked for SDG 7”, the willingness to pay for sustainable attributes increases, as they are considered separate funds for ethical purposes (Skwara 2023). Empirical evidence in green housing confirms that an additive or subtractive framing of green characteristics significantly modifies WTP for these attributes (Li et al. 2019), and that the explicit allocation of revenues to environmental purposes reinforces the “acceptability drive” of green taxes or investments (Mus et al. 2023).

2.4. The Role of Personality Traits in Investment Decisions

Personality traits are stable, dispositional factors that shape an individual’s behavior in different contexts (Honold and Oh 2025). These traits directly influence financial decision-making (Akhtar 2022). The Big Five model is a widely used framework for understanding individual differences in human personality and there are multiple studies that attest to this (Lawrenson and Dickason-Koekemoer 2020; Gambetti and Giusberti 2019; Jiang et al. 2024; Müller and Plug 2006; Muñoz-Muñoz et al. 2025). These five traits are:

• Extraversion: Characterizes sociable, energetic, and assertive individuals (Gambetti and Giusberti 2019; Jiang et al. 2024; Müller and Plug 2006; Tauni et al. 2020; Rao and Lakkol 2022). It has been positively associated with the propensity to invest and trade in futures (Gambetti and Giusberti 2019; Lee et al. 2022; Tauni et al. 2020). And they show a positive correlation with financial risk tolerance (Lawrenson and Dickason-Koekemoer 2020), In addition, extroverted investors are more likely to engage in short-term investments. In the context of sustainable investments, their relationship with pro-environmental attitudes has been null or negative (Soutter et al. 2020).
• Agreeableness: Describe people who are cooperative, friendly, trusting, and compassionate (Gambetti and Giusberti 2019; Jiang et al. 2024; Müller and Plug 2006; Tauni et al. 2020). An inverse correlation has been found with the probability of investing (Brown and Taylor 2014; Gambetti and Giusberti 2019), Friendliness also shows a negative influence on cryptocurrency investment intentions, indicating a lower inclination to allocate resources to these assets (Nyhus et al. 2024). An inverse correlation has also been found with financial risk tolerance (Lawrenson and Dickason-Koekemoer 2020), and a negative correlation with stock ownership (Bucciol and Zarri 2017). In pro-environmental behavior, kindness is positively associated (Soutter et al. 2020).
• Conscientiousness: It refers to precision, order, responsibility, discipline and perseverance (Gambetti and Giusberti 2019; Jiang et al. 2024; Müller and Plug 2006; Tauni et al. 2020). It is positively associated with the ability to manage money (Gambetti and Giusberti 2019), A negative correlation with financial risk tolerance has also been found (Lawrenson and Dickason-Koekemoer 2020). Investors with high conscientiousness tend to adopt a rational decision-making style (Gambetti and Giusberti 2019). It also influences the perception of control over investments (Mayfield et al. 2008; Gambetti and Giusberti 2019). Its relationship with pro-environmental behavior is positive (Soutter et al. 2020).
• Neuroticism: It describes an individual’s emotional resilience and the degree to which they experience negative emotions and stressors, characterized by vulnerability, anxiety, uncertainty, tension, and emotional instability (Gambetti and Giusberti 2019; Jiang et al. 2024; Tauni et al. 2020). Individuals with high neuroticism tend to save money and avoid investments, perceiving high risks and low returns (Gambetti and Giusberti 2019). It is associated with risk aversion and lower allocation of stock investments, and the propensity to sell assets prematurely due to emotional reactions (Dang et al. 2024; Gambetti and Giusberti 2019; Jiang et al. 2024). A negative or no relationship with pro-environmental attitudes has been observed (Soutter et al. 2020). Neuroticism has been linked to risk aversion. High levels of neuroticism can lead to risk-averse investment practices and are negatively correlated with risk attitude (Lawrenson and Dickason-Koekemoer 2020).
• Openness to Experience: Typical curious, imaginative, artistic, wide-interested, and unconventional people (Gambetti and Giusberti 2019; Muñoz-Muñoz et al. 2025; Jiang et al. 2024; Müller and Plug 2006; Tauni et al. 2020). People with high openness tend to lean towards unconventional investment opportunities, perceiving high-risk, high-reward assets as intellectually stimulating (Chatterjee et al. 2015; Akhtar 2022). However, one study found no significant relationship between openness and intention to invest in cryptocurrencies (Nyhus et al. 2024). The personality trait openness to experience, characterized by creativity, innovation and a global perspective (Muñoz-Muñoz et al. 2025; Aren and Hamamci 2020), It is frequently associated with actions that benefit the community and with a global approach to sustainability and environmental issues (Muñoz-Muñoz et al. 2025). These people tend to be actively involved in nature conservation and seek to reduce the environmental impact of their actions, which could indicate a preference for sustainable investments that contribute to the Sustainable Development Goals (SDGs) (Muñoz-Muñoz et al. 2025). A high degree of openness is positively correlated with the acceptance of financial risks (Gambetti and Giusberti 2019). However, a low openness can lead to a lower allocation of stock investments (Jiang et al. 2024).

Table 1. Characteristics of investors according to the big five personality traits.

Trait	Key Features	Influence on Investment Decisions	Influencing Sustainable Investments	References	Risk Aversion
Extroversion	Sociable, energetic, assertive	Propensity to invest and trade in futures; increased risk tolerance; They prefer short-term investments	No or negative relationship with pro-environmental attitudes	(Gambetti and Giusberti 2019; Lawrenson and Dickason-Koekemoer 2020; Soutter et al. 2020)	Low
Agreeableness	Cooperative, friendly, trusting, compassionate	Inverse correlation with the probability of investing; lower risk tolerance; less inclination to cryptocurrencies; Less stock ownership	Positive association with pro-environmental behaviors	(Gambetti and Giusberti 2019; Nyhus et al. 2024; Bucciol and Zarri 2017)	High
Neuroticism	Vulnerability, anxiety, uncertainty, tension, and emotional instability	They tend to save and avoid investments; they perceive high risks and low returns; risk aversion; lower allocation in shares; Premature sale of assets	Negative or no relationship with pro-environmental attitudes	(Gambetti and Giusberti 2019; Jiang et al. 2024; Lawrenson and Dickason-Koekemoer 2020; Soutter et al. 2020)	High
Openness	Curious, imaginative, artistic, wide-interested, and unconventional	They look for unconventional high-risk, high-reward opportunities; predicts higher stock returns and higher investment in financial markets	Associated with community actions and a global focus on sustainability; prefer investments aligned with SDGs	(Gambetti and Giusberti 2019; Jiang et al. 2024; Akhtar 2022; Muñoz-Muñoz et al. 2025; Aren and Hamamci 2020)	Low
Conscientiousness	Organized, disciplined, planners, goal-oriented	Greater propensity for planned and long-term investments; lower trading frequency; portfolio diversification; Higher savings rate	Tendency to invest in sustainable projects, with values of responsibility and long-term vision	(Gambetti and Giusberti 2019; Jiang et al. 2024; Müller and Plug 2006; Rao and Lakkol 2022; Tauni et al. 2020)	Low

Source: Own compilation.

, as a summary, establishes the relationship with risk aversion and sustainable investment of each of the five personality traits.

2.5. The Impact of Gender on Investment Decisions

Gender plays a crucial role in financial decision-making, especially in risk tolerance and investment behavior (Schmitt et al. 2017).

Gender differences are a widely studied demographic factor in investment behavior, although their influence can be complex and vary according to the cultural and socioeconomic context (Friedl et al. 2020; Holden and Tilahun 2022; Lawrenson and Dickason-Koekemoer 2020; Marinelli et al. 2017; Tauni et al. 2020).

2.5.1. Risk Tolerance

The most consistent finding is that women, on average, are more risk-averse than men (Mittal and Vyas 2011; Bhushan and Medury 2013). This difference is reflected in the composition of their portfolios: women tend to prefer assets considered safer, such as time deposits, pension plans and insurance policies; while men are more inclined towards riskier and more volatile assets, such as stocks and real estate (Bayyurt et al. 2013; Bhushan and Medury 2013). Experimental studies corroborate this, showing that women exhibit greater Relative Constant Risk Aversion (CRRA) and greater loss aversion, meaning that the pain of a financial loss outweighs the satisfaction of an equivalent gain (Holden and Tilahun 2022). Lawrenson and Dickason-Koekemoer (2020) Men are more than three times more likely than women to invest in cryptocurrencies (Nyhus et al. 2024), probably due to increased tolerance for financial risk and overconfidence. In general, men are considered more tolerant of risk than women (Schmitt et al. 2017).

Women value the possibility of loss and ambiguity more (Lawrenson and Dickason-Koekemoer 2020; Friedl et al. 2020; Holden and Tilahun 2022; Mittal and Vyas 2011; Tauni et al. 2020). This risk aversion manifests itself in lower investment compared to men (Holden and Tilahun 2022; Lawrenson and Dickason-Koekemoer 2020). It has also been observed that women spend more effort gathering information before making investment decisions, while men may rely on more obvious signals (Tauni et al. 2020). However, some more recent studies warn that gender differences may be small or partially explained by measurement errors (Holden and Tilahun 2022).

2.5.2. Investment Behavioral

It has been observed that men trade more frequently than women, possibly due to overconfidence or sensation-seeking behavior (Nyhus et al. 2024). However, one study showed that women were significantly more likely to adopt and use financial technology (Liu et al. 2022) than men. Despite controlling for socioeconomic and demographic variables such as financial literacy, age, or income, some studies still identify a significant “gender gap” in investment decisions (Friedl et al. 2020; Holden and Tilahun 2022; Lawrenson and Dickason-Koekemoer 2020; Marinelli et al. 2017). For example, in the context of Ethiopia, after controlling for risk attitudes and initial resource endowments, a gender difference of 0.28 standard deviations persists in total investments, suggesting that factors such as gender discrimination due to traditional norms may be responsible (Holden and Tilahun 2022). This implies that gender differences in investment behavior can be specific to culture and evolve during socialization, not just by nature (Friedl et al. 2020).

The similarity (or dissimilarity) between investors and their advisors in demographic characteristics, such as gender and education, has also been linked to investment performance, suggesting that the investor-advisor match can influence trading results (Tauni et al. 2020).

2.6. Investment and Financial Behavior: Influence of Gender and Personality

In addition to structural and economic factors, investment decisions are deeply conditioned by individual variables such as gender and personality traits. The literature on behavioral finance has documented clear patterns: men tend to take more risks and opt for volatile assets, while women show greater risk aversion and prefer conservative investments such as deposits, guaranteed funds or gold (Bayyurt et al. 2013; Embrey and Fox 1997).

This difference is partly explained by personality. The Big Five models reveal that traits such as meticulousness, openness to experience or neuroticism modulate financial behavior (Shaheen et al. 2025). For example, people with high meticulousness plan more cautiously and prioritize low-risk investments, while openness to expertise is associated with a willingness to explore sustainable or emerging assets (Mukhdoomi and Shah 2023).

Recent studies show that these traits interact differently depending on gender. Agreeableness, neuroticism, and meticulousness influence women’s investment decisions more than men’s, while extraversion and overconfidence are more linked to male behavior in assets like cryptocurrencies (Nyhus et al. 2024). In addition, factors such as risk perception, age, or cultural context amplify these differences (Suriyanti and Mandung 2024; Embrey and Fox 1997).

The role of financial education and psychological capital has also been highlighted: low confidence in one’s own ability to invest leads women to maintain more liquid and conservative assets, even when they have similar levels of wealth (Bayyurt et al. 2013; Martí-Ballester 2021). However, as financial literacy and available information increase, there is a convergence in attitudes towards risk. Under SDG 7, these differences have practical implications: designing sustainable financial products tailored to diverse profiles can mobilize capital towards green infrastructure, clean technology, and inclusive models (Martí-Ballester 2021). Understanding investment behavior with an intersectional approach—considering gender, personality, and context—not only improves financial efficiency but also facilitates an equitable energy transition with social impact.

In general, small or moderate gender differences have been found in personality traits (Schmitt et al. 2017), As the same authors give as an example, men tend to obtain lower scores than women in neuroticism, agreeableness and, to a lesser extent, in extraversion. The largest gender differences in personality traits were seen in agreeableness and neuroticism (Schmitt et al. 2017).

Below are several cases that explain the Joint Influence of Gender and Personality Traits, to influence investment decisions:

Cryptocurrencies: A study found that gender moderates the relationship between age and crypto investment intentions, revealing that men show a steeper decline in willingness to invest with age, while women maintain more consistent patterns (Nyhus et al. 2024). Additionally, financial overconfidence, agreeableness, and meticulousness mediated the gender difference in crypto investment intentions, while extraversion, emotional stability, and openness did not (Nyhus et al. 2024).

Socially Responsible Investment (SRI): A recent study indicated that gender also moderates the relationship between economic concern (selfish value) and attitude toward SRI, as well as between environmental concern (altruistic value) and attitude (Raut and Kumar 2023). Contrary to some previous research, this study found that women surveyed were less concerned about the environment and more about economic performance than their male counterparts and had a lower propensity toward SRI (Raut and Kumar 2023).

Financial Satisfaction and Investment Decisions: Research examining the mediating effect of financial satisfaction between personality traits and investment decisions, with gender as the moderator, found that financial satisfaction significantly mediated indirect relationships for both sexes (Akhtar 2022). However, the study found that the indirect effects of agreeableness on investment decisions through financial satisfaction were more significant for men than for women, while the indirect effects of conscientiousness and neuroticism were more significant for women (Akhtar 2022).

In summary, behavioral finance research underscores the complex interplay between personality, age, and gender in financial decision-making. Understanding these dynamics is crucial for financial advisors and investment platforms, allowing them to profile and a WTP their services to various investor profiles. While there are general patterns, each individual’s uniqueness, personal experiences, goals, and context also play important roles.

2.7. Choice Experiments and Willingness to Pay Estimation: Tools for Assessing the Value of Social Investment

The assessment of investor preferences for specific assets, such as clean energy projects, and the examination of the influence of personality traits and gender, can be significantly enhanced through the use of choice models. These models provide a systematic framework for analyzing how investment attributes and individual characteristics shape decision-making (Jiang et al. 2024). Their application makes it possible to disentangle the complexity of investment behavior, clarifying not only whether personality and gender matter, but also the extent of their impact—an essential step in understanding investor heterogeneity. The integration of demographic and personality variables with choice models that incorporate insights from behavioral finance is therefore crucial for advancing knowledge in this field (Jiang et al. 2024).

Choice experiments (CEs) have emerged as one of the most reliable methodologies for evaluating investment decisions involving multiple attributes. Grounded in random utility theory (McFadden 1974) CEs enable the estimation of the marginal utility of different characteristics of a good or service, as well as the willingness to pay (WTP) for non-monetary attributes (Hanley et al. 2002). This approach, widely applied in environmental economics and public policy, allows researchers to capture preference heterogeneity across investor groups differentiated by gender, age, financial experience, personality, or ideology.

When applied to sustainable urban investments, CEs make it possible to design hypothetical profiles of financial products—such as green bonds for social housing or thematic funds for clean mobility—and to analyze how attributes such as return, risk, and sustainability impact influence investor choices. By estimating the marginal utility of each attribute and the willingness to pay for sustainability improvements, this methodology provides valuable insights for the development of more attractive, tailored, and effective financial instruments.

The literature documents several successful applications of this methodology in the field of sustainable investment (Muñoz-Muñoz et al. 2025; Apostolakis et al. 2018; Curea et al. 2025; Mirón-Sanguino and Díaz-Caro 2022; Barber and Odean 2000). Key determinants identified include expected return, perceived risk, the type of financial institution, and the achievement of sustainability objectives. These dimensions are operationalized in the econometric model through variables such as: ASC (Alternative Specific Constant), capturing the utility of the opt-out option; Coop, a dummy variable indicating whether the provider is a cooperative; Sust, a dummy variable for sustainable financial institutions; Ret, a continuous variable representing expected return (interest rate); Risk_m and Risk_h, dummy variables for medium and high risk levels (with low risk as the reference category); and Odj, a dummy variable indicating whether the fund explicitly contributes to SDG 7.

Applying a CE to the context of SDG 7 is particularly relevant, as it enables the construction of investment scenarios that combine varying levels of return, risk, and urban impact, and the analysis of investor responses to these attributes. It also facilitates the identification of investor segments more inclined to value social or environmental attributes, which is critical for the design of customized financial products.

Willingness to Pay (WTP) is defined as the maximum monetary amount an individual is prepared to sacrifice to obtain a specific attribute or improvement in a good or service. In this study, WTP is employed to quantify the trade-off investors are willing to make between financial performance and non-monetary attributes such as sustainability impact. Derived from random utility theory, WTP is commonly estimated in discrete choice experiments to capture the implicit value of non-commercial attributes (Hanley et al. 2002). In the model, WTP is calculated as the ratio between the coefficient of a non-monetary attribute and the coefficient of the monetary attribute (interest rate), thereby expressing investor preferences in comparable monetary terms. While the CE presents hypothetical investment options defined by attributes such as risk, return, provider type, and SDG contribution, WTP estimation translates the relative importance of these attributes into monetary values, offering a more interpretable measure of preferences.

In addition, WTP estimation contributes to the economic quantification of the benefits perceived by private investors in sustainable clean energy projects. This, in turn, supports public policy design, the development of co-financing mechanisms, and the structuring of SDG-linked thematic bonds.

3. Materials and Methods

This study is structured around a central research question and divided into two main components. The first component consists of a choice experiment, which will be described in detail below (Mirón-Sanguino and Díaz-Caro 2022; Apostolakis et al. 2018; Muñoz-Muñoz et al. 2025). The second component involves a survey designed to identify the dominant personality traits of respondents, applying the “Big Five” model. In addition to the financial attributes included in the choice experiment, personality traits were measured following the model previously applied by (Fraj and Martinez 2006). The items were evaluated using multi-item scales with a seven-point Likert response format. This framework, widely used in behavioral research, has also been linked to willingness-to-pay (WTP) estimations in previous studies (Fraj and Martinez 2006). The classification of respondents into one of the five personality dimensions follows established approaches in the literature (Fraj and Martinez 2006; Soutter et al. 2020; Shaheen et al. 2025; Sarwar et al. 2020; Isidore and Arun 2022; Tauni et al. 2020; Zhang et al. 2014; Muñoz-Muñoz et al. 2025).

The review of behavioral economics literature highlights a substantial body of work examining gender and personality traits as differentiating mechanisms in financial decision-making, particularly in relation to investments that support sustainable development (Muñoz-Muñoz et al. 2025; Lawrenson and Dickason-Koekemoer 2020; Holden and Tilahun 2022; Friedl et al. 2020; Marinelli et al. 2017; Bhushan and Medury 2013; Gambetti and Giusberti 2019; Jiang et al. 2024; Müller and Plug 2006; Akhtar 2022).

Findings from these studies suggest that investors’ personalities influence the relative importance they assign to different attributes, including the sustainability dimension of investments. Moreover, personality effects are not uniform but vary according to gender, leading to distinct interaction patterns between traits and gender, as documented in prior research (Bayyurt et al. 2013; Shaheen et al. 2025; Mukhdoomi and Shah 2023; Nyhus et al. 2024).

Drawing on this evidence, the following working hypothesis is formulated:

Hypothesis 1.

Personality influences when investing in funds that promote SDG 7 and does so depending on the investor’s trait.

As previously discussed, countries with more advanced development in the field of energy sustainability tend to coincide with those that focus more intensively on developing sustainable cities. In this context, factors such as financial culture, the maturity level of sustainable investment markets, perceived institutional risk, and the degree of environmental awareness can significantly shape how investors evaluate different attributes of an urban project. The gender of the investor can be decisive when determining to invest in sustainable products, as may be the case in this paper with thematic funds that enhance the development of SDG 7;. Thus, the following hypothesis can be formulated:

Hypothesis 2.

Gender is decisive for investing sustainably, being willing to pay more depending on the gender that does so.

The CE methodology was considered the most appropriate tool for estimating investor preferences regarding funds that promote the development and implementation of SDG 7. The CE approach is grounded in the assumption that a good or service can be described by its constituent attributes (Mcfadden 1987; Hanley et al. 2002), and that consumers or, as in this case, investors, make decisions based on these attributes. In a CE, alternative versions of the same product are presented, each characterized by different combinations of attributes, and respondents are asked to select the option that best reflects their preferences (Gutsche and Ziegler 2019; Lagerkvist et al. 2020).

In this context, the application of a Choice Experiment (CE) to investors, in this case, in sustainable financial products, is considered an appropriate approach not only to compare their willingness to accept financial commitments with the development of clean energy (SDG 7), but also to identify differences in the way in which various attributes of this type of investment are valued. such as social and environmental impact, level of risk or profitability. For this reason, this study analyses the willingness of investors to choose financial products aligned with sustainability in the Spanish markets. In addition, this approach allows us to explore whether there are different patterns depending on gender and thus be able to a WTP future measures to market all types of thematic funds, and the same happens when analyzing the personality of investors.

Therefore, based on these hypotheses, a CE study has been conducted among investors in order to analyze sustainable investment preferences oriented toward the achievement of SDG 7.

The initial stage of a choice experiment involves determining the attributes and levels that characterize the investment alternatives presented to respondents.

Table 2. Attributes and levels used in the CE.

Attribute	Levels
Provider (Type of financial institution)	Conventional Bank; Cooperative; Sustainable institution
Return (yield)	1%; 3%; 5%
Risk level	Low; Medium; High
Contribution to SDG 7	Yes; No

Source: Own elaboration.

summarizes the attributes and levels adopted in this study. Their selection was guided by prior research on investor preferences, ensuring that the design reflects dimensions consistently highlighted in the literature (Apostolakis et al. 2018; Gutsche et al. 2023; Lagerkvist et al. 2020; Mirón-Sanguino and Díaz-Caro 2022; Muñoz-Muñoz et al. 2025).

The provider is defined as the financial institution authorized to distribute investment funds. The interest rate corresponds to the return offered by the fund, while the level of risk is linked to the composition of its portfolio. The attribute concerning contribution to the SDG reflects whether the fund explicitly reports its involvement (yes/no) in SDG 7. To ensure a minimum level of contextual understanding and to limit divergent interpretations, respondents were given a brief explanation of this attribute. This measure helped to mitigate the potential effects of the binary framing of “contribution to SDG 7”, which is recognized as a minor limitation.

Combining the selected attributes and levels yields a total of 54 hypothetical products. Such a number would be impractical for survey implementation. Since respondents are presented with “choice sets” consisting of two product alternatives and a “no choice” option, the total number of possible pairwise comparisons would amount to 2862 (54 × 53), making the survey infeasible. To address this, a fractional factorial design was applied to reduce the number of scenarios to a manageable level. The design was generated using the “Dcreate” module in Stata14, which employs the Fedorov algorithm to construct efficient experimental designs (Lancsar et al. 2017; Carlsson et al. 2003). This procedure successfully reduced the number of choice scenarios to eight.

Table 3. Example of choice sets presented in the survey.

		Levels
Attribute	Option 1	Option 2	Option 3
Provider	Conventional bank	Sustainable institution	None
Return	1%	3%
Risk	Low	High
Contribution to SDG 7	Yes	No

Source: Own elaboration.

illustrates an example of the choice sets presented to respondents. Each set contained three options: two alternatives representing different combinations of attribute levels, and a third option labeled “none of the above”, which allowed participants to reject both alternatives and thereby reflect the status quo. The survey was introduced with a detailed explanation of the terminology and the available options. To minimize potential biases associated with the hypothetical nature of the market, both a “Cheap Talk” script and an “Opt-out” reminder were incorporated, following methodological recommendations in the literature (Lancsar et al. 2017). In total, eight choice sets similar to the one shown in Table 3 were generated and administered in the survey.

To address potential concerns regarding external validity, it should be emphasized that choice experiments (CEs) are a widely recognized and methodologically rigorous approach for eliciting preferences in contexts where real-world experimentation is either impractical or ethically problematic. Although CEs rely on hypothetical scenarios and do not involve actual financial commitments, they enable the simulation of realistic decision-making environments and the exploration of preferences for products or attributes that may not yet be available in the market (Holmes et al. 2017).

This characteristic is particularly relevant in the domain of sustainable finance, where innovative products—such as SDG-linked investment funds—are still in the process of development. Furthermore, the inclusion of opt-out alternatives and the use of a “cheap talk” script in the survey design help to mitigate hypothetical bias and enhance the credibility of responses. Despite their inherent limitations, CEs remain a preferred method for analyzing consumer and investor behavior, especially when the objective is to examine trade-offs involving intangible attributes such as social or environmental impact (Shang and Chandra 2023).

For the empirical analysis, a Mixed Logit Model was applied, as it allows for the identification of heterogeneous preferences among investors. This behavioral approach aligns with prior research that highlights personality traits, gender, and ecological sensitivity as key determinants of sustainable investment decisions (Kim and Jeon 2025; Spitzmuller et al. 2015). Empirical evidence from studies such as (Jiang et al. 2024; Curea et al. 2025) further demonstrates that emotional involvement, perceptions of risk, and ethical alignment exert a significant influence on willingness to pay for ESG-related attributes, thereby supporting the adoption of the Mixed Logit framework in this study.

The methodological approach is briefly outlined here, while further details on its practical application can be found in (Mirón-Sanguino and Díaz-Caro 2022). The model is grounded in Random Utility Theory (Train 2002; Mcfadden 1987), which posits that an investor’s utility can be decomposed into two components: a deterministic part, derived from observable factors influencing the decision, and a stochastic part, which captures unobserved and unpredictable influences. Accordingly, the utility $U_{njt}$ of investor n when selecting alternative j in choice situation t can be expressed as

$$U_{njt}={\beta }_n{x}_{njt}+{\epsilon }_{njt},$$

(1)

where ${\beta }_n$ is the vector of individual-specific coefficients, $x_{njt}$ is the vector of attributes for individual n, and ${\epsilon }_{njt}$ is the random error term, assumed to be independently and identically distributed. The term $U_{njt}$ represents the latent utility or preference of individual n for alternative j in choice situation t, which underlies the probability of that alternative being selected.

A well-known limitation of the conditional logit model is its restrictive assumption that all individuals share identical preference structures. By contrast, the mixed logit model relaxes this constraint by allowing coefficients to vary across individuals, thereby capturing heterogeneity in preferences. Under this framework, the probability that investor n chooses alternative j in choice situation t, conditional on a given draw of ${\beta }_n$ is modeled as a function of the observed attributes $x_{njt}$ which represent the characteristics of each alternative.

$$P_{njt}\left({\beta }_n\right)={\displaystyle \frac{exp\left(x_{njt}{\beta }_n\right)}{\sum _{j=1}^{J}exp\left(x_{njt}{\beta }_n\right)}}.$$

(2)

Then, since ${\beta }_n$ is assumed to follow a specified distribution across the population, the unconditional choice probability is obtained by integrating the conditional probability over the distribution of ${\beta }_n$. This distribution is governed by a vector of parameters $\theta$, which typically includes the means and variances of the random coefficients. These parameters characterize the prior distribution assumed for ${\beta }_n$ across the population.

$$P_{njt}=\int {\displaystyle \frac{exp\left(x_{njt}{\beta }_n\right)}{\sum _{j=1}^{J}exp\left(x_{njt}{\beta }_n\right)}}f(\beta |\theta )d\beta .$$

(3)

Probabilities are approximated through simulation for any given value of $\theta$, thus providing the simulated log likelihood from the values of a particular draw r,

$$SLL\left(\theta \right)={\sum }_{n=1}^{N}ln\left\{{\displaystyle \frac{1}{R}}{\sum }_{r=1}^R{\prod }_{t=1R}^T{\prod }_{t=1R}^J{\left[{\displaystyle \frac{exp\left(x_{njt}{\beta }_n^r\right)}{\sum _{j=1}^{J}exp\left(x_{njt}{\beta }_n^r\right)}}\right]}^{y_{njt}}\right\},$$

(4)

where $R$ is the number of draws, and $y_{njt}$ is 1 if n chose j at draw t and 0 otherwise. Equation (4) provides a simulated approximation of the integral in Equation (3) using a finite number of random draws from the distribution of ${\beta }_n$. This simulation-based approach is necessary because the integral in Equation (3) generally lacks a closed-form solution. The parameter vector $\theta$ governs the distribution from which these draws are generated.

Base categories were established for each qualitative attribute in order to provide a reference framework (zero utility) against which the effects of other attribute levels could be compared. Specifically, the reference levels selected were “Conventional” for the Provider attribute, “Low” for the Risk attribute, and “No” for the SDG contribution attribute. The Interest rate was modeled as a continuous variable, thereby enabling the monetization of non-monetary attributes. On this basis, the econometric specification of the model is expressed as

$$U_{njt}={\beta }_{0}ASC+{\beta }_1{Coop}_{njt}+{\beta }_2{Sust}_{njt}+{\beta }_3{Ret}_{njt}+{\beta }_4{Risk\_m}_{njt}+{\beta }_5{Risk\_h}_{njt}+{\beta }_6{Ods}_{njt}+{ϵ}_{njt},$$

(5)

where ${\beta }_0$ refers to the status quo (Alternative-Specific Constant, ASC), i.e., the option of not choosing either of the proposed products, and ${\beta }_k$ represents the marginal utility associated with each attribute of the specific product.

In this specification, the covariates x_njt correspond to the observed attributes of each investment alternative. The expected return is modeled as a continuous variable, which enables the estimation of willingness to pay (WTP), whereas the remaining attributes are incorporated as dummy variables.

It is important to emphasize that, within the framework of a choice experiment, the dependent variable is the investor’s selection among hypothetical investment alternatives. Each alternative is characterized by a set of attributes—such as return, risk, provider type, and sustainability impact—and the utility function captures how these attributes shape the probability of choosing a particular option. The hypothesis that certain investors are prepared to accept lower financial returns in exchange for non-monetary benefits is evaluated indirectly through the estimation of WTP.

The price (interest rate or return) is included as a monetary attribute in the choice model. Therefore, the WTP can be defined as the ratio between a non-monetary coefficient, ${\beta }_k$ and the return coefficient, ${\beta }_{ret}$ representing how much an investor would be willing to pay (or forego) in monetary terms (percentage points of interest rate) to obtain an improvement in attribute k,

$${WTP}_k=-\left({\displaystyle \frac{{\beta }_k}{{\beta }_{ret}}}\right).$$

(6)

The data collection took place in Spain during 2024 and 2025, yielding a total of 873 valid responses. The survey was administered through Google Forms and distributed via social media platforms as well as databases previously employed in related studies. In this context, ‘similar research’ refers to previous survey-based studies on sustainable finance and responsible investment conducted in Spain, which employed comparable sampling strategies and distribution channels. Participation was voluntary, with no financial incentives offered, and anonymity was fully ensured. To ensure data quality, screening questions were included, incomplete responses were eliminated, and consistency checks were applied. Nevertheless, given that the survey was distributed via social media without financial incentives, the sample cannot be considered fully representative of the broader investor population. The study should therefore be regarded as exploratory in nature.

The questionnaire, written in Spanish, was structured into two main sections. The first section focused on the choice experiment, while the second gathered information on respondents’ socioeconomic characteristics.

To ensure clarity and reliability, a pre-test was conducted to detect and correct potential comprehension issues. Prior to launching the main survey, a pilot test was carried out with an independent sample to identify and address ambiguities, misinterpretations, or biases in the wording of questions and the presentation of attributes. The final survey design followed established practices in the literature, onsistent with earlier studies such as those by (Mirón-Sanguino and Díaz-Caro 2022; Muñoz-Muñoz et al. 2025; de Carlos Fraile et al. 2023)—the instrument used in this research was specifically a WTP adapted to be implemented based on SDG 7 investment.

Table 4. Statistical characteristics of the sample.

Variable	Average Value
Age	33.76
Female	57.04%
Income (less than €900)	5%
Income (between 901 and 1500€)	18%
Income (between 1501 and 2500€)	32%
Income (more than 2501€)	44%
1 Member Home	10%
2 Member Home	22%
3 Member Home	30%
Household of 4 or more members	38%
Married	40%
Divorced	7%
Single	51%
Widowers	1%
Basic studies	8%
Vocational training	18%
University Education	73%
No formal education	7%

Source: Own elaboration.

reports the frequency analysis used to contextualize the sample. The representative respondent is approximately 34 years old, predominantly female (57%), and reports a monthly income exceeding EUR 1500. Households typically include members from different age groups, with most respondents being married and possessing a university-level education.

4. Results

4.1. General Model, with Personality Traits and Without Distinction of Gender

The Mixed Logit Model is particularly suitable for capturing heterogeneity in investor decision-making, as it allows preference parameters to vary across individuals.

Table 5. Model Mixed logit. General model.

		Number of Obs		19.920
		Log Simulated Likelihood		−5712.192
General Model		Wald Chi2(11)		1359.160
Mixed Logit Model		Prob > Chi2		0.000
Elección	Coef.	Std. Err.	Z	p > |z|	[95% Conf. Interval]
Mean
Asc	−0.997	0.158	−6.33	0.000	−1.306	−0.689
Tipo	0.110	0.011	9.61	0.000	0.088	0.133
SDG_Conscientiousness	0.018	0.015	1.20	0.228	−0.011	0.046
SDG_Extraversion	−0.039	0.024	−1.61	0.107	−0.087	0.008
SDG_Agreeableness	0.018	0.021	0.86	0.392	−0.023	0.058
SDG_Neuroticism	0.009	0.016	0.56	0.573	−0.022	0.040
Cooperative	−0.616	0.124	−4.95	0.000	−0.860	−0.372
Sustainable	0.260	0.116	2.24	0.025	0.033	0.487
Medium risk	−1.127	0.100	−11.28	0.000	−1.323	−0.931
High risk	−3.265	0.140	−23.25	0.000	−3.541	−2.990
SDG	0.102	0.401	0.25	0.799	−0.685	0.888
SD
Cooperative	1.256	0.099			1.076	1.465
Sustainable	1.401	0.083			1.248	1.573
Medium risk	1.445	0.066			1.321	1.581
High risk	2.076	0.164			1.778	2.423
SDG	1.164	0.068			1.037	1.306

Source: Own compilation.

presents the estimation results for the general specification of the model—that is, without differentiating by gender—using the mixed logit framework. Alongside the estimated coefficients for each attribute, Table 5 also reports the corresponding willingness-to-pay (WTP) values derived from these estimates. The table is organized to display, for each attribute, the estimated coefficient obtained from the mixed logit model together with its associated WTP measure (Coef.), standard errors (Std. Err.), z-values (Z), p-values (p > |z|), and confidence intervals.

Table 6. WTP results based on interest rate. General model.

Results of the Willingness to Invest Based in the Interest Rate
	Asc	Cooperative	Sustainable	Medium Risk	High Risk
wtp	9.054	5.592	−2.356	10.233	29.642
ll	4.794	2.680	−4.274	7.059	22.726
ul	13.315	8.503	−0.438	13.407	36.558
	SDG	SDG_Conscientiousness	SDG_Extraversion	SDG_Agreeableness	SDG_Neuroticism
wtp	−0.925	−0.161	0.358	−0.161	−0.082
ll	−8.059	−0.424	−0.084	−0.533	−0.368
ul	6.208	0.103	0.799	0.210	0.204

Source: Own compilation. Note: WTP = Willingness to Pay; ll = lower limit; ul = upper limit.

presents the monetary valuation of each attribute, accompanied by lower (Ll) and upper (Ul) bounds of the 95% confidence interval.

As shown in Table 5, in aggregate analysis, the mixed logit model confirms several key patterns:

Rejection of the status quo, the negative and significant ASC suggests that, on average, investors show a greater preference for the investment options presented over not investing. The influence of the type of entity: Both cooperative and sustainable institutions have positive and significant coefficients, with estimated WTPs of 5.59 and −2.36 percentage points, respectively, compared to a conventional bank (the latter with a negative sign but less statistical relevance).

Risk is shown to be a decisive factor: Medium and, above all, high risk substantially reduce utility, with very high WTPs to avoid them (10.23 and 29.64 points, respectively). And with respect to investment promoted by SDG 7 and personality: Neither the explicit contribution to SDG 7 nor the personality traits measured reach relevant statistical significance in this global model, which indicates that, without segmenting by gender, objective financial attributes weigh more than individual characteristics.

Overall, the overall gender-neutral model shows that sustainable investment decisions under SDG 7 are fundamentally guided by objective financial attributes. The marked aversion to risk—especially high risk—and the positive assessment of cooperative entities compared to conventional banks confirm that, in aggregate terms, investors prioritize institutional security and confidence over factors such as the declared contribution to SDG 7 or differences in personality traits. The absence of significant effects of the latter variables suggests that, without demographic segmentation, explicit sustainability and psychological profiles take a back seat to more tangible considerations of profitability and risk.

Effect of the Openness to Experience Trait

To capture the assessment of the openness trait as a starting point in our model, the attribute “contribution to SDG 7” was established as a reference category in interactions. Thus, the estimated coefficient for SDG 7 (${\beta }_{SDG}$) reflects the marginal utility assigned by an investor whose personality profile is “Openness” (Table 5). The impact of the other traits (extroversion, agreeableness, conscientiousness, and neuroticism) is expressed as the difference in front of this reference.

Based on the coefficients of the general model:

β_SDG = 0.102

β_ret (Coefficient of return) = 0.018

the willingness to pay (WTP) that an investor would sacrifice for the label “contributes to SDG 7” is calculated as

$$\mathit{W}\mathit{T}{\mathit{P}}_{\mathit{O}\mathit{p}\mathit{e}\mathit{n}\mathit{n}\mathit{e}\mathit{s}\mathit{s}}=-{\displaystyle \frac{{\mathit{\beta }}_{\mathit{S}\mathit{D}\mathit{G}}}{{\mathit{\beta }}_{\mathit{r}\mathit{e}\mathit{t}}}}=-{\displaystyle \frac{\mathbf{0.102}}{\mathbf{0.018}}}\approx \mathbf{5.67} \mathit{p}.\mathit{p}.$$

The other traits adjust this base WTP according to their interaction coefficients (β_SDG_trait):

A positive coefficient indicates that the trait adds WTP versus Openness.

A negative coefficient indicates that WTP is subtracted.

Table 7. Willingness to Pay by Personality Trait (in percentage points of interest).

Trait	β_int	β_int/β_ret	Total WTP (pp)
Openness(reference)	–	0.00	5.67
Conscientiousness	+0.018	1.00	6.67
Extraversion	−0.039	−2.17	3.50
Agreeableness	+0.018	1.00	6.67
Neuroticism	+0.009	0.50	6.17

Source: Own compilation.

then summarizes the WTP for each profile, where the “Difference” column is the quotient $\beta int/\beta ret$ and the “Total WTP” column combines that difference with the Openness WTP.

The evaluation of Openness as a reference is supported by studies that associate it with a global approach to sustainability and interest in investments aligned with the SDGs (Muñoz-Muñoz et al. 2025), as well as with a greater willingness to assume certain financial risk (Sarwar et al. 2020). By quantifying the Opening WTP at 5.67 p.p., we can better understand the “plus”—or “minus” in the case of extraversion—that the other features contribute to the valuation of a fund that contributes to SDG 7.

4.2. Female Gender Outcomes and Personality Traits

In this model, investors’ choices are analyzed taking into account the responses of the female gender, which allows us to compare the differences in choice, in this case, with the male gender with the following Section 4.3.

Table 8. Model mixed logit. Gender model_women.

		Number of Obs		11.304
		Log Simulated Likelihood		−3075.606
Gender Model Women		Wald Chi2(11)		663.430
Mixed Logit Model		Prob > Chi2		0.000
Elección	Coef.	Std. Err.	Z	p > |z|	[95% Conf. Interval]
Mean
Asc	−1.400	0.238	−5.88	0.000	−1.867	−0.933
Tipo	0.031	0.017	1.82	0.069	−0.002	0.065
SDG_Conscientiousness	0.042	0.023	1.81	0.071	−0.003	0.087
SDG_Extraversion	−0.094	0.036	−2.56	0.010	−0.165	−0.022
SDG_Agreeableness	0.030	0.034	0.90	0.368	−0.036	0.097
SDG_Neuroticism	0.018	0.026	0.69	0.492	−0.033	0.068
Cooperative	−0.945	0.196	−4.81	0.000	−1.330	−0.560
Sustainable	0.412	0.176	2.33	0.020	0.066	0.757
Medium risk	−1.317	0.151	−8.71	0.000	−1.613	−1.020
High risk	−3.845	0.209	−18.41	0.000	−4.254	−3.435
SDG	0.191	0.575	0.33	0.740	−0.937	1.318
SD
Cooperative	1.612	0.145			1.352	1.922
Sustainable	1.609	0.129			1.376	1.882
Medium risk	1.631	0.098			1.451	1.834
High risk	2.182	0.249			1.744	2.729
SDG	1.381	0.103			1.193	1.599

Source: Own compilation.

and

Table 9. WTP results based on interest rate. Gender model women.

Results of the Willingness to Invest Based in the Interest Rate
	Asc	Cooperative	Sustainable	Medium Risk	High Risk
wtp	44.711	30.192	−13.146	42.053	122.797
ll	−14.646	−9.522	−27.671	−7.781	−13.314
ul	104.068	69.906	1.380	91.886	258.908
	SDG	SDG_Conscientiousness	SDG_Extraversion	SDG_Agreeableness	SDG_Neuroticism
wtp	−6.089	−1.328	2.987	−0.972	−0.564
ll	−42.579	−3.343	−1.001	−3.336	−2.288
ul	30.400	0.688	6.975	1.392	1.161

Source: Own compilation. Note: wpt = Willingness to Pay; ll = lower limit; ul = upper limit.

present the results of the usefulness and willingness to pay for the female gender, also taking into account their personality traits.

In the female segment, the results show important nuances:

Greater risk aversion: Women assign even higher penalties to risk, with WTP of 42.05 p.p. to avoid medium risk and 122.79 p.p. to avoid high risk, much higher than the overall average. As for the valuation of fund marketing entities, the preference for cooperatives and sustainable companies intensifies, with premiums of 30.19 p.p. and −13.14 p.p., respectively.

With regard to personality traits, extroversion shows a negative and significant relationship with the choice of SDG 7 funds, indicating that moreextroverted women are less likely to choose them. Conscientiousness approaches the threshold of significance, suggesting a possible positive influence.

The attribute “contributes to SDG 7” is not statistically significant, suggesting that, for this group, risk and entity type outweigh the explicit sustainability label. Note that in our scheme Openness functions as a reference category in interactions with SDG 7, so that the coefficient β_SDG = 0.191 reflects the valuation given to it by a woman with an open profile. Given that β_SDG does not reach significance (p = 0.740), we conclude that openness investors do not show a plus willingness to pay for the SDG 7 label.

In the case of the female segment, the model reveals a decision pattern strongly oriented towards risk minimization and confidence in the type of issuer. The willingness to pay to avoid medium and high-risk levels is well above the general average, confirming a particularly pronounced risk aversion. Cooperatives are the most valued type of supplier, followed—although with less consistency and a negative sign in the estimation—by sustainable entities. In terms of personality traits, only extroversion shows a statistically significant effect, and of a negative nature, suggesting that women with this profile tend to move away from SDG 7 investments, while responsibility points to a possible positive relationship. The lack of significance of the attribute “contributes to SDG 7” indicates that, for this group, the factors of security and institutional trust weigh more heavily in the decision than the mere sustainability statement.

4.3. Male Gender Outcomes and Personality Traits

Table 10. Model mixed logit. Gender model_men.

		Number of Obs		8.616
		Log Simulated Likelihood		−2583.710
Gender Model Men		Wald Chi2(11)		739.790
Mixed Logit Model		Prob > Chi2		0.000
Elección	Coef.	Std. Err.	Z	p > |z|	[95% Conf. Interval]
Mean
Asc	−0.666	0.213	−3.12	0.002	−1.084	−0.247
Tipo	0.185	0.016	11.72	0.000	0.154	0.216
SDG_Conscientiousness	0.006	0.019	0.33	0.738	−0.031	0.044
SDG_Extraversion	0.015	0.033	0.44	0.661	−0.050	0.080
SDG_Agreeableness	0.006	0.027	0.23	0.822	−0.048	0.060
SDG_Neuroticism	0.006	0.021	0.29	0.774	−0.034	0.046
Cooperative	−0.312	0.162	−1.92	0.055	−0.630	0.006
Sustainable	0.123	0.149	0.83	0.407	−0.168	0.414
Medium risk	−0.923	0.134	−6.9	0.000	−1.185	−0.661
High risk	−2.709	0.196	−13.81	0.000	−3.094	−2.325
SDG	−0.290	0.616	−0.47	0.637	−1.497	0.916
SD
Cooperative	0.883	0.137			0.651	1.197
Sustainable	1.152	0.107			0.960	1.382
Medium risk	1.254	0.095			1.080	1.455
High risk	1.947	0.242			1.527	2.484
SDG	0.917	0.093			0.752	1.119

Source: Own compilation.

and

Table 11. WTP results based on interest rate. Gender model men.

Results of the Willingness to Invest Based in the Interest Rate
	Asc	Cooperative	Sustainable	Medium Risk	High Risk
wtp	3.593	1.684	−0.664	4.980	14.624
ll	0.905	−0.182	−2.194	3.136	11.200
ul	6.280	3.549	0.865	6.824	18.048
	SDG	SDG_Conscientiousness	SDG_Extraversion	SDG_Agreeableness	SDG_Neuroticism
wtp	1.568	−0.034	−0.078	−0.033	−0.032
ll	−4.973	−0.235	−0.430	−0.323	−0.249
ul	8.108	0.167	0.273	0.257	0.185

Source: Own compilation. Note: wtp = Willingness to Pay; ll = lower limit; ul = upper limit.

present the results of the usefulness and willingness to pay for the male gender, also taking into account their personality traits.

In men, the pattern differs from the female group:

Lower risk penalty, although high risk is still the most rejected factor (WTP = 14.62 p.p.), the magnitude is much lower than that observed in women, confirming greater relative tolerance. With respect to the investment fund marketing entities, positive assessments are observed towards cooperatives and sustainable ones, but with modest premiums (1.68 p.p. and −0.66 p.p., respectively) and lowerstatistical significance than in the female segment.

Personality and SDG 7 show that none of the personality traits have significant coefficients; the SDG 7 attribute is also not relevant, which reinforces that, for this group, decisions are guided more by risk and return than by sustainability factors or psychological dispositions. Similarly, the SDG 7 assessment for the profile of Openness among men is contained in β_SDG = −0.290 (p = 0.637). As it is not significant, we confirm that investors with openness do not add willingness to pay for the SDG 7 label in this segment.

In the male group, the model reflects a higher relative risk tolerance compared to women, although high risk continues to be the most penalized attribute in their investment decisions. Positive assessments of cooperative and sustainable entities are modest and less statistically sound, suggesting that, for this segment, the type of supplier has a secondary weight over considerations of profitability and risk. The absence of significant effects of personality traits and the declared contribution to SDG 7 confirms that, in men, choices are mainly guided by objective financial variables, with explicit sustainability factors or individual psychological dispositions taking a back seat.

As a robust check, we also explored the interaction between income levels and personality traits. Although not the central focus of the study, this exploratory analysis suggests that income moderates the influence of certain traits on sustainable investment preferences. The results, while preliminary, enrich the interpretation of heterogeneity and are further discussed in the following section.

Overall, the findings show that, although objective financial attributes—especially risk and type of entity—are determinants in the decision to invest in sustainability, gender segmentation reveals substantial nuances: women show greater risk aversion and a more intense valuation of cooperative and sustainable entities, while men give lower relative weights to these factors and prioritize profitability and risk in a meaningful sense. more balanced. The influence of personality traits, proposed in Hypothesis 1, is limited in the general model, but emerges more clearly in the female group (case of extroversion). On the other hand, Hypothesis 2 is supported by the clear divergence in the provisions to be paid between genders. These results open the door to a more in-depth analysis of how psychological and demographic variables interact in the configuration of investor preferences, and of the practical implications for the design of financial products aligned with SDG 7, which is addressed in the discussion section.

5. Discussion

The results confirm that objective financial attributes, especially the level of risk and the type of provider entity, are the most influential determinants in the choice of investments aimed at SDG 7. The strong penalty observed for medium and high risk in all models is consistent with previous studies that document a clear risk aversion in the investment decision (Lawrenson and Dickason-Koekemoer 2020; Sarwar et al. 2020). An important implication of these findings is the role of sustainable finance education. Financial literacy oriented toward sustainability is a necessary precondition for mobilizing private capital, complementing systemic reforms. Early exposure to sustainability-oriented financial education can enhance individuals’ capacity to align investment decisions with long-term environmental goals. Although our results confirm the primacy of objective financial attributes, it is important to recognize that the degree of risk aversion and the valuation of cooperatives or sustainable institutions can be modulated by the institutional environment. In markets with high regulatory quality, the “contribution to SDG 7” attribute is more likely to gain prominence over risk, whereas in contexts with lower trust in the financial system the penalization of risk is exacerbated and crowds out sustainability concerns (Lins et al. 2017; Soegiarto and Ratnawati 2024). This cultural dimension not only nuances our gender and personality findings but also limits the extent to which they can be generalized to other countries or regions. However, the magnitude of willingness to pay (WTP) to avoid risk in our female segment is substantially higher than that reported in other contexts, reinforcing the evidence that women show a lower risk tolerance than men (Friedl et al. 2020; Holden and Tilahun 2022).

In terms of the type of entity, the positive premiums associated with cooperatives and, to a lesser extent, sustainable entities, are aligned with findings of (Apostolakis et al. 2018; Gutsche and Ziegler 2019), who emphasize that the structure and social orientation of the issuing institution generate an intangible value appreciated by investors with sensitivity to social impact. In our case, this effect is particularly pronounced in women, which coincides with studies that indicate that this group values the ethical alignment of the financial intermediary more (Raut and Kumar 2023).

Regarding personality traits, the overall model does not show statistically significant effects, differing from research that has found strong relationships between the Big Five and sustainable investment decisions (Muñoz-Muñoz et al. 2025; Akhtar 2022). However, a relevant pattern emerges in the female group: extroversion is negatively associated with the preference for SDG 7 funds. This result is consistent with the literature that links extroversion with a greater interest in short-term investments and less pro-environmental orientation (Soutter et al. 2020; Jiang et al. 2024) which could explain their lower affinity for sustainable vehicles with a longer return.

In the case of men, the absence of significance in personality traits and the lower magnitude of WTPs by non-financial attributes support the idea that, in this segment, decisions are guided to a greater extent by classic metrics such as profitability and risk, as documented by the (Aren and Hamamci 2020). This contrasts with the greater female sensitivity to institutional and impact factors, supporting Hypothesis 2 and aligning with previous evidence on the gender gap in socially responsible investment (Nyhus et al. 2024; Marinelli et al. 2017).

These gendered patterns can be interpreted in light of broader cultural and institutional contexts. Comparative studies indicate that women, across diverse markets, tend to emphasize environmental and social attributes when institutional trust is lower or when cooperative structures are more visible, while men prioritize economic returns under conditions of higher financial literacy and income security (Lawrenson and Dickason-Koekemoer 2020; Durmaz Bodur et al. 2023; Marinelli et al. 2017). In our Spanish sample, the stronger female preference for environmentally oriented investments may reflect both cultural norms of social responsibility and the institutional role of cooperatives, whereas men’s prioritization of economic factors is consistent with evidence that higher income levels and financial confidence reduce the relative weight of sustainability attributes. This interpretation situates our findings within a comparative perspective and underscores the importance of considering cultural and income-related moderators when analyzing gender differences in sustainable finance.

In summary, our results partially confirm Hypothesis 1, since personality influences only the female case (extroversion), while Hypothesis 2 is widely supported by the divergence in WTP between genders. This pattern suggests that strategies for the design and marketing of sustainable financial products should consider not only sociodemographic variables, but also differentiated behavioral profiles, as recommended (Gutsche et al. 2023; Ryszawska 2018).

Table 12. Main findings of the discussion.

Appearance/Variable	General Model	Female	Male	Comparison with Literature
Risk aversion	High, especially at high risk (WTP ≈ 29.6 p.p.)	Very high, with much higher WTPs (≈122.8 p.p. High risk)	Moderate-high, less than in women (≈14.6 p.p.)	It coincides with studies that show lower female tolerance to risk (Friedl et al. 2020; Holden and Tilahun 2022)
Entity Type	Cooperatives and sustainable well-valued; cooperative with the highest WTP	Preference for cooperatives (+30.19 p.p.); sustainable negative sign, but contextual relevance	More moderate positive preference (+1.68 p.p. cooperatives)	In line with (Apostolakis et al. 2018; Gutsche and Ziegler 2019) on the intangible value of socially oriented entities
SDG 7 contribution	Not significant	Not significant	Not significant	Contrast with studies that find positive effect of explicit ESG attributes (Muñoz-Muñoz et al. 2025)
Personality traits	No significant effects	Negative and significant extroversion; Liability near the threshold	No significant effects	Reinforces (Soutter et al. 2020; Jiang et al. 2024) on lower pro-environmental affinity in extroverts
Hypothesis 1 (personality)	Partially confirmed (only relevant in women)	Confirmed for extroversion	No confirmed	Aligns with literature that points to gender–personality interactions in SRI (Nyhus et al. 2024)
Hypothesis 2 (gender)	—	—	—	Confirmed: clear differences in WTP and attribute weight

Source: Own compilation.

and

Table 13. Comparison of investment preferences towards SDG 7: general model vs. gender segmentation.

Variable/Factor	General Model	Women	Men
Risk aversion	High (especially at high risk)	Very high (extreme penalties)	Moderate-high
Entity Type	They prefer cooperatives and, to a lesser extent, sustainable	Marked preference for cooperatives and sustainable	Moderate positive preference
SDG 7 explicit	Not significant	Not significant	Not significant
Personality	No effects	Negative extroversion; Responsibility close to signifying	No effects
Confirmed hypotheses	H2 partially (gender); Weak H1	H2 confirmed; H1 in extroversion	H2 confirmed; H1 in

Source: Own compilation.

reflect the main findings of the discussion and comparison of investment preferences in the general model and the gender section.

From a practical perspective, these findings offer valuable input for both the design of financial products and the formulation of public policies. In the corporate sphere, the clear divergence in the provisions to be paid suggests that financial institutions can increase the attraction to funds linked to SDG 7 through segmentation strategies that combine gender-differentiated messages and attributes, emphasizing safety and social orientation for women, and optimizing communication on risk-adjusted returns for men. In the regulatory field, the results support the incorporation of tax incentives and risk mitigation mechanisms that make sustainable investments more accessible, especially for profiles with greater risk aversion. Likewise, the limited weight of personality traits in the general sample suggests that financial education with a focus on sustainability—as recommended by (Gutsche et al. 2023; Ryszawska 2018)—it can homogenize attitudes and broaden the investment base, thus facilitating the mobilization of private capital towards the energy transition required by SDG 7.

Although this study has focused on SDG 7, the methodological approach can be extended to other Sustainable Development Goals. Future research could apply similar choice models to analyze investment preferences in areas such as SDG 13 (climate action) or SDG 10 (reduced inequalities). Exploring these dimensions would provide a broader understanding of how demographic and behavioral factors influence sustainable finance decisions across different domains of the 2030 Agenda.

6. Conclusions, Limitations, and Future Lines of Research

This study confirms that, in the context analyzed, sustainable investment decisions targeting SDG 7 are strongly conditioned by objective financial attributes, especially perceived risk and the type of provider entity. The medium- and high-risk penalty is consistent with previous evidence documenting the relevance of risk as a primary variable in financial choices (Lawrenson and Dickason-Koekemoer 2020; Sarwar et al. 2020) and with the behavioral finance literature linking risk aversion to demand for safer products.

However, segmentation by gender reveals substantial nuances. As they proposed (Friedl et al. 2020; Holden and Tilahun 2022), Women are markedly more risk-averse and give greater weight to socially oriented institutional characteristics (e.g., cooperatives or sustainable entities). In our case, the willingness to pay in the female group to avoid high risks far exceeds those reported in other studies, which could respond to a particular cultural and market context, in line with the conclusions of (Marinelli et al. 2017) on the influence of the environment on the investment “gender gap”.

The type of supplier also emerges as a value attribute, especially in women, which coincides with the conclusions of (Apostolakis et al. 2018; Gutsche and Ziegler 2019), who identify ethical reputation and cooperative structure as elements that increase perceived utility. On the other hand, the explicit mention of the contribution to SDG 7 does not reach statistical significance, which contrasts with what was found by (Muñoz-Muñoz et al. 2025), where ESG communication had a positive effect. This could indicate a certain “normalization” of sustainability as a basic expectation, rather than as a differentiating attribute.

Regarding personality traits, Hypothesis 1 is partially confirmed: the sample does not show significant effects, but in the female group extroversion is negatively associated with the choice of SDG 7 funds, corroborating what was pointed out by (Soutter et al. 2020; Jiang et al. 2024) about the lower link between this trait and pro-environmental behaviors. In the male group, the absence of significant effects reinforces the idea, observed by (Aren and Hamamci 2020), that decisions are mainly guided by tangible economic variables.

These findings fully support Hypothesis 2, which anticipated gender differences in willingness to pay, and show that the design of sustainable financial products should consider differentiated profiles. This approach, as they point out (Gutsche et al. 2023; Ryszawska 2018), it can increase the attraction of private capital towards the energy transition and facilitate an allocation of resources more in line with the objectives of the 2030 Agenda.

It should be emphasized that, by using Openness as a reference category, we find that its interaction with SDG 7 attribute is not significant in either women or men. This confirms that the SDG 7 label has no effect on willingness to pay, regardless of the openness trait.

6.1. Limitations

Hypothetical nature of the EC: Although techniques were applied to mitigate hypothetical bias (cheap talk and non-choice option), stated preferences could differ from decisions in real settings with money at stake (Holmes et al. 2017).

Abbreviated measure of personality: The short version of the Big Five may not capture all facets of traits, limiting the detection of fine interactions.

Bounded attributes: The design included a limited number of attributes; Factors such as liquidity, time horizon or tax incentives could enrich the model in future research.

Temporal sensitivity: Attitudes towards risk and sustainability are dynamic and can vary depending on the economic cycle or high-impact climate and political events.

This work, based solely on Spanish investors, does not incorporate direct measures of the institutional context—such as regulatory quality or sustainable market development—limiting its external validity, since variations in regulatory maturity, trust in authorities, and financial traditions can shape both risk aversion and the propensity to invest in SDG 7–linked products.

The absence of financial incentives for participation may have introduced self-selection bias, as individuals already interested in sustainable investing were more likely to respond. This limitation should be considered when interpreting the results.

6.2. Future Lines of Research

The results of this work suggest several ways to advance knowledge on investment preferences towards SDG 7 and, more broadly, on sustainable investment

Broaden geographic reach: Including markets with different levels of sustainable investment maturity (e.g., Nordics, emerging Asia, Latin America) would allow for contrasting the influence of cultural and regulatory context, as they point out (Friedl et al. 2020; Marinelli et al. 2017).

Incorporate additional attributes: Variables such as liquidity, time horizon, sector diversification or tax incentives could enrich the model and capture relevant nuances not contemplated in this design (Apostolakis et al. 2018).

Measure personality in depth: Applying full scales of the Big Five model or including other theoretical frameworks (e.g., HEXACO) would allow for the exploration of finer interactions between financial traits and variables, following the recommendations of (Soutter et al. 2020).

Evaluate decisions with real incentives: Replicating the experiment in laboratory or market environments with real monetary risk would help validate the correspondence between declared preferences and actual behavior, in line with what was proposed by (Holmes et al. 2017).

Longitudinal analysis: Observing the evolution of preferences over time, considering economic conjunctures and socio-environmental events, would allow identifying structural or conjunctural changes in the WTP by sustainable attributes.

Intersectional perspective: Deepen how variables such as age, educational level, income, or political ideology are combined with gender and personality to profile more precise segments, as suggested (Nyhus et al. 2024; Raut and Kumar 2023).