Socially Responsible Investing: Is Social Media an Influencer?

Abstract

As digital connectivity transforms financial decision-making, this study offers one of the first empirical investigations into the relationship between social media use and socially responsible investing (SRI). Using data from the 2021 National Financial Capability Study, multinomial regression analysis was used to explore whether people who rely on social media for investment decisions were more likely to invest in ways that reflect their values. The results show that investors who use social media for investment information are more likely to value being socially responsible as an important reason for investing. Younger, less experienced, and more risk-tolerant investors were especially likely to follow SRI strategies, and certain platforms like Twitter were more associated with SRI interest than others. These findings suggest that social media is not just a platform for sharing information; it may also shape how people think about investing and the role their money can play in making a societal difference. As online platforms continue to influence financial behavior, understanding their impact on values-based investing becomes increasingly important. This research contributes novel insights to the emerging intersection of social media, behavioral finance, and values-driven investing.

1. Introduction

In the age of digital connectivity, social media has become a powerful force in shaping consumer values, financial behaviors, and investment decisions. Platforms such as Twitter, Reddit, TikTok, and YouTube serve not only as hubs for financial discourse but also as dynamic ecosystems where narratives, trends, and ideologies are rapidly disseminated and reinforced (Kim & Fan, 2025). For many individuals, particularly younger, digitally native investors, social media now rivals traditional financial advisors and news outlets as a primary source of investment information (Meng et al., 2020).

This shift in information-seeking behavior has important implications for socially responsible investing (SRI), a strategy that integrates ethical, environmental, and social values into financial decision-making (Camilleri, 2021; Green & Roth, 2024; Hwang & Nam, 2021; Viviers & Eccles, 2012). SRI includes a broad range of approaches, from negative screening of harmful industries to proactive impact investing and shareholder engagement. While this concept reflects the evolving values and goals of investors, it can also complicate empirical research. To address this, our study adopts a focused operationalization of SRI based on whether investors self-report “making a difference in the world/supporting values” as a primary reason for investing, consistent with prior literature that centers on values-based motivations (Sandberg et al., 2009; Camilleri, 2021). These strategies have gained significant traction, driven in part by a desire among investors to make a positive difference while achieving financial returns (Sandberg et al., 2009). Notably, the growth of SRI has been especially prominent among younger generations, who report stronger preferences for aligning investments with personal values—and who are also the most active users of social media (Benson & O’Shea, 2024; Safdie, 2024; Schueth, 2003).

Social media’s ability to shape perceptions, reinforce values, and amplify narratives may play a key role in encouraging engagement with SRI (Benson & O’Shea, 2024). Through curated content, influencer endorsements, viral campaigns, and peer discussions, platforms can elevate social causes and investment opportunities that appeal to values-based investors (Safdie, 2024). This study aims to examine the extent to which social media use is associated with socially responsible investing. By empirically exploring this relationship, the research seeks to offer new insights into how digital platforms influence values-based financial decision-making and, ultimately, the evolving landscape of investor behavior.

This study makes a novel contribution to the literature by empirically examining how reliance on social media as a financial information source is associated with individuals’ motivations for socially responsible investing. While prior studies have explored the psychological or demographic characteristics of SRI investors, few have investigated how digital information environments, such as social media platforms, influence values-based investing behavior. By linking a nationally representative dataset from the NFCS with social learning theory, this research offers new insights into the digital channels that may shape ethical investment preferences, particularly among younger and less experienced investors.

1.1. Literature

Socially responsible investing (SRI) has evolved from a niche ethical approach into a mainstream investment strategy with significant implications for investment decisions (Camilleri, 2021; Viviers & Eccles, 2012). SRI involves integrating environmental, social, and governance (ESG) criteria into investment decisions, alongside traditional financial considerations (Camilleri, 2021; Green & Roth, 2024; Hwang & Nam, 2021). Over the past decade, SRI assets have grown exponentially worldwide, reflecting a surge in investor demand for values-based investing (US SIF, 2024).

Emerging research indicates that integrating SRI into investment planning may contribute to investors’ holistic well-being, especially as individuals increasingly seek harmony between their financial behaviors and ethical convictions (Talan et al., 2024). While client interest and engagement with SRI strategies vary, there has been a marked rise over the past decade in the desire to align financial choices with personal beliefs (Bauer & Smeets, 2015; Viviers & Eccles, 2012). Scholars have framed SRI as ideologically driven, contrasting it with investment decisions primarily motivated by profit (Baker & Nofsinger, 2012). This orientation reflects a broader preference to evaluate firms not only by their financial performance but also by their engagement with social, ethical, and environmental issues—underscoring a desire to hold corporate actors accountable to values that matter to individual investors (Sullivan & Mackenzie, 2017).

1.1.1. Social Media

Platforms such as Twitter, Reddit, and TikTok have democratized access to financial information, enabling individuals to engage with SRI discourse, share opinions, and influence peer investment behavior (Kim & Fan, 2025; Lei & Zhang, 2020). These platforms serve not only as information sources but also as catalysts for social validation and community-based decision-making around values-driven investing (Jaiswal et al., 2025; Lei & Zhang, 2020).

1.1.2. Social Media as a Financial Information Source

Social media has become an increasingly influential platform for disseminating financial information, shaping investor behavior through its accessibility, speed, and peer-to-peer format. Unlike traditional financial news outlets, platforms such as Reddit, YouTube, Twitter (X), and TikTok facilitate informal financial dialogue and amplify user-generated content. While this democratization of information can empower retail investors (Chen & Hwang, 2022), it also introduces risks of misinformation and behavioral biases, especially among inexperienced individuals (Arora et al., 2024).

1.1.3. Social Media and Socially Responsible Investing

The intersection of social media and SRI introduces a compelling new domain of research. Social media platforms often serve as channels for ESG advocacy, providing visibility to ethical funds, corporate responsibility movements, and social justice investing campaigns. These digital spaces can shape investor preferences toward SRI by fostering emotional engagement, peer endorsement, and normative influence (Rooh et al., 2023). At the same time, the rapid spread of information on ESG-related controversies or greenwashing claims may also influence disinvestment decisions. Preliminary evidence suggests that investors exposed to ESG content via social media may develop stronger preferences for SRI but may also be vulnerable to confirmation bias and herd-like behavior when ESG information is presented through echo chambers (Khemir et al., 2019; Nicolas et al., 2024). This dual role of social media, as both an awareness tool and a source of behavioral distortion, raises critical questions for financial planners navigating clients’ evolving value systems.

1.2. Theory

This study utilizes social learning theory (Bandura & Walters, 1977) as its theoretical foundation to explore how social media usage influences socially responsible investing behaviors. According to Bandura and Walters (1977), individuals learn new behaviors through observation and interaction in their environment, as shown in Figure 1. Social learning theory provides an important integration between behavioral and cognitive approaches to learning (Schunk & DiBenedetto, 2020). This theory bridges both approaches by recognizing that learning occurs not just through direct reinforcement (behavioral) or internal mental processes (cognitive) but through the combination of both (Deaton, 2015). Individuals learn by observing the behaviors of others and the consequences of said behaviors and then cognitively process whether they want to replicate the observed behaviors (Deaton, 2015). This interaction of cognitive and behavioral approaches guides Bandura’s framework, which consists of four interrelated processes: attention, retention, reproduction, and motivation (Firmansyah & Saepuloh, 2022).

Attention is the first component of Bandura’s (1986) framework. It is posited to be a prerequisite to learning and is influenced by multiple factors. Before an individual can learn from a modeled behavior, they must first observe and focus on it. Exposure alone does not determine whether a behavior will be modeled; the behavior must capture the individual’s interest and be determined to be worth modeling (Bandura & Jeffrey, 1973). The next element in the framework is retention. This phase of the process involves storing the modeled behavior in their minds to use at a later time (Bandura & Jeffrey, 1973). In order for behaviors to be successfully retained, Bandura and Jeffrey (1973) explain that they must be coded with a symbolic representation of the behavior. They argue that coding the behavior with a symbolic representation creates a template that can be easily accessed at a later time, when needed. The inherent design of social media creates this opportunity for attention and retention through its visual offerings and community engagement (Deaton, 2015).

Reproduction is the third phase in the social learning theory learning process. This phase of the process is when an individual takes the stored template of the behavior and puts it into action, resembling the modeled behavior (Fryling et al., 2011). Last is the concept of motivation. Learners need to be motivated to replicate the behavior, which is carried out through three different methods: direct reinforcement, vicarious reinforcement, and self-reinforcement (Horsburgh & Ippolito, 2018). Individuals learn not only by observing others’ behaviors but also from the reactions of others to the role model’s behavior (Horsburgh & Ippolito, 2018). Overall, an important element of social learning theory is that behavior can and does change through observing others, even if they are not deliberately focused on the behavior (Fryling et al., 2011).

Social learning theory and the observational learning process are particularly relevant in the digital age, where social media platforms serve as a setting for learning about financial information and making investment decisions. Social media provides a powerful opportunity for a user to engage in each of the four processes of social learning theory through the design of the platform. The visual representations, video components, opportunities to gain followers and likes, and the ability to engage with a community make social media a powerful motivator. A key element of Bandura’s theory is the idea that individuals believe they have influence over events in their lives (Schunk & DiBenedetto, 2020). This sense of agency has powerful implications when evaluating and modeling SRI strategies. Given its emphasis on observational learning, modeling, and the social reinforcement of behaviors, social learning theory provides a theoretical framework for investigating this study’s research question, “is there a relationship between social media usage and socially responsible investing?”. Based on this framework, we hypothesize the following:

H₁: 

Individuals who use social media for investment information are more likely to engage in socially responsible investing when compared to those who do not.

2. Materials and Methods

The National Financial Capabilities Study (NFCS) provided the data for this study. The NFCS is a national, cross-sectional study of the financial capability of American adults commissioned by the FINRA Investor Education Foundation and conducted every three years. The 2021 wave was used for this study and consists of two surveys, the State-by-State survey and the Investor survey. The State-by-State survey includes a sample of 27,118 respondents and was used to provide demographic information for the study. The Investor survey was designed as a follow-up survey administered to State-by-State survey participants who owned investments outside of their retirement accounts; a total of 2824 adults completed the Investor Survey (Lin et al., 2022). This portion of the survey provided data related to the key variables for the study. Survey data was weighted to align with U.S. Census benchmarks for age, gender, ethnicity, and education to ensure it is nationally representative (FINRA, 2022). NFCS survey questions provided respondents with options of prefer not to say or don’t know for some survey questions. In line with prior studies that have used the same dataset (Chatterjee & Chang, 2025; Joseph et al., 2024), those responses were treated as missing values and removed from the sample. This resulted in a final sample size of 2083.

To investigate the relationship between reliance on social media for investment decisions and the likelihood of SRI, we estimated a binary logistic regression model. This approach models the log-odds of engaging in SRI as a function of social media use and a set of control variables. The general form of the equation is presented below:

$$\begin{array}{l}Log[{\displaystyle \frac{p}{1-p}}]={\beta }_0+{\beta }_1\mathrm{socialmedia}+{\beta }_2\mathrm{invexp}+{\beta }_3\mathrm{invrisk}+{\beta }_4\mathrm{invassets}+{\beta }_5\mathrm{age}+{\beta }_6\mathrm{gender}+{\beta }_7\mathrm{ethnicity}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+ {\beta }_8\mathrm{subjknow}+{\beta }_9\mathrm{objknow}+{\beta }_{10}\mathrm{ownhome}+{\beta }_{11}\mathrm{income}+{\beta }_{12}\mathrm{maritalstatus}+{\beta }_{13}\mathrm{edu}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+ {\beta }_{14}\mathrm{employ}\end{array}$$

Note: p = P(Y_SRI = 1). Abbreviations: invexp = investment experience, invrisk = investment risk preference, subjknow = subjective financial knowledge, objknow = objective financial knowledge, edu = education, and employ = employment status.

In addition to examining overall reliance on social media, a second model was developed to investigate the influence of individual social media platforms on socially responsible investing. This model includes separate indicators for social media platforms, allowing for a more detailed analysis of how platform-specific usage relates to SRI behavior. By disaggregating social media use in this way, the model provides a clearer view of which platforms may be more strongly associated with motivations for socially responsible investing. The general form of the equation is presented below:

$$\begin{array}{l}Log [{\displaystyle \frac{p}{1-p}}]={\beta }_0+{\beta }_1\mathrm{YouTube}+{\beta }_2\mathrm{Facebook}+{\beta }_3\mathit{Reddit}+{\beta }_4\mathit{TikTok}+{\beta }_5\mathit{Instagram}+{\beta }_6\mathit{Twitter}+{\beta }_7\mathit{Discord}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{0.17em}\hspace{0.17em}+ {\beta }_8\mathit{Twitch}+{\beta }_9\mathit{Clubhouse}+{\beta }_{10}\mathit{LinkedIn}+{\beta }_{11}\mathit{StockTwits}+{\beta }_{12}\mathrm{invexp}+{\beta }_{13}\mathrm{invrisk}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{0.17em}\hspace{0.17em}+ {\beta }_{14}\mathrm{invassets}+{\beta }_{15}\mathrm{age}+{\beta }_{16}\mathrm{gender}+{\beta }_{17}\mathrm{ethnicity}+{\beta }_{18}\mathrm{subjknow}+{\beta }_{19}\mathrm{objknow}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{0.17em}\hspace{0.17em}+ {\beta }_{20}\mathrm{ownhome}+{\beta }_{21}\mathrm{income}+{\beta }_{22}\mathrm{maritalstatus}+{\beta }_{23}\mathrm{edu}+{\beta }_{24}\mathrm{employ}\end{array}$$

Note: p = P(Y_SRI = 1). Abbreviations: invexp = investment experience, invrisk = investment risk preference, subjknow = subjective financial knowledge, objknow = objective financial knowledge, edu = education, and employ = employment status

The majority of participants were seasoned investors, with approximately 68% having 10 years or more of investment experience. The demographic makeup skewed towards White individuals (82%), married participants (67%), and male respondents (63%). Forty-two percent of the sample were aged 65 or older, consisting of 41% retired individuals and 38% employed full-time. Risk preferences varied across the spectrum, with 55% reporting average risk tolerance, 29% reporting above average risk tolerance, and 8% reporting substantial risk tolerance. Likewise, reported investment assets spanned from less than USD 50 K (29%) to over USD 500 K (26%). Income distribution among respondents was fairly even, with 20% earning less than USD 50 K, 39% earning between USD 50 K and USD 100 K, and 41% earning USD 100 K or more annually. Sample descriptives are shown in

Table 1. Sample Descriptives.

	Weighted n	%	n	%
Investment Experience
Less than 1 year	92.88	4.46	86	4.13
1 year to less than 2 years	179.11	8.60	159	7.63
2 years to less than 5 years	188.99	9.07	190	9.12
5 years to less than 10 years	232.90	11.18	222	10.66
10 years or more	1389.12	66.69	1426	68.46
Investment Assets
Less than USD 50 K	620.81	29.80	602	28.90
USD 50 K up to USD 100 K	257.50	12.36	265	12.72
USD 100 K up to USD 500 K	703.10	33.75	685	32.89
USD 500 K up to USD 1 M	239.76	11.51	256	12.29
USD 1 M or more	261.84	12.57	275	13.20
Investment Risk Preference
Take substantial financial risk	181.29	8.70	174	8.34
Take above average financial risk	563.86	27.07	595	28.51
Take average financial risk	1152.48	55.33	1142	54.72
Not willing to take any financial risk	185.37	8.90	176	8.43
Income
Less than USD 50 K	436.17	20.94	424	20.36
USD 50 K up to USD 100 K	807.23	38.75	805	38.65
USD 100 K or more	839.60	40.31	854	41.00
Age
18 to 24	68.94	3.31	57	2.74
25 to 34	152.17	7.31	152	7.30
35 to 44	278.54	13.37	268	12.87
45 to 54	248.10	11.91	268	12.87
55 to 64	475.78	22.84	470	22.56
65 and older	859.47	41.26	868	41.67
Gender
Male	1328.83	63.79	1322	63.47
Female	754.17	36.21	761	36.53
Ethnicity
White, non-Hispanic	1556.78	74.74	1717	82.43
Non-White	526.22	25.26	366	17.57
Education
HS or less	200	9.60	242	11.63
Some college	373	17.91	409	19.66
Associate’s	212	10.18	241	11.58
Bachelor’s	816	39.17	744	35.71
Post-graduate	482	23.14	446	21.43
Employment Status
Self-employed	171.57	8.24	175	8.40
Full-time	773.16	37.12	792	38.02
Part-time	128.57	6.17	132	6.34
Retired	858.13	41.20	851	40.85
Other	151.57	7.28	133	6.39
Marital Status
Married	1392.61	66.86	1389	66.68
Single	380.44	18.26	374	17.95
Separated/Divorced/Widowed	309.95	14.88	320	15.36
	n	Mean	Std Dev	Min/Max
Subjective financial knowledge	2083	4.80	1.34	1/7
Objective financial knowledge	2083	4.37	1.41	0/6

Note. n = 2083.

.

2.1. Dependent Variable

Socially responsible investing was the dependent variable in the model and was measured with the question asking respondents “How well does each of the following describe why you invest?”, and the response chosen was “To make a difference in the world/support values I care about/be socially responsible”. Responses were categorized as a binary measure 1—somewhat or very well and 0—not at all.

2.2. Independent Variables

The key independent variables for this study included (1) social media used for investment decisions and (2) the usage of social media for financial information. The use of social media platforms for financial information was measured with the prompt asking respondents “Which, if any, of the following do you use for information about investing?”. Each social media platform was coded with a dichotomous response of 1—Yes or 0—No. The complete list of each type of social media platform included in the models is detailed in

Table 2. Key Independent Variables.

	Weighted n	%	n	%
Social Media Platforms Used for Investing Information
YouTube	417.68	20.05	408	18.83
Facebook	221.06	10.61	225	10.41
Reddit	219.69	10.55	220	10.24
Twitter	211.28	10.14	205	9.59
Discord	119.44	5.73	107	5.03
Twitch	66.65	3.20	64	3.01
Clubhouse	56.53	2.71	60	2.83
LinkedIn	189.32	9.09	175	8.26
Instagram	176.54	8.48	162	7.57
Stocktwits	117.47	5.64	108	5.11
TikTok	105.83	5.08	109	5.08
Social Media Groups Used for Investment Decisions
Not at all	1652.48	79.33	1727	79.70
Somewhat	315.60	14.94	316	14.58
A great deal	114.92	5.52	124	5.72

Note. N = 2083.

. Reliance on social media for investment decisions was operationalized with the question “How much do you rely on each of the following when making decisions about what to invest in?”, and the response chosen was “social media groups or message boards where people post investment ideas”. Responses were categorized as 1—not at all, 2—somewhat, and 3—a great deal.

2.3. Control Variables

Prior research has shown that demographic factors such as gender, age, education, and financial knowledge influence preferences for following SRI strategies. Aristei and Gallo (2023) found that higher levels of financial knowledge increased preferences for SRI for both genders, although some scholars have found women to have stronger preferences for this strategy (Aulia et al., 2024; Pasquino & Lucarelli, 2025). Prior studies have also found overconfidence to be related to risk assessment and investment decisions (Altaf & Jan, 2023); therefore, both objective and subjective financial knowledge were controlled for in the model. Similarly, investors with higher education levels have been found to have greater demand for investments in this category (Aulia et al., 2024; Rossi et al., 2019). Systematic reviews have also summarized studies that examined age and sustainable investing and found mixed results. Aulia et al. (2024) identified studies showing older males with lower levels of income and wealth as more inclined toward SRI. Yet, in a review of forty-five papers, Pasquino and Lucarelli (2025) identified studies showing both older and younger adults attracted to SRI, driven by Internet usage for younger investors and affluence for older investors. Research on investment risk preferences and their relationship with SRI is also mixed, with many studies aligning SRI with risk-averse investors while others find that when SRI is perceived as riskier than conventional investments, investors seeking risk diversification are willing to pay for SRI options (Junkus & Berry, 2010; Pasquino & Lucarelli, 2025). As a result, these demographic measures, along with investment risk preference, were included as control variables in each of this study’s models.

Table 3. Control Variable Measurements.

Variable	Measurement
Age	18–24, 25–34, 35–44, 45–54, 55–64, or 65 and older
Gender	Male or female
Race	White non-Hispanic or non-White
Marital status	Married, single, or separated/divorced/widowed
Education	HS or less, some college, associate’s, bachelor’s, or post-graduate
Employment status	Self-employed, full-time, part-time, retired, or other *
Objective financial knowledge
Interest	(1) Suppose you had USD 100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?
Inflation	(2) Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?
Bond pricing	(3) If interest rates rise, what will typically happen to bond prices?
Compounding	(4) Suppose you owe USD 1000 on a loan and the interest rate you are charged is 20% per year compounded annually. If you did not pay anything off, at this interest rate, how many years would it take for the amount you owe to double?
Mortgage	(5) A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be less.
Portfolio risk	(6) Buying a single company’s stock usually provides a safer return than a stock mutual fund.
Subjective financial knowledge	‘How would you assess your overall knowledge about investing?’ on a 7-point scale (1 = very low to, 7 = very high)
Investment assets	Less than USD 50 K, USD 50 K up to USD 100 K, USD 100 K up to USD 500 K, USD 500 K up to USD 1 M, or USD 1 M or more
Investment experience	Less than a year ago, 1 year to less than 2 years ago, 2 years to less than 5 years ago, 5 years to less than 10 years ago, or 10 years ago or more
Investment risk preference	Substantial financial risks, above average financial risks, average financial risks, or not willing to take any financial risks

Note: * Other = student, homemaker, disabled, or unemployed.

includes a full description for each measure.

3. Results

A correlation matrix was generated to examine bivariate correlations among the social media platforms used in the study (see

Table 4. Correlation Analysis Among Social Media Platforms.

	1	2	3	4	5	6	7	8	9	10	11
1	1.00
2	0.51 ***	1.00
3	0.43 ***	0.34 ***	1.00
4	0.38 ***	0.44 ***	0.34 ***	1.00
5	0.48 ***	0.62 ***	0.34 ***	0.59 ***	1.00
6	0.48 ***	0.52 ***	0.44 ***	0.49 ***	0.63 ***	1.00
7	0.39 ***	0.34 ***	0.42 ***	0.40 ***	0.44 ***	0.45 ***	1.00
8	0.27 ***	0.38 ***	0.34 ***	0.45 ***	0.47 ***	0.43 ***	0.44 ***	1.00
9	0.27 ***	0.35 ***	0.31 ***	0.46 ***	0.45 ***	0.39 ***	0.44 ***	0.52 ***	1.00
10	0.41 ***	0.42 ***	0.30 ***	0.39 ***	0.45 ***	0.41 ***	0.37 ***	0.37 ***	0.38 ***	1.00
11	0.25 ***	0.30 ***	0.33 ***	0.26 ***	0.29 ***	0.32 ***	0.34 ***	0.43 ***	0.44 ***	0.34 ***	1.00

Note. N = 2083; 1—YouTube, 2—Facebook, 3—Reddit, 4—TikTok, 5—Instagram. 6—Twitter, 7—Discord, 8—Twitch, 9—Clubhouse, 10—LinkedIn, and 11—Stocktwits; * p < 0.05, ** p < 0.01, and *** p < 0.001.

). The results showed that most platforms had low to moderate positive correlations. However, Pearson’s correlations between a few of the platforms could be considered strong (Bishara & Hittner, 2012; Cohen et al., 2003). For example, Instagram and Twitter (r(2083) = 0.63, p < 0.001), Facebook and Instagram (r(2083) = 0.62, p < 0.001), TikTok and Twitter (r(2083) = 0.59, p < 0.001), Facebook and Twitter (r(2083) = 0.52, p < 0.001), and YouTube and Facebook (r(2083) = 0.62, p < 0.001) each showed relative strong correlations. These findings prompted a review for potential multicollinearity prior to conducting the regression analyses. Multicollinearity was examined using variance inflation factors (VIFs). The results showed that all VIF values were well below the commonly used threshold of 10, with a mean VIF of 1.58, indicating that multicollinearity was not a concern (Thompson et al., 2017).

Next, a chi-square test of independence was used to examine the relationship between respondents’ motivations for investing and the extent to which they rely on social media when making investment decisions. The results indicated a statistically significant association between why people invest and how much they rely on social media when making investment decisions [Χ²(4, n = 2083) = 375.3927, p < 0.001]. Then, to examine the strength of this relationship and control for demographic and behavioral factors, we estimated a regression model. In the initial model, we examined the relationship between the use of social media for investing decisions and the outcome of socially responsible investing. A binary logistic regression model was chosen due to the binary nature of the dependent variable.

3.1. SRI in the Age of Digital Information

Table 5. Logistic Regression Model—The Influence of Social Media Use for Investment Decisions on Socially Responsible Investing.

	Socially Responsible Investing
	β(SE)	OR	95% CI
Social media	0.91(0.15) ***	2.49	[1.87, 3.31]
Investment Exp (ref 10 yr or more)
Less than 1 yr	0.31(0.29)	1.36	[0.77, 2.40]
1 yr to less than 2 yr	0.61(0.23) **	1.84	[1.18, 2.87]
2 yr to less than 5 yr	0.52(0.20) **	1.68	[1.14, 2.47]
5 yr to less than 10 yr	0.29(0.17)	1.33	[0.95, 1.88]
Investment Risk Preference (ref Average Risk)
Take substantial risk	0.18(0.21)	1.20	[0.80, 1.79]
Take above average risk	0.02(0.12)	1.02	[0.81, 1.29]
Not willing to take risk	−0.18(0.20)	0.83	[0.57, 1.22]
Age (ref 65 and older)
18 to 24	0.68(0.41)	1.96	[0.88, 4.37]
25 to 34	0.23(0.27)	1.25	[0.74, 2.13]
35 to 44	−0.11(0.22)	0.90	[0.59, 1.37]
45 to 54	−0.16(0.19)	0.85	[0.58, 1.24]
55 to 64	−0.03(0.15)	0.97	[0.72, 1.30]
Investment Assets (ref less than USD 50 K)
USD 50 K up to USD 100 K	0.09(0.18)	1.09	[0.77, 1.54]
USD 100 K up to 500 K	0.10(0.14)	1.11	[0.84, 1.47]
USD 500 K up to USD 1 M	0.09(0.19)	1.09	[0.75, 1.58]
USD 1 M or more	0.13(0.20)	1.14	[0.77, 1.67]
Subj financial knowledge	0.30(0.04) ***	1.35	[1.24, 1.48]
Obj financial knowledge	−0.21(0.04) ***	0.81	[0.75, 0.88]
Gender (ref Male)
Female	0.46(0.11) ***	1.59	[1.27, 1.98]
Own Home (ref Yes)
No	−0.37(0.16) *	0.69	[0.51, 0.95]
Ethnicity (ref White non-Hispanic)
Non-White	0.41(0.13) **	1.51	[1.17, 1.96]
Income	0.00(0.08)	1.00	[0.85, 1.18]
Marital Status (ref Married)
Single	0.14(0.16)	1.15	[0.85, 1.54]
Separated/Divorced	0.27(0.15)	1.31	[0.98, 1.76]
Education (ref Bachelor’s)
HS or less	−0.33(0.19)	0.72	[0.49, 1.04]
Some college	−0.27(0.15)	0.76	[0.57, 1.02]
Associate’s	−0.28(0.18)	0.76	[0.53, 1.08]
Post-graduate	0.21(0.13)	1.23	[0.95, 1.58]
Employment (ref Full-time)
Self-employed	0.02(0.19)	1.02	[0.71, 1.48]
Part-time	0.20(0.22)	1.22	[0.80, 1.85]
Retired	−0.46(0.16) **	0.63	[0.46, 0.86]
Other +	−0.25(0.23)	0.78	[0.50, 1.21]
N	2083
Log pseudo-likelihood	−1211.626
Chi-square	370.32 ***
Pseudo R2	0.133

Note. OR = odds ratio; SE = robust standard error; CI = confidence intervals; ⁺ Other = student, homemaker, disabled, or unemployed; * p < 0.05, ** p < 0.01, and *** p < 0.001.

presents the results of the logistic regression analysis which examined the relationship between relying on social media for investment decisions and SRI. The findings showed that, after controlling for other factors, investors who reported using social media for investment decisions had 2.49 times higher odds of engaging in SRI on some level (β = 0.91, OR = 2.49, p < 0.001) compared to those who are not motivated to practice this type of investing. In other words, these investors were 125% more likely to report a motivation to practice SRI.

Relative to experienced investors, those with investment experience of 1 year to 2 years (β = 0.61, OR = 1.84, p < 0.01) and 2 years up to 5 years (β = 0.52, OR = 1.68, p < 0.01) had a 84% and 68% higher odds of following SRI strategies, respectively. Objective financial knowledge was negatively associated with the likelihood of self-identifying as practicing SRI (β = −0.21, OR = 0.81, p < 0.001), indicating that for each unit increase in objective knowledge, the odds of identifying as an SRI investor decreased by 19%. Conversely, subjective financial knowledge was positively associated with practicing SRI (β = 0.30, OR = 1.35, p < 0.001). Specifically, each unit increase in subjective knowledge was associated with 35% higher odds of practicing SRI.

Demographic characteristics such as non-White ethnicity (β = 0.41, OR = 1.51, p < 0.01), female gender (β = 0.46, OR = 1.59, p < 0.001), and not owning a home (β = −0.37, OR = 0.69, p < 0.05) were each significantly related to identifying with SRI. Of these measures, only non-Whites—compared to Whites and not owning a home—showed a positive relationship with this investing strategy. Retired respondents had 37% lower odds of self-identifying with following SRI practices (β = −0.46, OR = 0.63, p < 0.01), compared to those working full-time. Factors including investment assets, risk preference, age, marital status, income and education levels had no significant relationships with SRI for those who self-identify with this investment strategy.

3.2. Social Media Platforms and SRI

To build on the initial analysis, multiple social media platforms were explored to gain deeper insight into their relationship with socially responsible investing on a more granular level. Of the social media platforms examined, YouTube (β = −0.38, OR = 0.68, p < 0.05), Twitter (β = −0.53, OR = 0.59, p < 0.05), and LinkedIn (β = −0.55, OR = 0.58, p < 0.05) users had 32%, 41%, and 42% significantly lower odds of practicing SRI, respectively.

Important demographic associations emerged in this model, with some patterns mirroring what was found in the initial model. For example, homeownership was significantly related to a motivation for this investing practice. Renters (β = −0.35, OR = 0.70, p < 0.05) had 30% lower odds of practicing SRI compared to homeowners. Similarly, work status was also significant. Retirees (β = −0.45, OR = 0.64, p < 0.05) also had 36% lower odds of practicing SRI compared to full-time workers. Females (β = 0.52, OR = 1.68, p < 0.001) had a 68% higher odds of practicing SRI compared to males. Non-white participants (β = 0.41, OR = 1.51, p < 0.01) were 1.51 times more likely to identify with this strategy, compared to White participants.

Investment experience was significantly related to SRI. Investors with 1 year to 2 years (β = 0.51, OR = 1.67, p < 0.05) of investment experience had 67% higher odds of following SRI, and those with 2 years to 5 years (β = 0.44, OR = 1.55, p < 0.05) of investment experience had 55% higher odds relative to experienced investors with 10 years or more of experience. Interestingly, objective and subjective financial knowledge were significantly associated with SRI. Those with objective financial knowledge (β = −0.15, OR = 0.86, p < 0.001) had 17% lower odds of identifying with SRI. This indicates that for each unit increase in objective financial knowledge, the odds of practicing SRI decreased by 17%. In contrast, subjective financial knowledge (β = 0.26, OR = 1.30, p < 0.001) had 30% higher odds of practicing SRI. Full results are shown in

Table 6. Logistic Regression Model–The Relationship Between Social Media Platforms Used for Financial Information and Socially Responsible Investing.

	Socially Responsible Investing
	β(SE)	OR	95% CI
Social Media Platforms Used for Financial Information
YouTube	−0.38(0.17) *	0.68	[0.49, 0.94]
Facebook	−0.36(0.24)	0.70	[0.44, 1.11]
Reddit	−0.23(0.21)	0.80	[0.52, 1.21]
TikTok	−0.11(0.38)	0.90	[0.43, 1.87]
Instagram	−0.63(0.34)	0.53	[0.27, 1.03]
Twitter	−0.53(0.25) *	0.59	[0.36, 0.97]
Discord	0.12(0.32)	1.12	[0.60, 2.12]
Twitch	−0.12(0.50)	0.89	[0.33, 2.39]
Clubhouse	0.24(0.52)	1.27	[0.46, 3.50]
LinkedIn	−0.55(0.23) *	0.58	[0.37, 0.91]
Stocktwits	−0.21(0.29)	0.81	[0.46, 1.43]
Investment Exp (ref 10 yr or more)
Less than 1 yr	0.29(0.29)	1.34	[0.76, 2.39]
1 yr to less than 2 yr	0.51(0.23) *	1.67	[1.06, 2.63]
2 yr to less than 5 yr	0.44(0.20) *	1.55	[1.05, 2.31]
5 yr to less than 10 yr	0.21(0.18)	1.24	[0.87, 1.76]
Investment Risk Preference (ref Average Risk)
Take substantial risk	−0.01(0.22)	0.99	[0.64, 1.53]
Take above average risk	0.01(0.12)	1.00	[0.80, 1.27]
Not willing to take risk	−0.21(0.20)	0.81	[0.55, 1.19]
Age (ref 65 and older)
18 to 24	0.40(0.43)	1.50	[0.64, 3.51]
25 to 34	0.09(0.28)	1.10	[0.64, 1.89]
35 to 44	−0.13(0.22)	0.88	[0.57, 1.35]
45 to 54	−0.20(0.20)	0.81	[0.56, 1.20]
55 to 64	−0.02(0.15)	0.98	[0.73, 1.31]
Investment Assets (ref less than USD 50 K)
USD 50 K up to USD 100 K	0.07(0.18)	1.07	[0.75, 1.52]
USD 100 K up to 500 K	0.05(0.15)	1.05	[0.79, 1.41]
USD 500 K up to USD 1 M	0.05(0.19)	1.05	[0.72, 1.54]
USD 1 M or more	0.12(0.20)	1.13	[0.77, 1.66]
Subj financial knowledge	0.26(0.05) ***	1.30	[1.19, 1.42]
Obj financial knowledge	−0.18(0.04) ***	0.83	[0.77, 0.91]
Gender (ref Male)
Female	0.52(0.11) ***	1.68	[1.34, 2.09]
Own Home (ref Yes)
No	−0.35(0.16) *	0.70	[0.51, 0.97]
Ethnicity (ref White non-Hispanic)
Non-White	0.41(0.13) **	1.51	[1.16, 1.96]
Income	−0.01(0.08)	0.99	[0.84, 1.17]
Marital Status (ref Married)
Single	0.19(0.15)	1.20	[0.89, 1.63]
Separated/Divorced	0.25(0.15)	1.29	[0.96, 1.72]
Education (ref Bachelor’s)
HS or less	−0.31(0.19)	0.74	[0.50, 1.08]
Some college	−0.24(0.15)	0.79	[0.59, 1.06]
Associate’s	−0.24(0.18)	0.79	[0.55, 1.12]
Post-graduate	0.19(0.13)	1.21	[0.93, 1.56]
Employment (ref Retired)
Self-employed	0.02(0.19)	1.02	[0.70, 1.49]
Part-time	0.21(0.22)	1.23	[0.80, 1.88]
Retired	−0.45(0.16) *	0.64	[0.47, 0.88]
Other +	−0.23(0.23)	0.79	[0.51, 1.24]
N	2083
Log pseudo-likelihood	−1195.078
Wald Chi-square	403.42 ***
Pseudo R2	0.144

Note. OR = odds ratio; SE = robust standard error; CI = confidence intervals; ⁺ Other = student, homemaker, disabled, or unemployed; * p < 0.05, ** p < 0.01, and *** p < 0.001.

.

4. Discussion

This study explored the relationship between social media use and the motivation for socially responsible investing, providing insights into the influence digital communication platforms have on financial decisions. The findings support the study’s hypothesis that individuals who use social media for investment information had higher odds of being motivated to engage in SRI when compared to those who do not. This suggests that social media may play a meaningful role in shaping perceptions related to ethical investing. In line with prior research examining the influence of media on SRI performance (Lei & Zhang, 2020), the results indicate that users of social media were more likely to report a preference for this style of investing. These findings suggest that social media may play a role in reinforcing values and amplifying narratives that are associated with an individual’s motivations or preferences for SRI, though the direction and nature of this relationship cannot be definitively established with the current data. The cross-sectional design and potential unobserved factors limit causal interpretation and highlight the need for longitudinal or experimental design in future research.

While SRI is a broad umbrella encompassing ethical, environmental, and governance-based investment strategies, our study focuses specifically on the values-based motivation dimension of SRI. This approach allows us to maintain conceptual clarity while capturing investor sentiment tied to personal and societal values. However, we recognize that the broader SRI category includes multiple strategic and ideological layers, and future research could refine this analysis by distinguishing between distinct forms of socially responsible behavior, such as ESG screening, impact investing, or shareholder advocacy.

Interestingly, when individual social media platforms were considered, only a few had significant relationships. These include YouTube, Instagram, LinkedIn, and Twitter. Moreover, with the exception of Twitter, users of those platforms were less likely to adopt this investment strategy. The negative relationship between these platforms and the likelihood of identifying with SRI may reflect differences in platform content or audience demographics. For example, Instagram and YouTube users may receive messaging more focused on consumption or short-term financial tips, which may conflict with the long-term, values-driven orientation of SRI. LinkedIn’s results may also seem counterintuitive but could reflect a more traditional, career-focused user base. Overall, these findings suggest that not all social media platforms are equally effective channels for promoting SRI. The differing associations across platforms may reflect variations in content type, user demographics, or platform norms; however, these interpretations remain speculative. Future research could investigate these possibilities using content analysis or user-level engagement data. Longitudinal data or experimental designs could also help disentangle the directionality of the relationship between social media engagement and SRI motivation.

Herding behavior and confirmation bias are psychological influences that are especially relevant in this digital landscape. Herding behavior occurs as users mimic investment trends popularized on social media (Bikhchandani & Sharma, 2001), while confirmation bias leads individuals to seek and engage with content that reinforces their existing beliefs (Cheng, 2019). On platforms that reward visibility and emotional resonance, content related to SRI may be disproportionately amplified. As a result, social media may not only promote awareness of SRI but also reinforce users’ values and influence the likelihood of participation in such investments (Lei & Zhang, 2020). However, while we acknowledge the dual role of social media as both a source of awareness and a potential amplifier of behavioral biases, our analysis is limited in its ability to disentangle these effects. The NFCS dataset does not include direct measures of cognitive or emotional mechanisms such as herding behavior or confirmation bias. Future research could extend our findings by incorporating psychological scales or experimental designs that assess how individuals process investment content across platforms. This would allow for a clearer distinction between informational learning and behavioral distortion in the context of values-driven investing.

Despite these compelling intersections, there were limitations that should be noted. First, cross-sectional data was used for the study’s analysis, so causal relationships between social media use and SRI cannot be established. While this study identifies differing associations between specific social media platforms and socially responsible investing, the use of secondary survey data limits our ability to investigate the underlying reasons for these relationships. Future research could build on these findings by using qualitative methods or content analysis to explore how the nature of investment-related content varies across platforms and influences investor behavior. Next, the study relied upon self-reported social measures, particularly social media use and SRI investing, which may be subject to social desirability bias. A valuable direction for future research involves supplementing quantitative analyses with qualitative interviews or surveys to better capture the nuances of social media engagement, e.g., the influence of financial influencers or community discussions, and to provide context that may help mitigate social desirability bias in self-reported measures. Lastly, there may also be differences in how users engage with social media platforms, e.g., scrolling vs. active sharing, that was not reflected in the measures used and could influence SRI in nuanced ways. This study is limited by the use of binary and categorical measures of social media use, which do not capture important aspects such as frequency, time spent, or the nature of engagement. Future research should incorporate more nuanced indicators to better understand how different types of social media involvement influence socially responsible investing.

Empirical research exploring the connections among social media usage, SRI, and underlying behavioral mechanisms such as herding and confirmation bias remains limited. While previous studies have investigated financial literacy, risk tolerance, and demographic predictors of SRI, the influence of digital engagement on values-based investing has received comparatively little scholarly attention. This study addresses that gap in the literature.

Emerging literature suggests that herding behavior on social media can amplify certain investment trends, as users are exposed to repeated messages and actions from peers and influencers. At the same time, confirmation bias, may intensify among social media users who selectively engage with content that aligns with their ethical or ideological investment preferences. Together, future research could create a feedback loop, whereby exposure to socially responsible content on social media strengthens users’ commitment to SRI. Since the dataset used for this study does not capture the type of content consumed or the nature of user engagement, such as following influencers or participating in discussions, we were unable to empirically test psychological mechanisms like herding behavior or confirmation bias. However, this should be explored in future research.

5. Conclusions

This study explored the relationship between social media use and SRI, finding a strong association between relying on social platforms for investment decisions and a greater likelihood of motivation for SRI investments. Using NFCS 2021 survey data, we found that investors who turn to social media, especially younger and less experienced ones, are more likely to express values-based motivations when making investment decisions. While this relationship holds even after controlling for demographics and financial characteristics, not all platforms had the same effect. Platforms such as YouTube, Instagram, and LinkedIn were associated with a lower likelihood of valuing SRI very well. This suggests that the way information is presented and the type of engagement found on each platform can shape how people think about and act on values-based investing.

These findings suggest that social media may play a role in shaping investment decisions, not just by spreading information but also by reinforcing social norms and shared values. For financial professionals, this highlights the importance of understanding where clients obtain their information and how that shapes their perceptions and motivations. Financial planners now operate in an environment where clients often arrive with pre-formed opinions shaped by viral content or influencer narratives, necessitating greater adaptability and communication skills to debunk myths and guide sound decisions. From a policy perspective, recognizing the influence of social media could inform educational initiatives aimed at promoting critical digital literacy around investing. Academically, these results highlight the need for further research into the types of content and engagement that influence investor behavior and how these dynamics interact with individual values and biases. As SRI continues to grow, social media is likely to remain a powerful influence, impacting ethical investing and also amplifying potential biases. Future research should look more closely at how specific types of content and interactions affect investor choices.