Role of Finfluencer Advice Across Behavioral Clusters in Shaping Sustainable Finance

Role of Finfluencer Advice Across Behavioral Clusters in Shaping Sustainable Finance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Role of Finfluencer Advice Across Behavioral Clusters in Shaping Sustainable Finance Ritika Bhatia, Alpa Sethi, Vaidehi Panjwani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7759133/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in Discover Sustainability → Version 1 posted 12 You are reading this latest preprint version Abstract This study explores how trust, impulsiveness, and verification tendencies shape individuals’ preferences for budgeting and investment advice on social media. A survey of 357 Indian users, combined with K-means clustering, ANOVA, and thematic analysis, revealed that budgeting preferences vary across behavioral clusters, while investment preferences do not. Verification and trust predicted budgeting content preference, whereas impulsiveness reduced preference for structured advice. Simplicity, credibility, and emotional appeal drove trust. A typology categorizing advice formats was proposed. The study provides practical insights for finfluencers and educators on promoting sustainable financial practices. The findings of the study contribute to behavioral finance by integrating user behavior, content analytics, and qualitative insights. Finfluencer Social media Behavioral Traits Sustainable Finance Figures Figure 1 Figure 2 Figure 3 1. Introduction Social media platforms such as YouTube, Instagram, and Twitter have significantly reshaped the landscape of financial education, especially among younger investors and digitally engaged individuals [9,33]. Central to this evolution is the emergence of financial influencers, popularly known as “finfluencers,” who utilize storytelling, visually appealing content, and simplified explanations to democratize complex financial information for mass audiences [3,16]. The rise of finfluencers has notably enhanced accessibility to financial literacy and participation in financial markets, reducing the barriers traditionally associated with formal financial advisory services and enabling broader audience segments to actively engage in financial planning and investment activities [8,32,36]. This study is situated within the Indian context, where collectivist values, social media credibility dynamics, and rapid digitization intersect to shape unique consumer interpretations of financial advice. While social media platforms have democratized access to financial information, they have also raised serious concerns regarding the accuracy, reliability, and regulation of online financial advice [8,29]. Many finfluencers operate without formal financial credentials, leading to the dissemination of oversimplified, biased, or even misleading guidance [3,16]. Research highlights that persuasive techniques, emotional appeal, and social validation cues (such as likes, shares, and comments) often drive engagement, sometimes at the expense of factual accuracy [1,13,21]. The absence of standardized regulatory frameworks exacerbates the risks, allowing financial misinformation to spread rapidly and influence individuals’ sustainable financial practices adversely. These challenges have made it critical to understand how users assess, trust, and act upon financial content presented through social media platforms [35]. Despite the growing academic attention on the role of social media in shaping individuals’ financial behavior, significant research gaps remain. First, much of the existing work focuses on the general impact of finfluencers without systematically examining how users’ psychological characteristics—such as trust, impulsiveness, and verification tendencies—interact with the design and framing of financial content [13]. Second, studies often rely heavily on demographic segmentation, overlooking deeper behavioral and cognitive patterns that influence sustainable financial decision-making [11, 27]. Third, prior research tends to treat financial content on social media as homogenous, ignoring potential differences in the framing of financial content and how users perceive distinct types of financial advice, such as budgeting strategies versus investment recommendations [9, 16, 25]. Lastly, there is limited empirical work in the Indian context, despite India’s rapidly growing digital finance ecosystem, creating a need for localized investigations that capture unique cultural and behavioral nuances [34]. Understanding user engagement with financial advice on social media necessitates drawing upon foundational theories from behavioral finance, psychology, and social cognition. Behavioral finance research emphasizes that financial decision-making is rarely rational and is instead heavily shaped by cognitive heuristics and psychological biases, including confirmation bias, anchoring, mental accounting, and framing effects [27,28]. Kahneman and Tversky’s Prospect Theory further explains how individuals perceive financial gains and losses asymmetrically, often leading to risk-averse or risk-seeking behavior depending on the framing of information [15]. On social media platforms, these cognitive biases are exacerbated by information overload, emotional triggers, and social validation signals such as likes, shares, and comments, which serve as heuristics for credibility [13,22,30]. The theory of information overload suggests that the rapid flow of information can overwhelm users’ cognitive processing abilities, making them more reliant on superficial cues rather than critical evaluation [1,29]. Additionally, social cognitive theory posits that observational learning, social influence, and peer modeling significantly impact behavioral outcomes—factors that are particularly potent in social media environments [2]. Building on the above theoretical perspectives, this study aims to fill important gaps in the current understanding of how psychological characteristics and demographic factors shape user interactions with financial content on social media. Specifically, the research pursues the following objectives: To identify different behavioral clusters of users’ based on the combinations of their behavioral traits. To investigate whether behavioral clusters affect users’ preferences for different types of social media financial content. To explore whether users’ demographic variables, including age, gender, income, and employment status, moderate the relationship between behavioral traits and their preferences for different types of social media financial content. By integrating behavioral traits and their clusters with social media content preference analysis and demographic moderation, this study offers a nuanced perspective on how users assess and engage with personal finance content in digital environments. The findings contribute to behavioral finance and social media research by highlighting the role of users’ behavioral traits and cognitive biases in influencing financial decision-making. Moreover, the results provide actionable insights for educators, platform designers, finfluencers, and policymakers aiming to foster more responsible and effective financial communication on social platforms, thereby encouraging sustainable financial behavior among individuals. 2. Literature Review Social media platforms have created new channels for financial information. Recently, it has evolved as a popular destination for providing financial advice. In India, research shows that social media is the most preferred source of information for young adults looking to make investment decisions, and many survey respondents chose social media over a financial advisor [9, 34]. The “finfluencers” on YouTube, Instagram, Twitter and other social media palatiform are providing financial tips in a simpler, easily digestible way [3, 10, 16]. Many finfluencers mix financial education with personal anecdotes and simplified investing instructions, targeting users who may have a lower level of financial knowledge. This democratization of financial information has provided a wider audience with exposure to financial education and investment strategies. This research, however, also suggests that social media financial content is often unvetted, driven by commercial interests, and lacking accountability [10,11,29]. Finfluencers often have no formal qualifications, and while the primary goal of many social media financial content creators may be to help their viewers, they frequently have partnerships and sponsorship deals which color their advice [10,11,29]. For example, one paper suggests that financial misinformation on social media has skewed users’ beliefs about the stock market and pushed them to make decisions based on viral or emotionally charged content [13, 29, 30]. In another study, the lack of quality control and institutional oversight is mentioned as a risk of social media for users; there are no clear disclaimers or evidence to back up advice that is given, whether that is for general budgeting, long-term investing, or day trading [11, 26, 29, 30]. While platforms such as Instagram have introduced new financial content advertising policies in recent years, a lack of transparency on algorithms and platform-level enforcement is a continuing challenge, especially as advertisers use cross-platform approaches to reach users [4, 18]. The result is a largely unregulated marketplace that creates significant risks for users who do not have a background or training in assessing the quality and reliability of digital content. Behavioral finance research has also suggested that financial decisions are influenced not just by the actual merits of a financial product or advice, but also by cognitive biases, heuristics, and emotion. Some of the effects of content design, network effects, and social comparison may be rooted in these psychological decision-making shortcuts, and social media content amplifies many of them [19, 27, 28]. Confirmation bias, for example, leads users to gravitate toward the social media account that affirms their existing beliefs and opinions, while anchoring bias and mental accounting affect how users perceive price points and budget for their finances [27, 28]. Emotional factors also play a role: fear of missing out (FOMO), overconfidence in their own decision-making, or social validation often pushes users to act on information that might be unverified or unvetted, especially when the CTA includes urgent language or validation from one’s peers [13,16]. The constant stimulation of new financial content and topics may also lead to cognitive overload and users resorting to cues like likes, shares, number of posts, formatting, and other simple decision-making heuristics instead of actually critically evaluating financial decisions [22, 30]. Despite growing academic interest in the role of social media in financial decision-making, important gaps remain. First, while previous studies have examined the general impact of finfluencers and content framing, few have explored how users’ behavioral traits—such as trust, impulsiveness, and verification tendencies—interact with content framing to shape sustainable financial decisions [9,16]. Second, existing research has primarily analyzed user behavior through demographic lenses, often overlooking latent psychological patterns that may better explain variations in content interpretation and preference [6,13,27]. Third, most studies treat social media financial content as a homogenous category, without investigating how users respond differently to distinct types of content framing and financial advice (e.g., budgeting vs. investing) [9,25]. Finally, there is limited empirical work on how these behavioral and demographic factors jointly influence engagement patterns, particularly in India. To address these gaps, this study employs behavioral clustering and content preference analysis, adjusted for demographic variables, to evaluate the behaviors of social media users that govern their actions towards financial advice content. 3. Theoretical Background and Hypotheses Development The increasing reliance on social media platforms for financial decision-making has raised critical questions about how individuals or users of social media process and respond to online financial advice. Behavioral finance literature suggests that financial decisions are shaped not purely by rational evaluation, but by cognitive heuristics and psychological biases such as impulsiveness, confirmation bias, and reliance on social proof [27,28]. Social media environments exacerbate these biases through emotionally framed content, peer endorsement signals (likes, shares), and information overload, potentially impairing users’ critical evaluation capabilities [13, 22,30,33 ]. Prospect Theory further highlights that individuals perceive gains and losses asymmetrically depending on how information is framed, which has direct implications for the design and reception of financial content [15]. At the same time, Social Cognitive Theory emphasizes the role of observational learning and social influence in shaping behavioral outcomes, particularly in environments rich with peer-generated content like social media [2]. These theories collectively suggest that individual differences in behavioral traits—such as impulsiveness, trust in social media, and verification tendencies—play a crucial role in determining how financial advice is interpreted and acted upon. Furthermore, demographic factors such as age, gender, income, and employment status may moderate these relationships, influencing the degree to which behavioral traits impact content preferences. Lastly, the nature of financial advice—whether related to budgeting or investment—may itself interact with user traits and cognitive biases to shape content preferences differently. The visual and interactive design of platforms like Instagram or LinkedIn—whether through short-form reels, carousel posts, or comment sections—may amplify or suppress the perceived authority and emotional framing of content, requiring platform-specific analysis. Based on these theoretical perspectives, a conceptual framework is proposed to explain the influence of behavioral traits and demographic factors on financial content preferences on social media. The conceptual framework illustrating the proposed relationships among behavioral traits, demographic factors and financial content preferences (budgeting and investment choices) is presented in Fig.1. 3.1. Behavioral Traits and Financial Contents Behavioral finance theories emphasize that individual financial decision-making is significantly shaped by cognitive heuristics and psychological biases rather than purely rational analysis [27,28]. In the context of social media, specific behavioral traits such as impulsiveness, trust in social media content, and verification tendency critically influence how users evaluate and act upon financial advice. Impulsiveness, characterized by emotional reactivity and a tendency toward rapid decision-making, increases users’ likelihood of responding to emotionally framed and persuasive financial posts without critically assessing their credibility [13,22]. Trust in social media platforms reduces skepticism and encourages reliance on heuristic cues such as social proof indicators (likes, shares, endorsements), thereby amplifying susceptibility to persuasive but potentially unreliable financial advice [1,16,31]. Conversely, individuals who exhibit strong verification tendencies actively seek additional information, cross-check facts, and demonstrate more cautious and informed engagement with financial content [13,29]. Financial contents on social media can be broadly categorized into budgeting advice and investment advice, each engaging users differently based on their behavioral traits and cognitive processing styles [36]. Budgeting advice often focuses on short-term financial control and emphasizes structure, simplicity, and immediate applicability, which may particularly appeal to impulsive users seeking quick, actionable solutions [9,16]. In contrast, investment advice typically requires a longer-term perspective, evaluation of risk-return trade-offs, and a greater degree of analytical engagement, resonating more strongly with users who exhibit higher verification tendencies and fact-oriented information processing [27,28]. Based on these arguments, the following hypotheses are proposed: • H1a: Budgeting content preference differs across behavioral clusters. • H1b: Investment content preference differs across behavioral sclusters. • H1c:Impulsiveness, trust, and verification tendencies differed significantly across behavioral clusters. 3.2. Demographic Moderators and Financial Content Evaluation Demographic characteristics such as age, gender, income, and employment status are widely recognized as important determinants of sustainable financial behavior and literacy [11, 25, 34]. Age influences cognitive maturity, financial risk tolerance, and emotional reactivity, with younger individuals often exhibiting higher impulsiveness and susceptibility to emotionally framed content [6]. Gender differences are evident in sustainable financial decision-making, where women typically demonstrate greater risk aversion, higher information verification tendencies, and stronger critical evaluation skills compared to men [11,13]. Income levels impact access to financial resources and education, affecting individuals’ engagement with budgeting versus investment content [19]. Employment status further shapes financial priorities and the perceived relevance of different financial strategies, influencing preferences for immediate budgeting tips versus long-term investment advice [23]. These demographic variables are expected to moderate the strength and direction of the relationships between behavioral traits and financial content preferences. Accordingly, the following hypotheses are proposed: • H2a: Age moderates the relationship between user’s behavioral traits or its clusters and financial content preferences. • H2b: Gender moderates the relationship between user’s behavioral traits or its clusters and financial content preferences. • H2c: Income level moderates the relationship between user’s behavioral traits or its clusters and financial content preferences. • H2d: Employment status moderates the relationship between user’s behavioral traits and its clusters financial content preferences. 4. Research Methodology 4.1. Participants An online survey method was selected to efficiently reach the target population—Indian social media users who actively engage with financial advice content. Given the wide geographical dispersion and digital nature of the target audience, personal survey administration would have been logistically challenging and cost-prohibitive. Regular follow-up emails and gentle reminders were used to encourage survey completion. The study focused on individuals aged 18 years and above who had prior experience engaging with financial advice content—such as budgeting strategies or investment recommendations—on platforms like YouTube, Instagram, and LinkedIn. Eligibility criteria ensured that participants had meaningful exposure to personal finance information online. The sample’s age profile primarily reflects digital natives whose content consumption patterns may be more shaped by heuristics and emotion-rich stimuli than older generations accustomed to traditional advisory modes. A stratified purposive sampling technique was employed to enhance demographic diversity across key variables, including gender, age group, income level, employment status, and education level [ 5 , 12 ]. Purposive sampling allowed for the deliberate selection of respondents based on their relevance to the research objective. Before full-scale data collection, a pilot study involving 50 participants was conducted to validate the survey instrument. Feedback from both academic experts and industry professionals in financial literacy and digital media communication was incorporated to refine question phrasing, sequencing, and survey length. Following the pilot revisions, the final version of the survey was distributed online. In total, 389 responses were received. After screening for incomplete responses and removing participants who did not meet the eligibility criteria (e.g., no prior engagement with financial content), a final dataset of 357 valid responses was retained for analysis. Among the 357 respondents, males constituted 51.82% (n = 185) and females constituted 48.18% (n = 172). In terms of age distribution, 58.82% (n = 210) of the participants were aged between 18 and 25 years, while 41.18% (n = 147) were older than 25 years. Regarding monthly income levels, 27.17% (n = 97) of respondents earned less than INR 2.5 lakhs, 39.22% (n = 140) earned between INR 2.5 lakhs and INR 5 lakhs, 19.05% (n = 68) earned between INR 5 lakhs and INR 7.5 lakhs, 8.40% (n = 30) earned between INR 7.5 lakhs and INR 10 lakhs, and 6.16% (n = 22) reported earning more than INR 10 lakhs annually. In terms of employment status, 55.46% (n = 198) of respondents were students and 44.54% (n = 159) were working professionals. Regarding education level, 50.42% (n = 180) of the participants were undergraduates, 42.02% (n = 150) held postgraduate degrees, and 7.56% (n = 27) possessed doctoral qualifications. Participation in the study was entirely voluntary, and respondents were assured anonymity and confidentiality to minimize social desirability bias. The socio-demographic characteristics of the sample are summarized in Table 1 . Table 1 Socio-Demographic Characteristics of Respondents Characteristics Group Frequency (n) Percentage (%) Gender Male 185 51.82 Female 172 48.18 Age Group 18–25 years 210 58.82 Above 25 years 147 41.18 Annual Income Less than INR 2.5 lakhs 97 27.17 INR 2.5 lakhs–5 lakhs 140 39.22 INR 5 lakhs–7.5 lakhs 68 19.05 INR 7.5 lakhs–10 lakhs 30 8.40 Above INR 10 lakhs 22 6.16 Employment Status Student 198 55.46 Working Professional 159 44.54 Education Level Undergraduate 180 50.42 Postgraduate (Master’s degree) 150 42.02 Doctorate 27 7.56 4.2. Measures The measurement instrument for this study was developed based on prior literature on financial behavior, social media trust, and decision-making biases. All items were adapted from previously validated scales wherever possible and were rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), unless otherwise stated. Impulsiveness was measured using three items adapted from [ 11 ], capturing participants’ tendencies to make spontaneous financial decisions without substantial deliberation. A sample item includes, “I often make financial decisions on the spur of the moment.” Trust in financial advice on social media was assessed through three items based on [ 14 ] with statements such as, “I trust the financial advice shared by influencers on social media.” Verification tendency, reflecting the extent to which users cross-check information before acting upon it, was also measured with three items adapted from [ 14 ], such as “I usually cross-check financial advice from social media before acting on it.” Content preference was evaluated through binary forced-choice selections. Participants were shown two sample posts under each category—budgeting strategies and index fund investment advice, designed to vary in framing (emotional versus factual), structure, and tone. Participants were asked to select the post they found more credible and actionable in each category. To further enrich the findings, open-ended responses were collected immediately after each preference selection, asking participants to explain the rationale behind their choices. These qualitative responses facilitated thematic analysis. The internal consistency of each construct was assessed using Cronbach’s alpha. Impulsiveness achieved a Cronbach’s alpha of 0.78, trust in social media financial content achieved 0.81, and verification tendency achieved 0.75, exceeding the minimum recommended threshold of 0.70 for established reliability [ 24 ]. 4.3. Analytical Approach Data analysis was conducted using SPSS version 26 and Microsoft Excel. Preliminary analyses included descriptive statistics(as shown in Table 2 ) to summarize the sample characteristics and ensure data suitability for further statistical procedures. Reliability analysis was performed to evaluate the internal consistency of the measurement scales. Cronbach’s alpha values for all constructs—Impulsiveness (0.78), Trust in Social Media Financial Content (0.81), and Verification Tendency (0.75)—exceeded the commonly accepted threshold of 0.70, indicating satisfactory internal consistency [ 24 ]. Following the reliability assessment, a K-Means clustering analysis was conducted to segment participants into distinct behavioral groups based on their impulsiveness, trust, and verification tendencies. The K-Means method was selected for its efficiency in handling continuous variables and its suitability for exploratory behavioral segmentation. Several cluster solutions were evaluated, and a three-cluster solution was finalized based on interpretability, cluster sizes, and the reduction in within-cluster variance. After clustering, one-way ANOVA tests were used to examine whether content preferences for budgeting and investment posts significantly differed across the behavioral clusters. Additionally, two-way ANOVA analyses were conducted to explore whether demographic variables (gender, age group, income level, and employment status) moderated the relationship between behavioral cluster membership and content preference. Finally, thematic analysis of the open-ended responses was undertaken following [ 7 ] methodology. An inductive approach was employed to identify recurring patterns in the participants’ explanations for their content choices, allowing for a richer interpretation of the quantitative findings. Table 2 Descriptive Statistics and Reliability of Constructs Construct Number of Items Mean Standard Deviation Cronbach’s Alpha Impulsiveness 3 3.42 0.84 0.78 Trust in Social Media Financial Content 3 3.65 0.79 0.81 Verification Tendency 3 3.29 0.86 0.75 5. Results The results are presented in three major parts. First, the behavioral clustering of participants based on impulsiveness, trust, and verification tendency is reported. Second, the analysis of variance (ANOVA) results testing the proposed hypotheses are presented. Finally, findings from the thematic analysis of open-ended responses are discussed. 5.1. Behavioral Clustering To cluster participants based on behavioral tendencies toward financial content on social media, K-Means clustering analysis was conducted using standardized scores of impulsiveness, trust in financial content, and verification tendency. Several clustering solutions were examined, and a three-cluster solution was selected based on interpretability, cluster size balance, and reduction in within-cluster variance. Cluster 0 (Highly Engaged but Vulnerable Users): Comprising 34.45% of the sample (n = 123), users in this cluster exhibited high levels of self-rated financial literacy and trust in social media financial advice. However, they also reported high impulsiveness and lower tendencies to verify information before acting. These users were highly active consumers of online financial content but were more susceptible to emotionally charged or persuasive messages without critical evaluation. Cluster 1 (Moderately Cautious Users): Representing 38.10% of the participants (n = 136), this cluster showed moderate trust in financial advice shared on social media, moderate impulsiveness, and a moderate verification tendency. Participants in this group demonstrated a balanced approach, engaging with financial content but also exercising some degree of caution and fact-checking. Cluster 2 (Skeptical and Analytical Users): This cluster included 27.45% of the respondents (n = 98) and was characterized by low trust in social media financial advice, low impulsiveness, and high verification tendency. Users in this cluster appeared more critical, analytical, and less emotionally influenced when interacting with online financial advice. Behavioral characteristics across the three clusters in terms of impulsiveness, trust in social media financial content, and verification tendency are illustrated in Fig. 2 . The figure visually highlights the distinct cognitive and emotional patterns that differentiate each user clustering. 5.2. ANOVA and Regression Results To test the proposed hypotheses, a series of one-way ANOVA and multiple regression analyses were conducted. First, the impact of behavioral cluster membership on participants’ preferences for budgeting content was examined. The one-way ANOVA results revealed a statistically significant difference in budgeting post preference across the three behavioral clusters ( F (2, 354) = 4.12, p = 0.019) thus supporting H1a . Participants in Cluster 1 (moderately cautious users) preferred structured budgeting advice (50-30-20 rule) more strongly, while Cluster 0 participants (highly engaged but vulnerable users) showed a greater inclination toward flexible budgeting frameworks (70-20-10 rule). Post hoc comparisons using Tukey’s HSD indicated that the significant differences were primarily between Cluster 0 and Cluster 1, while Cluster 2 (skeptical users) exhibited intermediate preferences. In contrast, the ANOVA conducted to examine investment post preferences across clusters was not statistically significant ( F (2, 354) = 1.28, p = 0.284), indicating that participants across clusters demonstrated relatively uniform preferences towards index fund investment content. Consequently, H1b was not supported . To validate the behavioral segmentation, additional one-way ANOVAs were conducted to assess whether impulsiveness, trust, and verification tendencies differed significantly across clusters. The results confirmed substantial differences in impulsiveness ( F (2, 354) = 45.32, p ¡ 0.001), trust ( F (2, 354) = 58.17, p ¡ 0.001), and verification tendency ( F (2, 354) = 41.89, p ¡ 0.001), supporting H1c . These results indicate that behavioral clusters represent distinct cognitive and emotional profiles. Two-way ANOVA analyses were conducted to examine whether demographic factors moderated the relationship between behavioral clusters and budgeting content preference. Results revealed significant moderation effects for gender ( F (2, 354) = 4.21, p = 0.016) and age group ( F (2, 354) = 3.89, p = 0.024), thereby supporting H2a and H2b . In contrast, no significant moderation effects were observed for income group ( F (2, 354) = 0.80, p = 0.644) or employment status ( F (2, 354) = 1.16, p = 0.330), leading to H2c and H2d not being supported . Multiple regression analyses were further conducted to assess whether behavioral traits predicted content preferences. For budgeting post preference, the regression model was statistically significant ( R 2 = 0.17, F (3, 353) = 24.12, p = 0.001), with trust ( β = 0.34, p = 0.001) and verification tendency ( β = 0.28, p = 0.001) serving as positive predictors, and impulsiveness ( β = -0.19, p = 0.003) serving as a negative predictor. However, the regression model predicting investment post preference was not statistically significant ( R 2 = 0.04, F (3, 353) = 3.91, p = 0.078). In summary, Table 3 provides an overview of the hypothesis testing results. Table 3 Summary of Hypotheses Testing Results Hypothesis Description Supported? H1a Budgeting content preference differs across behavioral clusters Yes H1b Investment content preference differs across behavioral clusters No H1c Impulsiveness, trust, and verification tendencies differed significantly across clusters. Yes H2a Gender moderates budgeting content preference across clusters Yes H2b Age group moderates budgeting content preference across clusters Yes H2c Income group moderates budgeting content preference across clusters No H2d Employment status moderates budgeting content preference across clusters No 5.3. Thematic Analysis To complement the quantitative findings, a thematic analysis was conducted on participants’ open-ended responses explaining their content preferences. Following the six-phase approach by Braun and Clarke [ 7 ], qualitative data were coded inductively, allowing themes to emerge organically based on patterns in user reasoning. This analysis yielded four major themes that influenced participants’ selection of financial content: simplicity and clarity, perceived credibility, visual formatting, and emotional appeal. Theme 1: Simplicity and Clarity - Participants consistently favored content that was easy to understand, concise, and actionable. Posts that employed simple language, direct structure (e.g., lists or percentages), and clear budgeting rules (such as the 50-30-20 method) were perceived as more accessible. Many respondents indicated that they “prefer straightforward frameworks without jargon” or that they “could immediately apply” the budgeting rule. [ 20 ] similarly observed that content with budgeting templates and goal-setting prompts improved financial intentions among university students. Theme 2: Credibility and Expertise - Trustworthiness of the source or the way the content was presented played a key role. Users associated detailed explanations, numerical examples, or factual tone with greater credibility. Participants often mentioned trusting content “that sounds data-driven,” “has real-life context,” or “resembles advice from a certified planner.” This aligns with earlier findings that verification tendencies influence trust levels in sustainable financial decision-making. Theme 3: Visual Presentation and Formatting - Engagement was strongly tied to visual elements, such as use of icons, emojis, structured bullets, or infographics. Several users noted that formatting “made the post easier to scan” and “visually highlighted the important points.” Posts with more polished visuals were perceived as more professional and memorable, especially among younger participants. Theme 4: Emotional Appeal and Relatability - Content that invoked motivation empowerment, or personal storytelling appealed to many users, especially in budgeting contexts. Responses reflected how emotional tone (“encouraging,” “non-judgmental,” “relatable”) helped reduce guilt or fear around money management. One participant noted, “It felt like the post understood my situation instead of lecturing me.” Interestingly, participants in Cluster 0 (high trust and impulsiveness) showed greater sensitivity to emotionally framed and visually rich content, while those in Cluster 2 (skeptical and cautious) prioritized structure, credibility, and verification. This qualitative insight supports the quantitative evidence regarding how behavioral traits shape interpretation and trust in financial advice. The thematic analysis thus reinforces the conclusion that trust in social media financial content is co-constructed through message clarity, perceived source expertise, visual design, and emotional resonance. These dimensions play a crucial role in determining how users internalize financial advice and translate it into behavior. Figure 3 synthesizes the qualitative insights from the thematic analysis by categorizing financial content into four distinct quadrants based on their perceived credibility and emotional appeal. The vertical axis represents the perceived credibility of the content, while the horizontal axis captures its emotional resonance. The upper-left quadrant represents content that combines high credibility with low emotional appeal, such as clear budgeting rules supported by data. This type of content appeals to users who value structure and factual precision, particularly those with high verification tendencies. The upper-right quadrant includes content with both high credibility and high emotional appeal, such as engaging infographics paired with expert insights. This content type appears to be the most universally preferred, as it balances analytical trustworthiness with visual and emotional engagement. The lower-left quadrant characterizes low-credibility, low-emotion content, often consisting of generic financial tips with jargon. Participants described such content as hard to relate to or too abstract to apply. Finally, the lower-right quadrant illustrates content that is emotionally persuasive but lacks factual substance, such as motivational stories with minimal data. This quadrant was particularly appealing to highly impulsive users (Cluster 0) but distrusted by more skeptical users (Cluster 2). This framework provides a useful heuristic for understanding how individuals assess financial advice on social media. It also offers practical design implications for content creators and educators aiming to communicate financial concepts more effectively by calibrating both trustworthiness and emotional resonance. 6. Discussion This study contributes to a deeper understanding of how behavioral traits shape users’ engagement with financial advice on social media, particularly within the Indian context, where digital financial literacy is gaining momentum [ 8 , 37 ]. By employing behavioral clustering based on trust in social media content, impulsiveness, and verification tendencies, we demonstrate that users are not a homogenous audience of financial content but rather exhibit distinct psychological profiles that significantly influence how they interpret and trust online financial advice. These findings build on prior research in behavioral finance, which highlights the role of heuristics and biases in shaping sustainable financial decisions [ 19 , 27 ], and extend them into a digital, user-generated content environment. These results have important implications for B2C brands, particularly fintech startups and budgeting apps, which may tailor communication strategies to specific behavioral segments. One of the central insights from this research is the differentiated effect of behavioral clusters on budgeting versus investment content preferences. Budgeting content elicited significant divergence in user responses across clusters, while investment content—specifically posts about index funds—elicited relatively uniform preferences. This distinction may arise because budgeting advice often involves immediate, emotionally laden trade-offs in everyday life, whereas index fund investments are perceived as low-risk, data-driven, and aligned with conventional long-term planning. Thematically, content that was perceived as simple, credible, and visually structured resonated more with skeptical and cautious users, while emotionally framed or motivational posts were preferred by more impulsive and trusting participants. These insights align with [ 13 ] work, which emphasized that user susceptibility to financial narratives varies with individual gullibility and cognitive styles. By visually organizing content types along two cognitive dimensions—credibility and emotional appeal—this study proposes a typology that classifies posts into four quadrants: clear budgeting rules with data, engaging infographics with expert insights, basic financial tips with jargon, and motivational stories with minimal data. As illustrated in Fig. 3 , the top-right quadrant (high credibility, high emotional appeal) appears to offer the strongest potential for user engagement without compromising informational quality. This echoes earlier work on persuasive messaging in financial contexts, which highlights the importance of both rational and affective cues [ 11 , 30 ]. From a theoretical perspective, these findings offer important contributions to the discourse on financial literacy and misinformation in digital spaces. Rather than focusing solely on source authority, users appear to infer credibility through message-level attributes such as data presentation, formatting, and tone. This suggests that trust is constructed dynamically during content interpretation, a phenomenon consistent with contemporary models of distributed credibility in social media environments [4,29]. Furthermore, the predictive power of verification tendencies and trust levels on budgeting content preference offers empirical support for integrating psychological screening mechanisms into financial education frameworks. Practically, these insights have implications for finfluencers, educational institutions, and social media platforms. Content creators should design advice that balances emotional resonance with informational accuracy, especially when targeting impulsive or less financially literate users. Posts grounded in real data, formatted clearly, and framed in relatable language were found to foster trust while maintaining user engagement—an observation that supports recent regulatory calls for responsible financial communication online [ 32 ]. For educators, these results underscore the importance of equipping users not only with financial knowledge but also with the evaluative tools to detect bias, framing effects, and emotional manipulation. Platforms themselves can play a role in moderating content amplification by incorporating trust-enhancing features such as source verification, transparency cues, and financial content warnings. However, this study is not without limitations. The cross-sectional design restricts the ability to infer causality, and the self-reported nature of behavioral indicators may be influenced by social desirability biases. The sample, though diverse in age and profession, is still limited to digitally active users in India and may not generalize across cultures or offline financial behaviors. Additionally, while the cluster-based segmentation and regression models provide explanatory power, further research could apply longitudinal methods or experimental designs to examine how repeated exposure to content shifts user behavior over time. Incorporating biometric data (e.g., eye-tracking, response latency) or psychophysiological metrics could also deepen understanding of cognitive and emotional processing in sustainable digital financial decision-making. 7. Practical Implications The findings of this study offer actionable insights for educators, content creators, platform designers, and policymakers aiming to improve the quality and impact of financial content shared on social media. For educators and institutions promoting financial literacy, the results highlight the need to go beyond foundational financial knowledge and integrate lessons on cognitive biases, emotional framing, and the persuasive techniques used in digital content. Training programs should emphasize developing critical evaluation skills and promote awareness about how content format and presentation can affect decision-making. For content creators, particularly finfluencers, the study reinforces the importance of presenting financial advice in a manner that is both clear and ethically responsible. Given that users are influenced by perceived credibility, simplicity, and formatting, creators can foster trust by providing transparent, data-driven guidance and avoiding exaggerated claims or emotionally manipulative messaging. For platform designers, the results suggest an opportunity to support users in navigating financial content more effectively. Incorporating credibility markers, visual trust cues, and warnings for unverified content could assist users in distinguishing reliable advice from potentially misleading posts. Algorithms used for content recommendation should also be designed to reduce the amplification of emotionally charged or biased content, especially for users prone to impulsive decision-making. Finally, for policymakers and regulators, the study highlights the urgency of establishing clearer guidelines for financial content dissemination on social media. This includes setting standards for disclosure of sponsorships, promoting transparency in influencer partnerships, and creating mechanisms to monitor and address the spread of financial misinformation. Collaborative efforts between platforms and regulatory bodies can ensure that financial content shared online is accurate and accountable, protecting users from misleading or harmful advice. 8. Limitations and Future Work Although this study offers valuable insights into the relationship between behavioral traits and social media content preferences, it is important to acknowledge several limitations. First, using a cross-sectional survey design restricts the ability to infer causality between behavioral traits and content preferences. Longitudinal studies or experiments that track behavior over time could provide stronger evidence regarding how repeated exposure to financial content affects trust, literacy, and decision-making. Second, while the study examined budgeting and index fund posts, it focused on only two types of financial content. Sustainable financial decision-making on social media spans a broader spectrum, including cryptocurrencies, credit management, and retirement planning. Future studies should explore whether the patterns observed here hold across other financial domains. Third, although clustering provided useful segmentation of participants, the boundaries between behavioral profiles may not be clear-cut. Additional behavioral or psychographic data—such as risk tolerance, emotional regulation, or digital literacy—could enhance the clustering model and offer a more refined understanding of user profiles. Finally, the findings are context-specific to Indian social media users. Cultural factors, financial systems, and regulatory environments vary globally, which may influence how users engage with and interpret financial content. Replicating the study in different geographical and cultural settings would help validate the results and expand their relevance. Future work can also integrate behavioural experiments and eye-tracking tools to investigate how users interact with content in real-time. In addition, developing predictive models that use behavioral data to anticipate content vulnerability could contribute to more targeted educational or regulatory interventions. Future research should also investigate how infrastructural disparities—such as smartphone access, internet bandwidth, or financial literacy programs—affect the design and interpretation of content in developing regions. 9. Conclusion In an era where social media platforms increasingly influence personal financial behavior, this study offers novel insights into how behavioral traits shape individuals’ engagement with financial advice disseminated online. By clustering users based on trust, impulsiveness, and verification tendencies, and analyzing their preferences for budgeting and investment content, the study demonstrates that cognitive and emotional patterns significantly mediate content interpretation. The combination of quantitative clustering, ANOVA, regression analyses, and qualitative thematic exploration reveals that while investment content preferences remain relatively stable across user groups, budgeting content preferences are highly sensitive to users’ behavioral profiles. The study advances theoretical understanding by extending behavioral finance frameworks into digital contexts, highlighting how message design and user psychology interact to influence sustainable financial decision-making [13,19,27]. It further enriches emerging research on digital financial literacy and content trust dynamics by emphasizing the co-construction of credibility through both message features and cognitive biases [4,29,32]. Practically, the findings offer actionable guidance for finfluencers, financial educators, and social media platforms, underscoring the need to balance emotional resonance with informational rigor in financial content design. Despite its contributions, the study acknowledges limitations, including its cross-sectional design, reliance on self-reported measures, and geographic concentration within digitally active Indian users. Future research could adopt longitudinal approaches, experimental designs, or cross-cultural comparisons to deepen the understanding of sustainable digital financial behavior and explore interventions that enhance critical evaluation skills among social media users. Overall, this research contributes to a growing body of literature aiming to promote safer, more informed, and psychologically attuned sustainable financial decision-making in the increasingly complex and persuasive digital media environment. Declarations Author Contributions Statement The author of this manuscript, Dr Ritika Bhatia: Conceptualized the study, designed the methodology, collected and analysed the data, performed the statistical analyses and led the manuscript writing and revisions. Dr Alpa Sethi: Conducted the literature review, assisted in drafting and revisions of the manuscript, collected data and contributed to the interpretation of the results. Ms. Vaidehi Panjwani: Ensured compliance with ethical standards and formatting guidelines. All authors reviewed and approved the final version of the manuscript. Funding This research was not funded by anybody Data Availability The data supporting the findings of this study are available upon reasonable request from the corresponding author. · Ethical approval and accordance The research protocol was reviewed and approved by the MUJ Ethics Committee of Manipal University Jaipur (Approval No. Ethical/BBA/06/2025) in accordance with the ICMR Ethical guidelines. 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M., and J. Rasso. 2024. "If the Tweet Fits: How Investors Process Financial Information Received via Social Media." Journal of Information Systems 38 (2): 1–18. doi: https://doi.org/10.2308/ISYS-2022-044 Subramanian, Y. R. 2021. "Social-Media Influence on the Investment Decisions among the Young Adults in India." Advancement in Management and Technology (AMT) 2 (1): 17–26. doi: https://doi.org/10.46977/apjmt.2021v02i01.003 Tai, S. 2024. "Examining the Market Impact of Social Media Key Opinion Leaders on Investor Sentiment and Financial Market." Advances in Economics, Management and Political Sciences 124 (1): 107–113. Tiwari, C. K., Gopalkrishnan, S., Kaur, D., & Pal, A. (2020). Promoting Financial Literacy through Digital Platforms: A Systematic Review of Literature and Future Research Agenda. Journal of General Management Research, 7(2). Wijaya, Lientang, I. Gede Wahyu Artha Kusuma, and Umu Khouroh.2024. "Peran Influencer dalam Investasi Cryptocurrency: Literasi Keuangan Digital Mediator ataukah Moderator?." Business Management Research 3(2):99-109.doi: https://doi.org/10.26905/bismar.v3i2.14050 Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":22553,"visible":true,"origin":"","legend":"\u003cp\u003eBehavioral Characteristics Across Clusters: Mean Scores for Impulsiveness, Trust in Social Media Financial Content, and Verification Tendency\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7759133/v1/99c6a4eb665444e3589e181c.png"},{"id":96812836,"identity":"5b0efdde-a43a-43c6-adbb-b08895e40cd4","added_by":"auto","created_at":"2025-11-26 10:29:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184923,"visible":true,"origin":"","legend":"\u003cp\u003ePerceived Dimensions of Financial Content Based on Emotional Appeal and 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Introduction","content":"\u003cp\u003eSocial media platforms such as YouTube, Instagram, and Twitter have significantly reshaped the landscape of financial education, especially among younger investors and digitally engaged individuals [9,33]. Central to this evolution is the emergence of financial influencers, popularly known as “finfluencers,” who utilize storytelling, visually appealing content, and simplified explanations to democratize complex financial information for mass audiences [3,16]. The rise of finfluencers has notably enhanced accessibility to financial literacy and participation in financial markets, reducing the barriers traditionally associated with formal financial advisory services and enabling broader audience segments to actively engage in financial planning and investment activities [8,32,36]. This study is situated within the Indian context, where collectivist values, social media credibility dynamics, and rapid digitization intersect to shape unique consumer interpretations of financial advice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile social media platforms have democratized access to financial information, they have also raised serious concerns regarding the accuracy, reliability, and regulation of online financial advice [8,29]. Many finfluencers operate without formal financial credentials, leading to the dissemination of oversimplified, biased, or even misleading guidance [3,16]. Research highlights that persuasive techniques, emotional appeal, and social validation cues (such as likes, shares, and comments) often drive engagement, sometimes at the expense of factual accuracy [1,13,21]. The absence of standardized regulatory frameworks exacerbates the risks, allowing financial misinformation to spread rapidly and influence individuals’\u0026nbsp;sustainable\u0026nbsp;financial practices adversely. These challenges have made it critical to understand how users assess, trust, and act upon financial content presented through social media platforms [35].\u003c/p\u003e\n\u003cp\u003eDespite the growing academic attention on the role of social media in shaping individuals’ financial behavior, significant research gaps remain. First, much of the existing work focuses on the general impact of finfluencers without systematically examining how users’ psychological characteristics—such as trust, impulsiveness, and verification tendencies—interact with the design and framing of financial content [13]. Second, studies often rely heavily on demographic segmentation, overlooking deeper behavioral and cognitive patterns that influence sustainable financial decision-making [11, 27]. Third, prior research tends to treat financial content on social media as homogenous, ignoring potential differences in the framing of financial content and how users perceive distinct types of financial advice, such as budgeting strategies versus investment recommendations [9, 16, 25]. Lastly, there is limited empirical work in the Indian context, despite India’s rapidly growing digital finance ecosystem, creating a need for localized investigations that capture unique cultural and behavioral nuances [34].\u003c/p\u003e\n\u003cp\u003eUnderstanding user engagement with financial advice on social media necessitates drawing upon foundational theories from behavioral finance, psychology, and social cognition. Behavioral finance research emphasizes that financial decision-making is rarely rational and is instead heavily shaped by cognitive heuristics and psychological biases, including confirmation bias, anchoring, mental accounting, and framing effects [27,28]. Kahneman and Tversky’s Prospect Theory further explains how individuals perceive financial gains and losses asymmetrically, often leading to risk-averse or risk-seeking behavior depending on the framing of information [15].\u003c/p\u003e\n\u003cp\u003eOn social media platforms, these cognitive biases are exacerbated by information overload, emotional triggers, and social validation signals such as likes, shares, and comments, which serve as heuristics for credibility [13,22,30]. The theory of information overload suggests that the rapid flow of information can overwhelm users’ cognitive processing abilities, making them more reliant on superficial cues rather than critical evaluation [1,29]. Additionally, social cognitive theory posits that observational learning, social influence, and peer modeling significantly impact behavioral outcomes—factors that are particularly potent in social media environments [2]. Building on the above theoretical perspectives, this study aims to fill important gaps in the current understanding of how psychological characteristics and demographic factors shape user interactions with financial content on social media. Specifically, the research pursues the following objectives:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo identify different behavioral clusters of users’ based on the combinations of their behavioral traits.\u003c/li\u003e\n \u003cli\u003eTo investigate whether behavioral clusters affect users’ preferences for different types of social media financial content.\u003c/li\u003e\n \u003cli\u003eTo explore whether users’ demographic variables, including age, gender, income, and employment status, moderate the relationship between behavioral traits and their preferences for different types of social media financial content.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy integrating behavioral traits and their clusters with social media content preference analysis and demographic moderation, this study offers a nuanced perspective on how users assess and engage with personal finance content in digital environments. The findings contribute to behavioral finance and social media research by highlighting the role of users’ behavioral traits and cognitive biases in influencing financial decision-making. Moreover, the results provide actionable insights for educators, platform designers, finfluencers, and policymakers aiming to foster more responsible and effective financial communication on social platforms, thereby encouraging sustainable financial behavior among individuals.\u003c/p\u003e"},{"header":"2.\tLiterature Review","content":"\u003cp\u003eSocial media platforms have created new channels for financial information. Recently, it has evolved as a popular destination for providing financial advice. In India, research shows that social media is the most preferred source of information for young adults looking to make investment decisions, and many survey respondents chose social media over a financial advisor [9, 34]. The “finfluencers” on YouTube, Instagram, Twitter and other social media palatiform are providing financial tips in a simpler, easily digestible way [3, 10, 16]. Many finfluencers mix financial education with personal anecdotes and simplified investing instructions, targeting users who may have a lower level of financial knowledge. This democratization of financial information has provided a wider audience with exposure to financial education and investment strategies.\u003c/p\u003e\n\u003cp\u003eThis research, however, also suggests that social media financial content is often unvetted, driven by commercial interests, and lacking accountability [10,11,29]. Finfluencers often have no formal qualifications, and while the primary goal of many social media financial content creators may be to help their viewers, they frequently have partnerships and sponsorship deals which color their advice [10,11,29]. For example, one paper suggests that financial misinformation on social media has skewed users’ beliefs about the stock market and pushed them to make decisions based on viral or emotionally charged content [13, 29, 30]. In another study, the lack of quality control and institutional oversight is mentioned as a risk of social media for users; there are no clear disclaimers or evidence to back up advice that is given, whether that is for general budgeting, long-term investing, or day trading [11, 26, 29, 30]. While platforms such as Instagram have introduced new financial content advertising policies in recent years, a lack of transparency on algorithms and platform-level enforcement is a continuing challenge, especially as advertisers use cross-platform approaches to reach users [4, 18]. The result is a largely unregulated marketplace that creates significant risks for users who do not have a background or training in assessing the quality and reliability of digital content.\u003c/p\u003e\n\u003cp\u003eBehavioral finance research has also suggested that financial decisions are influenced not just by the actual merits of a financial product or advice, but also by cognitive biases, heuristics, and emotion. Some of the effects of content design, network effects, and social comparison may be rooted in these psychological decision-making shortcuts, and social media content amplifies many of them [19, 27, 28]. Confirmation bias, for example, leads users to gravitate toward the social media account that affirms their existing beliefs and opinions, while anchoring bias and mental accounting affect how users perceive price points and budget for their finances [27, 28]. Emotional factors also play a role: fear of missing out (FOMO), overconfidence in their own decision-making, or social validation often pushes users to act on information that might be unverified or unvetted, especially when the CTA includes urgent language or validation from one’s peers [13,16]. The constant stimulation of new financial content and topics may also lead to cognitive overload and users resorting to cues like likes, shares, number of posts, formatting, and other simple decision-making heuristics instead of actually critically evaluating financial decisions [22, 30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite growing academic interest in the role of social media in financial decision-making, important gaps remain. First, while previous studies have examined the general impact of finfluencers and content framing, few have explored how users’ behavioral traits—such as trust, impulsiveness, and verification tendencies—interact with content framing to shape sustainable financial decisions [9,16]. Second, existing research has primarily analyzed user behavior through demographic lenses, often overlooking latent psychological patterns that may better explain variations in content interpretation and preference [6,13,27]. Third, most studies treat social media financial content as a homogenous category, without investigating how users respond differently to distinct types of content framing and financial advice (e.g., budgeting vs. investing) [9,25]. Finally, there is limited empirical work on how these behavioral and demographic factors jointly influence engagement patterns, particularly in India. To address these gaps, this study employs behavioral clustering and content preference analysis, adjusted for demographic variables, to evaluate the behaviors of social media users that govern their actions towards financial advice content.\u003c/p\u003e"},{"header":"3. Theoretical Background and Hypotheses Development","content":"\u003cp\u003eThe increasing reliance on social media platforms for financial decision-making has raised critical questions about how individuals or users of social media process and respond to online financial advice. Behavioral finance literature suggests that financial decisions are shaped not purely by rational evaluation, but by cognitive heuristics and psychological biases such as impulsiveness, confirmation bias, and reliance on social proof [27,28]. Social media environments exacerbate these biases through emotionally framed content, peer endorsement signals (likes, shares), and information overload, potentially impairing users\u0026rsquo; critical evaluation capabilities [13, 22,30,33 ]. Prospect Theory further highlights that individuals perceive gains and losses asymmetrically depending on how information is framed, which has direct implications for the design and reception of financial content [15]. At the same time, Social Cognitive Theory emphasizes the role of observational learning and social influence in shaping behavioral outcomes, particularly in environments rich with peer-generated content like social media [2]. These theories collectively suggest that individual differences in behavioral traits\u0026mdash;such as impulsiveness, trust in social media, and verification tendencies\u0026mdash;play a crucial role in determining how financial advice is interpreted and acted upon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, demographic factors such as age, gender, income, and employment status may moderate these relationships, influencing the degree to which behavioral traits impact content preferences. Lastly, the nature of financial advice\u0026mdash;whether related to budgeting or investment\u0026mdash;may itself interact with user traits and cognitive biases to shape content preferences differently. The visual and interactive design of platforms like Instagram or LinkedIn\u0026mdash;whether through short-form reels, carousel posts, or comment sections\u0026mdash;may amplify or suppress the perceived authority and emotional framing of content, requiring platform-specific analysis. Based on these theoretical perspectives, a conceptual framework is proposed to explain the influence of behavioral traits and demographic factors on financial content preferences on social media. The conceptual framework illustrating the proposed relationships among behavioral traits, demographic factors and financial content preferences (budgeting and investment choices) is presented in Fig.1.\u003c/p\u003e\n\u003ch2\u003e3.1. \u003cstrong\u003eBehavioral Traits and Financial Contents\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eBehavioral finance theories emphasize that individual financial decision-making is significantly shaped by cognitive heuristics and psychological biases rather than purely rational analysis [27,28]. In the context of social media, specific behavioral traits such as impulsiveness, trust in social media content, and verification tendency critically influence how users evaluate and act upon financial advice. Impulsiveness, characterized by emotional reactivity and a tendency toward rapid decision-making, increases users\u0026rsquo; likelihood of responding to emotionally framed and persuasive financial posts without critically assessing their credibility [13,22]. Trust in social media platforms reduces skepticism and encourages reliance on heuristic cues such as social proof indicators (likes, shares, endorsements), thereby amplifying susceptibility to persuasive but potentially unreliable financial advice [1,16,31]. Conversely, individuals who exhibit strong verification tendencies actively seek additional information, cross-check facts, and demonstrate more cautious and informed engagement with financial content [13,29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinancial contents on social media can be broadly categorized into budgeting advice and investment advice, each engaging users differently based on their behavioral traits and cognitive processing styles [36]. Budgeting advice often focuses on short-term financial control and emphasizes structure, simplicity, and immediate applicability, which may particularly appeal to impulsive users seeking quick, actionable solutions [9,16]. In contrast, investment advice typically requires a longer-term perspective, evaluation of risk-return trade-offs, and a greater degree of analytical engagement, resonating more strongly with users who exhibit higher verification tendencies and fact-oriented information processing [27,28]. Based on these arguments, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH1a: Budgeting content preference differs across behavioral clusters.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH1b: Investment content preference differs across behavioral sclusters.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH1c:Impulsiveness, trust, and verification tendencies differed significantly across behavioral clusters.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e3.2. Demographic Moderators and Financial Content Evaluation\u003c/h2\u003e\n\u003cp\u003eDemographic characteristics such as age, gender, income, and employment status are widely recognized as important determinants of sustainable financial behavior and literacy [11, 25, 34]. Age influences cognitive maturity, financial risk tolerance, and emotional reactivity, with younger individuals often exhibiting higher impulsiveness and susceptibility to emotionally framed content [6]. Gender differences are evident in sustainable financial decision-making, where women typically demonstrate greater risk aversion, higher information verification tendencies, and stronger critical evaluation skills compared to men [11,13]. Income levels impact access to financial resources and education, affecting individuals\u0026rsquo; engagement with budgeting versus investment content [19]. Employment status further shapes financial priorities and the perceived relevance of different financial strategies, influencing preferences for immediate budgeting tips versus long-term investment advice [23]. These demographic variables are expected to moderate the strength and direction of the relationships between behavioral traits and financial content preferences.\u003c/p\u003e\n\u003cp\u003eAccordingly, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH2a: Age moderates the relationship between user\u0026rsquo;s behavioral traits or its clusters and financial content preferences.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH2b: Gender moderates the relationship between user\u0026rsquo;s behavioral traits or its clusters and financial content preferences.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH2c: Income level moderates the relationship between user\u0026rsquo;s behavioral traits or its clusters and financial content preferences.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cem\u003eH2d: Employment status moderates the relationship between user\u0026rsquo;s behavioral traits and its clusters financial content preferences.\u003c/em\u003e\u003c/p\u003e"},{"header":"4. Research Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Participants\u003c/h2\u003e\u003cp\u003eAn online survey method was selected to efficiently reach the target population\u0026mdash;Indian social media users who actively engage with financial advice content. Given the wide geographical dispersion and digital nature of the target audience, personal survey administration would have been logistically challenging and cost-prohibitive. Regular follow-up emails and gentle reminders were used to encourage survey completion.\u003c/p\u003e\u003cp\u003eThe study focused on individuals aged 18 years and above who had prior experience engaging with financial advice content\u0026mdash;such as budgeting strategies or investment recommendations\u0026mdash;on platforms like YouTube, Instagram, and LinkedIn. Eligibility criteria ensured that participants had meaningful exposure to personal finance information online. The sample\u0026rsquo;s age profile primarily reflects digital natives whose content consumption patterns may be more shaped by heuristics and emotion-rich stimuli than older generations accustomed to traditional advisory modes.\u003c/p\u003e\u003cp\u003eA stratified purposive sampling technique was employed to enhance demographic diversity across key variables, including gender, age group, income level, employment status, and education level [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Purposive sampling allowed for the deliberate selection of respondents based on their relevance to the research objective.\u003c/p\u003e\u003cp\u003eBefore full-scale data collection, a pilot study involving 50 participants was conducted to validate the survey instrument. Feedback from both academic experts and industry professionals in financial literacy and digital media communication was incorporated to refine question phrasing, sequencing, and survey length. Following the pilot revisions, the final version of the survey was distributed online. In total, 389 responses were received. After screening for incomplete responses and removing participants who did not meet the eligibility criteria (e.g., no prior engagement with financial content), a final dataset of 357 valid responses was retained for analysis.\u003c/p\u003e\u003cp\u003eAmong the 357 respondents, males constituted 51.82% (n\u0026thinsp;=\u0026thinsp;185) and females constituted 48.18% (n\u0026thinsp;=\u0026thinsp;172). In terms of age distribution, 58.82% (n\u0026thinsp;=\u0026thinsp;210) of the participants were aged between 18 and 25 years, while 41.18% (n\u0026thinsp;=\u0026thinsp;147) were older than 25 years. Regarding monthly income levels, 27.17% (n\u0026thinsp;=\u0026thinsp;97) of respondents earned less than INR 2.5 lakhs, 39.22% (n\u0026thinsp;=\u0026thinsp;140) earned between INR 2.5 lakhs and INR 5 lakhs, 19.05% (n\u0026thinsp;=\u0026thinsp;68) earned between INR 5 lakhs and INR 7.5 lakhs, 8.40% (n\u0026thinsp;=\u0026thinsp;30) earned between INR 7.5 lakhs and INR 10 lakhs, and 6.16% (n\u0026thinsp;=\u0026thinsp;22) reported earning more than INR 10 lakhs annually. In terms of employment status, 55.46% (n\u0026thinsp;=\u0026thinsp;198) of respondents were students and 44.54% (n\u0026thinsp;=\u0026thinsp;159) were working professionals. Regarding education level, 50.42% (n\u0026thinsp;=\u0026thinsp;180) of the participants were undergraduates, 42.02% (n\u0026thinsp;=\u0026thinsp;150) held postgraduate degrees, and 7.56% (n\u0026thinsp;=\u0026thinsp;27) possessed doctoral qualifications. Participation in the study was entirely voluntary, and respondents were assured anonymity and confidentiality to minimize social desirability bias. The socio-demographic characteristics of the sample are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-Demographic Characteristics of Respondents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than INR 2.5 lakhs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR 2.5 lakhs\u0026ndash;5 lakhs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR 5 lakhs\u0026ndash;7.5 lakhs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR 7.5 lakhs\u0026ndash;10 lakhs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove INR 10 lakhs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorking Professional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUndergraduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePostgraduate (Master\u0026rsquo;s degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoctorate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Measures\u003c/h2\u003e\u003cp\u003eThe measurement instrument for this study was developed based on prior literature on financial behavior, social media trust, and decision-making biases. All items were adapted from previously validated scales wherever possible and were rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), unless otherwise stated. Impulsiveness was measured using three items adapted from [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], capturing participants\u0026rsquo; tendencies to make spontaneous financial decisions without substantial deliberation. A sample item includes, \u0026ldquo;I often make financial decisions on the spur of the moment.\u0026rdquo; Trust in financial advice on social media was assessed through three items based on [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] with statements such as, \u0026ldquo;I trust the financial advice shared by influencers on social media.\u0026rdquo; Verification tendency, reflecting the extent to which users cross-check information before acting upon it, was also measured with three items adapted from [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], such as \u0026ldquo;I usually cross-check financial advice from social media before acting on it.\u0026rdquo;\u003c/p\u003e\u003cp\u003eContent preference was evaluated through binary forced-choice selections. Participants were shown two sample posts under each category\u0026mdash;budgeting strategies and index fund investment advice, designed to vary in framing (emotional versus factual), structure, and tone. Participants were asked to select the post they found more credible and actionable in each category. To further enrich the findings, open-ended responses were collected immediately after each preference selection, asking participants to explain the rationale behind their choices. These qualitative responses facilitated thematic analysis. The internal consistency of each construct was assessed using Cronbach\u0026rsquo;s alpha. Impulsiveness achieved a Cronbach\u0026rsquo;s alpha of 0.78, trust in social media financial content achieved 0.81, and verification tendency achieved 0.75, exceeding the minimum recommended threshold of 0.70 for established reliability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Analytical Approach\u003c/h2\u003e\u003cp\u003eData analysis was conducted using SPSS version 26 and Microsoft Excel. Preliminary analyses included descriptive statistics(as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to summarize the sample characteristics and ensure data suitability for further statistical procedures. Reliability analysis was performed to evaluate the internal consistency of the measurement scales. Cronbach\u0026rsquo;s alpha values for all constructs\u0026mdash;Impulsiveness (0.78), Trust in Social Media Financial Content (0.81), and Verification Tendency (0.75)\u0026mdash;exceeded the commonly accepted threshold of 0.70, indicating satisfactory internal consistency [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFollowing the reliability assessment, a K-Means clustering analysis was conducted to segment participants into distinct behavioral groups based on their impulsiveness, trust, and verification tendencies. The K-Means method was selected for its efficiency in handling continuous variables and its suitability for exploratory behavioral segmentation. Several cluster solutions were evaluated, and a three-cluster solution was finalized based on interpretability, cluster sizes, and the reduction in within-cluster variance.\u003c/p\u003e\u003cp\u003eAfter clustering, one-way ANOVA tests were used to examine whether content preferences for budgeting and investment posts significantly differed across the behavioral clusters. Additionally, two-way ANOVA analyses were conducted to explore whether demographic variables (gender, age group, income level, and employment status) moderated the relationship between behavioral cluster membership and content preference. Finally, thematic analysis of the open-ended responses was undertaken following [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] methodology. An inductive approach was employed to identify recurring patterns in the participants\u0026rsquo; explanations for their content choices, allowing for a richer interpretation of the quantitative findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics and Reliability of Constructs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003cp\u003eDeviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCronbach\u0026rsquo;s\u003c/p\u003e\u003cp\u003eAlpha\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImpulsiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrust in Social Media\u003c/p\u003e\u003cp\u003eFinancial Content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVerification Tendency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Results","content":"\u003cp\u003eThe results are presented in three major parts. First, the behavioral clustering of participants based on impulsiveness, trust, and verification tendency is reported. Second, the analysis of variance (ANOVA) results testing the proposed hypotheses are presented. Finally, findings from the thematic analysis of open-ended responses are discussed.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Behavioral Clustering\u003c/h2\u003e\u003cp\u003eTo cluster participants based on behavioral tendencies toward financial content on social media, K-Means clustering analysis was conducted using standardized scores of impulsiveness, trust in financial content, and verification tendency. Several clustering solutions were examined, and a three-cluster solution was selected based on interpretability, cluster size balance, and reduction in within-cluster variance. Cluster 0 (Highly Engaged but Vulnerable Users): Comprising 34.45% of the sample (n\u0026thinsp;=\u0026thinsp;123), users in this cluster exhibited high levels of self-rated financial literacy and trust in social media financial advice. However, they also reported high impulsiveness and lower tendencies to verify information before acting. These users were highly active consumers of online financial content but were more susceptible to emotionally charged or persuasive messages without critical evaluation. Cluster 1 (Moderately Cautious Users): Representing 38.10% of the participants (n\u0026thinsp;=\u0026thinsp;136), this cluster showed moderate trust in financial advice shared on social media, moderate impulsiveness, and a moderate verification tendency. Participants in this group demonstrated a balanced approach, engaging with financial content but also exercising some degree of caution and fact-checking. Cluster 2 (Skeptical and Analytical Users): This cluster included 27.45% of the respondents (n\u0026thinsp;=\u0026thinsp;98) and was characterized by low trust in social media financial advice, low impulsiveness, and high verification tendency. Users in this cluster appeared more critical, analytical, and less emotionally influenced when interacting with online financial advice. Behavioral characteristics across the three clusters in terms of impulsiveness, trust in social media financial content, and verification tendency are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The figure visually highlights the distinct cognitive and emotional patterns that differentiate each user clustering.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.2. ANOVA and Regression Results\u003c/h2\u003e\u003cp\u003eTo test the proposed hypotheses, a series of one-way ANOVA and multiple regression analyses were conducted. First, the impact of behavioral cluster membership on participants\u0026rsquo; preferences for budgeting content was examined. The one-way ANOVA results revealed a statistically significant difference in budgeting post preference across the three behavioral clusters (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;4.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) thus supporting \u003cem\u003eH1a\u003c/em\u003e. Participants in Cluster 1 (moderately cautious users) preferred structured budgeting advice (50-30-20 rule) more strongly, while Cluster 0 participants (highly engaged but vulnerable users) showed a greater inclination toward flexible budgeting frameworks (70-20-10 rule). Post hoc comparisons using Tukey\u0026rsquo;s HSD indicated that the significant differences were primarily between Cluster 0 and Cluster 1, while Cluster 2 (skeptical users) exhibited intermediate preferences. In contrast, the ANOVA conducted to examine investment post preferences across clusters was not statistically significant (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;1.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.284), indicating that participants across clusters demonstrated relatively uniform preferences towards index fund investment content. Consequently, \u003cem\u003eH1b was not supported\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eTo validate the behavioral segmentation, additional one-way ANOVAs were conducted to assess whether impulsiveness, trust, and verification tendencies differed significantly across clusters. The results confirmed substantial differences in impulsiveness (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;45.32, \u003cem\u003ep\u003c/em\u003e \u0026iexcl; 0.001), trust (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;58.17, \u003cem\u003ep\u003c/em\u003e \u0026iexcl; 0.001), and verification tendency (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;41.89, \u003cem\u003ep\u003c/em\u003e \u0026iexcl; 0.001), supporting \u003cem\u003eH1c\u003c/em\u003e. These results indicate that behavioral clusters represent distinct cognitive and emotional profiles.\u003c/p\u003e\u003cp\u003eTwo-way ANOVA analyses were conducted to examine whether demographic factors moderated the relationship between behavioral clusters and budgeting content preference. Results revealed significant moderation effects for gender (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;4.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and age group (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;3.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), thereby supporting \u003cem\u003eH2a\u003c/em\u003e and \u003cem\u003eH2b\u003c/em\u003e. In contrast, no significant moderation effects were observed for income group (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;0.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.644) or employment status (\u003cem\u003eF\u003c/em\u003e(2, 354)\u0026thinsp;=\u0026thinsp;1.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.330), leading to \u003cem\u003eH2c and H2d not being supported\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eMultiple regression analyses were further conducted to assess whether behavioral traits predicted content preferences. For budgeting post preference, the regression model was statistically significant (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003eF\u003c/em\u003e(3, 353)\u0026thinsp;=\u0026thinsp;24.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), with trust (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and verification tendency (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) serving as positive predictors, and impulsiveness (\u003cem\u003eβ\u003c/em\u003e = -0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) serving as a negative predictor. However, the regression model predicting investment post preference was not statistically significant (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003eF\u003c/em\u003e(3, 353)\u0026thinsp;=\u0026thinsp;3.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.078). In summary, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an overview of the hypothesis testing results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Hypotheses Testing Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSupported?\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBudgeting content preference differs across behavioral clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvestment content preference differs across behavioral clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImpulsiveness, trust, and verification tendencies differed significantly across clusters.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender moderates budgeting content preference across clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge group moderates budgeting content preference across clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncome group moderates budgeting content preference across clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment status moderates budgeting content preference across clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Thematic Analysis\u003c/h2\u003e\u003cp\u003eTo complement the quantitative findings, a thematic analysis was conducted on participants\u0026rsquo; open-ended responses explaining their content preferences. Following the six-phase approach by Braun and Clarke [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], qualitative data were coded inductively, allowing themes to emerge organically based on patterns in user reasoning. This analysis yielded four major themes that influenced participants\u0026rsquo; selection of financial content: simplicity and clarity, perceived credibility, visual formatting, and emotional appeal.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTheme 1: Simplicity and Clarity\u003c/em\u003e - Participants consistently favored content that was easy to understand, concise, and actionable. Posts that employed simple language, direct structure (e.g., lists or percentages), and clear budgeting rules (such as the 50-30-20 method) were perceived as more accessible. Many respondents indicated that they \u0026ldquo;prefer straightforward frameworks without jargon\u0026rdquo; or that they \u0026ldquo;could immediately apply\u0026rdquo; the budgeting rule. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] similarly observed that content with budgeting templates and goal-setting prompts improved financial intentions among university students.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTheme 2: Credibility and Expertise\u003c/em\u003e - Trustworthiness of the source or the way the content was presented played a key role. Users associated detailed explanations, numerical examples, or factual tone with greater credibility. Participants often mentioned trusting content \u0026ldquo;that sounds data-driven,\u0026rdquo; \u0026ldquo;has real-life context,\u0026rdquo; or \u0026ldquo;resembles advice from a certified planner.\u0026rdquo; This aligns with earlier findings that verification tendencies influence trust levels in sustainable financial decision-making.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTheme 3: Visual Presentation and Formatting\u003c/em\u003e - Engagement was strongly tied to visual elements, such as use of icons, emojis, structured bullets, or infographics. Several users noted that formatting \u0026ldquo;made the post easier to scan\u0026rdquo; and \u0026ldquo;visually highlighted the important points.\u0026rdquo; Posts with more polished visuals were perceived as more professional and memorable, especially among younger participants.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTheme 4: Emotional Appeal and Relatability\u003c/em\u003e - Content that invoked motivation empowerment, or personal storytelling appealed to many users, especially in budgeting contexts. Responses reflected how emotional tone (\u0026ldquo;encouraging,\u0026rdquo; \u0026ldquo;non-judgmental,\u0026rdquo; \u0026ldquo;relatable\u0026rdquo;) helped reduce guilt or fear around money management. One participant noted, \u0026ldquo;It felt like the post understood my situation instead of lecturing me.\u0026rdquo;\u003c/p\u003e\u003cp\u003eInterestingly, participants in Cluster 0 (high trust and impulsiveness) showed greater sensitivity to emotionally framed and visually rich content, while those in Cluster 2 (skeptical and cautious) prioritized structure, credibility, and verification. This qualitative insight supports the quantitative evidence regarding how behavioral traits shape interpretation and trust in financial advice. The thematic analysis thus reinforces the conclusion that trust in social media financial content is co-constructed through message clarity, perceived source expertise, visual design, and emotional resonance. These dimensions play a crucial role in determining how users internalize financial advice and translate it into behavior.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e synthesizes the qualitative insights from the thematic analysis by categorizing financial content into four distinct quadrants based on their perceived credibility and emotional appeal. The vertical axis represents the perceived credibility of the content, while the horizontal axis captures its emotional resonance. The upper-left quadrant represents content that combines high credibility with low emotional appeal, such as clear budgeting rules supported by data. This type of content appeals to users who value structure and factual precision, particularly those with high verification tendencies. The upper-right quadrant includes content with both high credibility and high emotional appeal, such as engaging infographics paired with expert insights. This content type appears to be the most universally preferred, as it balances analytical trustworthiness with visual and emotional engagement. The lower-left quadrant characterizes low-credibility, low-emotion content, often consisting of generic financial tips with jargon. Participants described such content as hard to relate to or too abstract to apply. Finally, the lower-right quadrant illustrates content that is emotionally persuasive but lacks factual substance, such as motivational stories with minimal data. This quadrant was particularly appealing to highly impulsive users (Cluster 0) but distrusted by more skeptical users (Cluster 2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis framework provides a useful heuristic for understanding how individuals assess financial advice on social media. It also offers practical design implications for content creators and educators aiming to communicate financial concepts more effectively by calibrating both trustworthiness and emotional resonance.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study contributes to a deeper understanding of how behavioral traits shape users\u0026rsquo; engagement with financial advice on social media, particularly within the Indian context, where digital financial literacy is gaining momentum [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. By employing behavioral clustering based on trust in social media content, impulsiveness, and verification tendencies, we demonstrate that users are not a homogenous audience of financial content but rather exhibit distinct psychological profiles that significantly influence how they interpret and trust online financial advice. These findings build on prior research in behavioral finance, which highlights the role of heuristics and biases in shaping sustainable financial decisions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and extend them into a digital, user-generated content environment. These results have important implications for B2C brands, particularly fintech startups and budgeting apps, which may tailor communication strategies to specific behavioral segments.\u003c/p\u003e\u003cp\u003eOne of the central insights from this research is the differentiated effect of behavioral clusters on budgeting versus investment content preferences. Budgeting content elicited significant divergence in user responses across clusters, while investment content\u0026mdash;specifically posts about index funds\u0026mdash;elicited relatively uniform preferences. This distinction may arise because budgeting advice often involves immediate, emotionally laden trade-offs in everyday life, whereas index fund investments are perceived as low-risk, data-driven, and aligned with conventional long-term planning. Thematically, content that was perceived as simple, credible, and visually structured resonated more with skeptical and cautious users, while emotionally framed or motivational posts were preferred by more impulsive and trusting participants. These insights align with [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] work, which emphasized that user susceptibility to financial narratives varies with individual gullibility and cognitive styles.\u003c/p\u003e\u003cp\u003eBy visually organizing content types along two cognitive dimensions\u0026mdash;credibility and emotional appeal\u0026mdash;this study proposes a typology that classifies posts into four quadrants: clear budgeting rules with data, engaging infographics with expert insights, basic financial tips with jargon, and motivational stories with minimal data. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the top-right quadrant (high credibility, high emotional appeal) appears to offer the strongest potential for user engagement without compromising informational quality. This echoes earlier work on persuasive messaging in financial contexts, which highlights the importance of both rational and affective cues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, these findings offer important contributions to the discourse on financial literacy and misinformation in digital spaces. Rather than focusing solely on source authority, users appear to infer credibility through message-level attributes such as data presentation, formatting, and tone. This suggests that trust is constructed dynamically during content interpretation, a phenomenon consistent with contemporary models of distributed credibility in social media environments [4,29]. Furthermore, the predictive power of verification tendencies and trust levels on budgeting content preference offers empirical support for integrating psychological screening mechanisms into financial education frameworks.\u003c/p\u003e\u003cp\u003ePractically, these insights have implications for finfluencers, educational institutions, and social media platforms. Content creators should design advice that balances emotional resonance with informational accuracy, especially when targeting impulsive or less financially literate users. Posts grounded in real data, formatted clearly, and framed in relatable language were found to foster trust while maintaining user engagement\u0026mdash;an observation that supports recent regulatory calls for responsible financial communication online [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For educators, these results underscore the importance of equipping users not only with financial knowledge but also with the evaluative tools to detect bias, framing effects, and emotional manipulation. Platforms themselves can play a role in moderating content amplification by incorporating trust-enhancing features such as source verification, transparency cues, and financial content warnings.\u003c/p\u003e\u003cp\u003eHowever, this study is not without limitations. The cross-sectional design restricts the ability to infer causality, and the self-reported nature of behavioral indicators may be influenced by social desirability biases. The sample, though diverse in age and profession, is still limited to digitally active users in India and may not generalize across cultures or offline financial behaviors. Additionally, while the cluster-based segmentation and regression models provide explanatory power, further research could apply longitudinal methods or experimental designs to examine how repeated exposure to content shifts user behavior over time. Incorporating biometric data (e.g., eye-tracking, response latency) or psychophysiological metrics could also deepen understanding of cognitive and emotional processing in sustainable digital financial decision-making.\u003c/p\u003e"},{"header":"7. Practical Implications","content":"\u003cp\u003eThe findings of this study offer actionable insights for educators, content creators, platform designers, and policymakers aiming to improve the quality and impact of financial content shared on social media.\u003c/p\u003e\u003cp\u003eFor educators and institutions promoting financial literacy, the results highlight the need to go beyond foundational financial knowledge and integrate lessons on cognitive biases, emotional framing, and the persuasive techniques used in digital content. Training programs should emphasize developing critical evaluation skills and promote awareness about how content format and presentation can affect decision-making.\u003c/p\u003e\u003cp\u003eFor content creators, particularly finfluencers, the study reinforces the importance of presenting financial advice in a manner that is both clear and ethically responsible. Given that users are influenced by perceived credibility, simplicity, and formatting, creators can foster trust by providing transparent, data-driven guidance and avoiding exaggerated claims or emotionally manipulative messaging.\u003c/p\u003e\u003cp\u003eFor platform designers, the results suggest an opportunity to support users in navigating financial content more effectively. Incorporating credibility markers, visual trust cues, and warnings for unverified content could assist users in distinguishing reliable advice from potentially misleading posts. Algorithms used for content recommendation should also be designed to reduce the amplification of emotionally charged or biased content, especially for users prone to impulsive decision-making.\u003c/p\u003e\u003cp\u003e Finally, for policymakers and regulators, the study highlights the urgency of establishing clearer guidelines for financial content dissemination on social media. This includes setting standards for disclosure of sponsorships, promoting transparency in influencer partnerships, and creating mechanisms to monitor and address the spread of financial misinformation. Collaborative efforts between platforms and regulatory bodies can ensure that financial content shared online is accurate and accountable, protecting users from misleading or harmful advice.\u003c/p\u003e"},{"header":"8. Limitations and Future Work","content":"\u003cp\u003eAlthough this study offers valuable insights into the relationship between behavioral traits and social media content preferences, it is important to acknowledge several limitations. First, using a cross-sectional survey design restricts the ability to infer causality between behavioral traits and content preferences. Longitudinal studies or experiments that track behavior over time could provide stronger evidence regarding how repeated exposure to financial content affects trust, literacy, and decision-making.\u003c/p\u003e\u003cp\u003eSecond, while the study examined budgeting and index fund posts, it focused on only two types of financial content. Sustainable financial decision-making on social media spans a broader spectrum, including cryptocurrencies, credit management, and retirement planning. Future studies should explore whether the patterns observed here hold across other financial domains.\u003c/p\u003e\u003cp\u003eThird, although clustering provided useful segmentation of participants, the boundaries between behavioral profiles may not be clear-cut. Additional behavioral or psychographic data\u0026mdash;such as risk tolerance, emotional regulation, or digital literacy\u0026mdash;could enhance the clustering model and offer a more refined understanding of user profiles.\u003c/p\u003e\u003cp\u003eFinally, the findings are context-specific to Indian social media users. Cultural factors, financial systems, and regulatory environments vary globally, which may influence how users engage with and interpret financial content. Replicating the study in different geographical and cultural settings would help validate the results and expand their relevance.\u003c/p\u003e\u003cp\u003eFuture work can also integrate behavioural experiments and eye-tracking tools to investigate how users interact with content in real-time. In addition, developing predictive models that use behavioral data to anticipate content vulnerability could contribute to more targeted educational or regulatory interventions. Future research should also investigate how infrastructural disparities\u0026mdash;such as smartphone access, internet bandwidth, or financial literacy programs\u0026mdash;affect the design and interpretation of content in developing regions.\u003c/p\u003e"},{"header":"9. Conclusion","content":"\u003cp\u003eIn an era where social media platforms increasingly influence personal financial behavior, this study offers novel insights into how behavioral traits shape individuals’ engagement with financial advice disseminated online. By clustering users based on trust, impulsiveness, and verification tendencies, and analyzing their preferences for budgeting and investment content, the study demonstrates that cognitive and emotional patterns significantly mediate content interpretation. The combination of quantitative clustering, ANOVA, regression analyses, and qualitative thematic exploration reveals that while investment content preferences remain relatively stable across user groups, budgeting content preferences are highly sensitive to users’ behavioral profiles.\u003c/p\u003e\n\u003cp\u003eThe study advances theoretical understanding by extending behavioral finance frameworks into digital contexts, highlighting how message design and user psychology interact to influence sustainable financial decision-making [13,19,27]. It further enriches emerging research on digital financial literacy and content trust dynamics by emphasizing the co-construction of credibility through both message features and cognitive biases [4,29,32]. Practically, the findings offer actionable guidance for finfluencers, financial educators, and social media platforms, underscoring the need to balance emotional resonance with informational rigor in financial content design.\u003c/p\u003e\n\u003cp\u003eDespite its contributions, the study acknowledges limitations, including its cross-sectional design, reliance on self-reported measures, and geographic concentration within digitally active Indian users. Future research could adopt longitudinal approaches, experimental designs, or cross-cultural comparisons to deepen the understanding of sustainable digital financial behavior and explore interventions that enhance critical evaluation skills among social media users. Overall, this research contributes to a growing body of literature aiming to promote safer, more informed, and psychologically attuned sustainable financial decision-making in the increasingly complex and persuasive digital media environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author of this manuscript, Dr Ritika Bhatia: Conceptualized the study, designed the methodology, collected and analysed the data, performed the statistical analyses and led the manuscript writing and revisions. Dr Alpa Sethi: Conducted the literature review, assisted in drafting and revisions of the manuscript, collected data and contributed to the interpretation of the results. Ms. Vaidehi Panjwani: Ensured compliance with ethical standards and formatting guidelines. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was not funded by anybody\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eEthical approval and accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was reviewed and approved by the MUJ Ethics Committee of Manipal University Jaipur (Approval No. Ethical/BBA/06/2025) in accordance with the ICMR Ethical guidelines. All participants provided informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhern, K. R., and J. Peress. 2023. \u0026ldquo;The Role of Media in Financial Decision-Making.\u0026rdquo; In \u003cem\u003eHandbook of Financial Decision Making\u003c/em\u003e :192\u0026ndash;212. Cheltenham: Edward Elgar Publishing. doi:https://doi.org/10.4337/9781802204179.00019\u003c/li\u003e\n \u003cli\u003eBandura, Albert. 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. 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R. 2021. \u0026quot;Social-Media Influence on the Investment Decisions among the Young Adults in India.\u0026quot; \u003cem\u003eAdvancement in Management and Technology (AMT)\u003c/em\u003e 2 (1): 17\u0026ndash;26. doi: https://doi.org/10.46977/apjmt.2021v02i01.003\u003c/li\u003e\n \u003cli\u003eTai, S. 2024. \u0026quot;Examining the Market Impact of Social Media Key Opinion Leaders on Investor Sentiment and Financial Market.\u0026quot; \u003cem\u003eAdvances in Economics, Management and Political Sciences\u003c/em\u003e 124 (1): 107\u0026ndash;113.\u003c/li\u003e\n \u003cli\u003eTiwari, C. K., Gopalkrishnan, S., Kaur, D., \u0026amp; Pal, A. (2020). Promoting Financial Literacy through Digital Platforms: A Systematic Review of Literature and Future Research Agenda. Journal of General Management Research, 7(2).\u003c/li\u003e\n \u003cli\u003eWijaya, Lientang, I. Gede Wahyu Artha Kusuma, and Umu Khouroh.2024. \u0026quot;Peran Influencer dalam Investasi Cryptocurrency: Literasi Keuangan Digital Mediator ataukah Moderator?.\u0026quot; \u003cem\u003eBusiness Management Research\u003c/em\u003e 3(2):99-109.doi: https://doi.org/10.26905/bismar.v3i2.14050\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Finfluencer, Social media, Behavioral Traits, Sustainable Finance","lastPublishedDoi":"10.21203/rs.3.rs-7759133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7759133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores how trust, impulsiveness, and verification tendencies shape individuals\u0026rsquo; preferences for budgeting and investment advice on social media. A survey of 357 Indian users, combined with K-means clustering, ANOVA, and thematic analysis, revealed that budgeting preferences vary across behavioral clusters, while investment preferences do not. Verification and trust predicted budgeting content preference, whereas impulsiveness reduced preference for structured advice. Simplicity, credibility, and emotional appeal drove trust. A typology categorizing advice formats was proposed. The study provides practical insights for finfluencers and educators on promoting sustainable financial practices. 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