In the digital age, social media has transformed investment decision-making by providing real-time financial insights, market trends, and investment opportunities. This study explores the profound impact of social media on investor behavior, analyzing its influence on financial markets, decision-making processes, and risk perception. Platforms such as Twitter, Reddit, YouTube, and Instagram have emerged as key sources of financial information, fostering a shift from traditional advisory-based investing to community-driven discussions.
Through a mixed-method research approach, including surveys and statistical analysis, the study examines the role of financial influencers, viral stock trends, and sentiment analysis in shaping investor confidence. Findings indicate that while social media democratizes financial knowledge, it also presents challenges such as misinformation, herd mentality, and speculative investing. The research highlights the need for critical evaluation of online financial content and regulatory measures to safeguard investor interests. By understanding social media’s growing role in investment decisions, this study provides valuable insights for investors, financial institutions, and policymakers in navigating the evolving digital investment landscape.

Excerpt

ABSTRACT

1.INTRODUCTION
1.1 Importance of the Topic
1.2 Need of The Study
1.3 Limitations of The Study
1.4 Scope of The Study
1.5 Objectives of The Study

2. Review of Literature
2.1 Research Gap

3. Research Methodology

4. Data Analysis and Interpretation

5. Findings
5.1 Suggestions
5.2 Conclusion

6. References

ABSTRACT

Through a mixed-method research approach, including surveys and statistical analysis, the study examines the role of financial influencers, viral stock trends, and sentiment analysis in shaping investor confidence. Findings indicate that while social media democratizes financial knowledge, it also presents challenges such as misinformation, herd mentality, and speculative investing. The research highlights the need for critical evaluation of online financial content and regulatory measures to safeguard investor interests. By understanding social media’s growing role in investment decisions, this study provides valuable insights for investors, financial institutions, and policymakers in navigating the evolving digital investment landscape.

KEY WORDS - Social Media, Investor Behaviour, Financial Markets, Sentiment Analysis, Regulatory Measures

Introduction

In the contemporary digital age, social media has revolutionized various aspects of human life, ranging from communication and entertainment to business and finance. Among these domains, the financial sector has witnessed a profound transformation due to the increasing influence of social media on investment decisions. Traditional investment strategies, which once relied on professional financial advisors, institutional reports, and economic indicators, are now significantly shaped by the vast and dynamic world of social media platforms.

Social media channels such as Twitter, Reddit, Facebook, YouTube, Instagram, TikTok, and LinkedIn have emerged as critical sources of financial information, providing investors with real-time updates, market analysis, and trading strategies. The rise of financial influencers, investment forums, and community-driven trading discussions has redefined how individuals, especially retail investors, perceive and respond to investment opportunities. The accessibility of financial knowledge through these platforms has led to a democratization of investment decision-making, enabling even novice investors to participate actively in stock markets, cryptocurrencies, and other financial instruments.

One of the most notable impacts of social media on investment decisions is the rapid dissemination of information. Market trends, stock recommendations, and breaking financial news spread instantaneously, influencing investor sentiment and market movements in unprecedented ways. The viral nature of social media amplifies both positive and negative market sentiments, often leading to market volatility. For instance, platforms like Twitter have been instrumental in driving stock prices through trending hashtags, while Reddit’s r/WallStreetBets has demonstrated the power of collective retail investor action, as seen in the GameStop stock surge in early 2021.

Furthermore, social media has fostered the rise of influencer-driven investments, where prominent financial analysts, entrepreneurs, and even celebrities shape investment trends. Influencers such as Elon Musk, whose tweets have significantly impacted the prices of cryptocurrencies like Bitcoin and Dogecoin, highlight the immense power of social media in swaying investor behavior. Additionally, YouTube channels and podcasts dedicated to financial education have become major sources of investment advice, often rivaling traditional financial news outlets.

While social media provides numerous benefits in terms of financial literacy and investment opportunities, it also presents challenges and risks. Misinformation, market manipulation, and herd mentality are prevalent concerns associated with investment decisions influenced by social media. Investors often fall prey to unverified financial claims, pump-and-dump schemes, and speculative trading driven by online hype rather than fundamental analysis. Consequently, regulatory bodies and financial institutions have increasingly scrutinized the role of social media in investment activities to prevent fraudulent practices and protect investors from potential financial losses.

The growing interdependence between social media and investment decisions underscores the need for a balanced approach to leveraging online financial information. While the democratization of investment knowledge has empowered a broader audience, investors must exercise caution, critical thinking, and due diligence before making financial commitments based on social media trends. Moreover, financial regulators and policymakers must address the emerging challenges posed by social media-driven investments to ensure market stability and investor protection.

Additionally, the evolution of artificial intelligence and machine learning has further shaped the intersection of social media and investment decisions. Algorithmic trading systems and sentiment analysis tools utilize data from social media platforms to predict market trends, offering institutional and retail investors new avenues for making informed investment choices. However, this also raises ethical concerns about data privacy, information asymmetry, and the potential for market manipulation through automated strategies.

Another crucial aspect of social media’s impact on investment decisions is its influence on behavioral finance. Investors often experience cognitive biases, such as confirmation bias and overconfidence, when engaging with financial content on social media. The echo chamber effect, where investors are exposed predominantly to opinions that align with their existing beliefs, can lead to irrational investment decisions and increased market speculation. Understanding these psychological factors is essential for investors aiming to make objective and rational investment choices.

Moreover, the role of government regulations and corporate policies in curbing misleading financial content on social media cannot be overlooked. Regulatory bodies such as the U.S. Securities and Exchange Commission (SEC) and the Financial Conduct Authority (FCA) have introduced guidelines to monitor misleading financial advice, fraudulent investment schemes, and insider trading activities on digital platforms. The increasing collaboration between social media companies and financial regulators reflects a proactive approach to mitigating the risks associated with online investment discussions.

Social media has also played a significant role in driving financial market trends, as seen in the rise of meme stocks, crowdfunding campaigns, and decentralized finance (DeFi) initiatives. Platforms like TikTok and Instagram have become unexpected yet powerful players in financial education, with short-form video content offering quick investment tips, financial planning strategies, and cryptocurrency insights. The influence of social media extends beyond stock markets, impacting alternative investments such as NFTs (Non-Fungible Tokens), real estate crowdfunding, and peer-to-peer lending.

Moreover, the real-time nature of social media has led to the emergence of flash trading and sentiment-driven investment strategies. Traders and investors react almost instantly to news, tweets, or viral posts, causing rapid fluctuations in stock and cryptocurrency markets. This phenomenon has given rise to both opportunities and risks, as traders must navigate the volatility caused by unpredictable online movements. The increasing reliance on social media for investment decisions highlights the need for investors to develop resilience against market noise and the pressure of making impulsive decisions based on trending topics.

Another key consideration is the role of data analytics in measuring the impact of social media on investments. Financial institutions and hedge funds are increasingly leveraging big data and AI-driven sentiment analysis tools to track social media trends, assess investor sentiment, and forecast market movements. This approach has transformed traditional investment research methodologies, incorporating online discussions, search engine queries, and even social media engagement metrics into financial models.

Furthermore, the global nature of social media enables cross-border investment discussions, fostering financial inclusion and awareness on a massive scale. Investors from different countries can exchange insights, discuss global economic developments, and analyze investment opportunities in emerging markets. However, this also introduces risks related to information accuracy, regulatory discrepancies, and market speculation across international financial systems.

The psychological and emotional impact of social media-driven investment trends also deserves attention. The fear of missing out (FOMO) has become a major driver of impulsive investing, as individuals chase after rapidly rising stocks or cryptocurrencies based on social media hype. At the same time, panic selling due to negative news or trending discussions can lead to unnecessary financial losses. This behavioral aspect of investing, intensified by social media, highlights the need for financial education and self-discipline among investors.

Finally, as social media platforms evolve, their role in investment decision-making will continue to grow. Future trends may include deeper integration of blockchain technology for transparent financial transactions, social trading platforms where investors can directly replicate expert portfolios, and AI-powered investment assistants offering real-time market insights. Understanding these evolving dynamics is essential for investors, policymakers, and financial institutions aiming to adapt to the ever-changing digital investment landscape.

In conclusion, the impact of social media on investment decisions is undeniable, shaping modern financial landscapes in both positive and challenging ways. As social media continues to evolve, its role in influencing investor behavior and market dynamics will likely expand further, necessitating a more informed and strategic approach to online financial engagement. This paper will explore various dimensions of this impact, including the benefits, risks, regulatory considerations, technological advancements, behavioral finance, global financial implications, psychological factors, and future trends. Understanding the complex interplay between social media and investments is crucial for both individual investors and financial institutions seeking to navigate this rapidly evolving digital landscape.

1.1 Importance of the Topic

Social media has become an indispensable tool in the modern financial landscape, influencing how investors gather information, analyze market trends, and make investment decisions. With millions of users engaging in financial discussions across platforms like Twitter, Reddit, YouTube, and TikTok, the accessibility of financial knowledge has expanded significantly. Unlike traditional investment methods that required reliance on professional analysts or institutional reports, social media provides real-time updates, democratizing access to financial markets. This shift has particularly empowered retail investors, allowing them to participate in investment activities alongside institutional investors.

One of the key benefits of social media in investment decisions is the speed at which information is disseminated. Market-moving news, earnings reports, and geopolitical developments are instantly shared, enabling investors to react quickly. This real-time nature of social media allows investors to stay ahead of market trends, reducing the dependency on conventional media outlets. For example, tweets from influential figures such as Elon Musk have caused significant price fluctuations in stocks and cryptocurrencies, highlighting the power of social media in shaping financial markets.

Furthermore, social media fosters financial education and knowledge-sharing, allowing individuals to learn from experts, analysts, and fellow investors. Platforms like YouTube and LinkedIn provide educational content on investment strategies, risk management, and financial planning, making investing more accessible to a broader audience. Financial influencers, known as “finfluencers,” help bridge the gap between complex financial concepts and everyday investors, encouraging financial literacy and informed decision-making.

However, social media’s influence on investment decisions is not without risks. The spread of misinformation, market manipulation, and speculative trading are significant concerns. Many investors fall victim to pump-and-dump schemes, where certain stocks or cryptocurrencies are hyped up online to artificially inflate prices before a sudden crash. The herd mentality, driven by viral trends and online communities, can lead to irrational investment decisions, causing financial losses for uninformed investors. Therefore, while social media is a powerful tool, it requires critical thinking and due diligence from investors.

Regulatory bodies and financial institutions have recognized the growing impact of social media on investment markets and are increasingly implementing measures to monitor and control misleading financial content. Governments and financial regulators are working with social media platforms to detect and remove fraudulent schemes, ensuring that investors receive accurate and transparent financial information. The introduction of stricter guidelines on online financial advertising and influencer marketing further reflects the need for regulatory oversight in this evolving digital investment landscape.

The integration of artificial intelligence and big data analytics with social media has also enhanced investment strategies. Many hedge funds and institutional investors use AI-driven sentiment analysis tools to track market trends based on social media discussions. These tools provide valuable insights into investor sentiment, helping traders make more informed investment choices. This technological advancement has transformed investment strategies, shifting from traditional fundamental analysis to a more data-driven, real-time approach.

In conclusion, the impact of social media on investment decisions is a double-edged sword, offering both opportunities and challenges. While it has democratized access to financial knowledge, improved market responsiveness, and fostered financial inclusion, it also introduces risks related to misinformation and market volatility. Investors must strike a balance between leveraging social media as a valuable resource and applying critical analysis before making financial decisions. As social media continues to evolve, its influence on the financial markets will only grow, making it essential for investors, financial regulators, and institutions to adapt to this dynamic investment environment.

1.2 Need for the Study

The increasing role of social media in investment decision-making has necessitated an in-depth analysis of its effects on financial markets and investor behavior. In the past, investment decisions were primarily influenced by financial advisors, institutional reports, and traditional media. However, the rise of digital platforms like Twitter, Reddit, YouTube, and TikTok has drastically changed how investors receive and process financial information. This study is essential to understand the opportunities and risks associated with social media-driven investments, particularly in an era where retail investors have unprecedented access to financial markets.

One of the primary reasons for conducting this study is to assess how social media has democratized financial knowledge. Previously, investing was often seen as a domain dominated by professionals, but today, millions of retail investors rely on social media for insights, stock recommendations, and market trends. By analyzing this shift, the study can provide insights into how social media platforms empower individual investors and whether this has led to better financial literacy and informed decision-making.

Additionally, social media’s ability to spread financial information at an unprecedented speed has significantly impacted market dynamics. The rise of meme stocks, cryptocurrency trends, and viral investment discussions have led to increased market volatility. This study is necessary to examine the extent to which social media-driven trading affects market stability, liquidity, and the potential for speculative bubbles. Understanding these effects can help policymakers and financial regulators develop strategies to mitigate risks while preserving the benefits of social media as a financial tool.

Another critical aspect of this study is to explore the role of misinformation and market manipulation in social media-driven investments. While social media provides easy access to financial knowledge, it is also a breeding ground for unverified claims, misleading investment advice, and pump-and-dump schemes. Many retail investors, especially those with limited financial experience, may make investment decisions based on hype rather than fundamental analysis. This study will examine the prevalence of misinformation and the need for regulatory oversight to protect investors from financial fraud.

The integration of artificial intelligence and big data analytics with social media has further transformed investment decision-making. Many institutional investors and hedge funds now use sentiment analysis tools to track online discussions and predict market trends. This study is necessary to explore how AI-driven investment strategies leverage social media data, their implications for market efficiency, and the ethical considerations surrounding the use of technology in financial decision-making.

Understanding the behavioral finance aspect of social media-driven investments is another crucial reason for this study. Investor psychology, including cognitive biases like fear of missing out (FOMO), herd mentality, and overconfidence, plays a significant role in financial decision-making. Social media platforms amplify these psychological tendencies, influencing how investors react to market trends. This study aims to analyze how social media affects investor sentiment and whether it leads to rational or irrational investment behaviors.

Lastly, this study is needed to explore the future implications of social media in finance and investment markets. As technology evolves, social media’s role in financial decision-making is expected to grow even further. With the rise of decentralized finance (DeFi), blockchain-based investments, and algorithmic trading strategies, understanding the long-term impact of social media on financial markets is crucial. This study will provide insights into emerging trends and help investors, regulators, and financial institutions prepare for the future of digital investment landscapes.

1.3 LIMITATIONS OF THE STUDY

· Limited Sample Representation – The study may not fully represent all investor groups, especially institutional investors or those who do not actively use social media.
· Respondents may not fully engage with surveys or questionnaires, leading to incomplete or inaccurate responses.
· Limited time for data collection, analysis, and review, impacting the thoroughness of the study.
· Rapidly changing trends and viral content make it difficult to capture consistent patterns.
· Social media users may not represent the entire investor population, leading to biased results.

1.4 SCOPE OF THE STUDY:

1. Examines how individual and institutional investors use social media for investment decisions.
2. Focuses on platforms like Twitter, Reddit, YouTube, LinkedIn, and financial discussion forums.
3. Studies how age, experience level, and financial knowledge affect investment decisions influenced by social media.
4. The study may struggle to differentiate between short-term reactions to social media trends and long-term investment strategies.
5. Assesses the role of positive and negative sentiments on social media in shaping market trends and investor confidence.
6. Specifies whether the study focuses on short-term, medium-term, or long-term investment decision-making patterns.

1.5 OBJECTIVES OF THE STUDY

1. To analyze how businesses integrate real-time social media insights into strategic decision-making and its impact on organizational performance.
2. To develop robust models that integrate behavioral finance and machine learning for improving the accuracy of investment and financial decision-making predictions.
3. To examine how different industries can leverage social media for sustainability-driven innovations and their effects on business growth.
4. To assess the moderating effects of cultural differences, digital trust, and platform-specific engagement on influencer marketing and brand equity.
5. To explore the role of social media analytics in enhancing content marketing and consumer engagement strategies across various sectors.

2. REVIEW OF LITERATURE

1. (Nugroho & Angela, 2024) This study explores how social media analytics influences strategic decision-making in SMEs, focusing on the roles of organizational innovation and adaptability. By utilizing Structural Equation Modeling (SEM) with Smart PLS 4.1, data from 200 SMEs were analyzed. The results reveal that social media analytics enhances innovation and adaptability, leading to better strategic decisions. This underscores the importance of social media as a key strategic resource in an evolving business landscape. The study contributes to existing research on social media analytics and SME management. Practically, it advises SMEs to adopt social media analytics to boost adaptability and innovation.

2.(Esposito et al., 2024) This study examines how European agri-food companies utilized Twitter to engage stakeholders in circular economy discussions during the COVID-19 pandemic. By applying a coding framework based on the 4-R paradigm (reduce, reuse, recycle, and recover), the research analyzed corporate tweets to understand engagement strategies. The results show that companies mainly focused on recycling and general circular economy topics, with most posts being informative rather than interactive. Social media played a key role in raising awareness and driving discussions on circular economy transitions. The COVID-19 crisis further highlighted the significance of circular economy practices in the agri-food sector. This study is the first to explore corporate social media engagement in circular economy initiatives within the industry.

3.(Chidiogo Uzoamaka Akpuokwe et al., 2024)Social media plays a crucial role in advancing gender equality by empowering women entrepreneurs with access to resources, networks, and markets. It enables them to overcome traditional business barriers, connect with mentors, and expand their professional circles. Beyond individual empowerment, social media fosters collaboration, advocacy, and awareness of gender biases, driving systemic change in entrepreneurship. It also provides women with greater access to funding and investment opportunities, breaking long-standing financial barriers. However, challenges such as online harassment, digital skill gaps, and the digital divide still exist. Addressing these issues requires collective efforts from governments, the private sector, and civil society to ensure social media remains a catalyst for gender equality.

4.(Fattah AL-AZZAM & Al-mizeed, 2021)This study explores how digital marketing influences purchasing decisions in Jordan, particularly across various online platforms and product categories. Through a simple sampling technique, 300 questionnaires were distributed, with a 73% response rate. Data analysis methods, including descriptive statistics and multiple regressions, revealed that social media and mobile marketing significantly impact consumer behavior, especially among students. Different digital platforms shape student purchasing decisions, emphasizing the importance of digital strategies for businesses. The findings suggest that companies should leverage digital marketing to enhance brand awareness and competitiveness. Adopting these strategies can help businesses thrive in today’s digital-driven market.

5.(Singh et al., 2024)This study examines the factors influencing investors' risk perception toward equity shares and explores their interconnections using Social Network Analysis (SNA). Through a literature review, key factors were identified, and the Delphi technique was used to assess their associations. The findings highlight culture and education as the most critical factors affecting risk perception, while investor capacity, framing effect, and loss aversion have minimal impact. The study provides valuable insights to help investors make informed equity investment decisions. Additionally, it opens avenues for future research by identifying areas needing further exploration. These findings contribute to a deeper understanding of risk perception in equity investments.

6.(Tumasjan, 2024)This study explores the role of social media in business and economics research over the past 15 years by analyzing 1,419 articles from top journals published between 2008 and 2022. It identifies seven key themes, including social media as a market and resource-oriented interaction hub, an information market, and a platform for innovation and business ventures. Additionally, it highlights social media’s influence as a societal challenge, a political tool, and a valuable data source. The study emphasizes the diverse functions of social media in shaping economic and business activities. It also underscores the growing significance of social media in these domains. Finally, the research proposes a future agenda to further explore its evolving impact.

7.(Almestarihi et al., 2024)In today's competitive digital landscape, businesses invest heavily in paid advertising to boost their online presence and drive revenue. This study explores the impact of these campaigns on profitability, analyzing data from various industries to assess their effectiveness. Measuring ROI is complex, requiring careful consideration of ad spend, conversion rates, and customer lifetime value. Accurate tracking and attribution of conversions are essential to determine the true success of advertising efforts. While paid advertising can be expensive, a strategic, data-driven approach can yield substantial financial benefits. The study provides valuable insights for marketers and business leaders looking to refine their advertising strategies.

8.(Junaidi et al., 2024)This study examines how public administration, social media education, and population literacy awareness influence public satisfaction in population services. Using a quantitative approach with Smart PLS for data analysis, the findings reveal that public administration plays a crucial role in shaping population literacy. While social media enhances literacy awareness, its direct mediating effect on public satisfaction remains uncertain. The study emphasizes the need for digital platforms and effective communication strategies to foster an informed and engaged citizenry. Governments should implement targeted education and communication initiatives to enhance public understanding and satisfaction.

9. (S. Khan et al., 2023)This study explores the impact of digital influencers (DIs) on consumer purchase intentions using a knowledge-based system (KBS) and the fuzzy analytic hierarchy process (AHP). It highlights how DIs significantly shape consumer choices, particularly in the organic skincare industry. The proposed KBS helps marketing managers assess influencer effectiveness and key decision-making factors. As the first study to apply fuzzy AHP and KBS in this context, it offers a comprehensive framework for understanding the relationship between influencer strategies and consumer decisions.

10.(Seyyedamiri & Tajrobehkar, 2021)This study examines how social content marketing influences new product development in high-tech companies, considering e-trust as a potential mediator. Analyzing data from 384 industry professionals using structural equation modeling, the findings reveal that while social content marketing and e-trust enhance product development, e-trust does not mediate their relationship. High-tech companies can leverage social content marketing and user-generated content to foster idea generation and minimize commercialization risks. This strategy helps boost market share, revenue, and overall product success. The study provides valuable insights for managers to refine their marketing and development approaches.

11.(Rani S & Prerana.M, 2021)In today’s digital age, media plays a significant role in shaping our decisions about everything from clothing and gadgets to entertainment and financial investments. With the rise of digital platforms like YouTube, Instagram, Facebook, and Twitter, people increasingly turn to the internet to validate their choices, often relying on social media for advice or information. While this dependency can offer some benefits, it also comes with its own set of drawbacks. Social media platforms act as not only tools for communication but also sources of both informative and entertainment content. This article explores how the informational content shared on social media influences financial investment decisions. Many content creators on platforms like YouTube and Instagram share insights on various investment opportunities, teaching their followers about options they might not have previously considered. Additionally, some creators market specific investments, attempting to persuade their audience to consider certain tactics or choices. For example, Elon Musk’s tweet expressing support for the cryptocurrency Dogecoin caused a massive surge in investment, with its value increasing by 8% overnight. This demonstrates the significant impact social media can have on financial decisions .Recognizing the strong influence of social media on young adults, this study focuses on understanding how these platforms affect investment choices among this demographic in India. The findings aim to shed light on the power of digital media in shaping.financial behaviours and decisions in today’s connected world.

12. (Majeed et al., 2021)social media plays a crucial role in shaping consumer purchase intention, particularly through its impact on brand equity within Ghana’s fashion industry. The study employed a quantitative survey approach, gathering data from 500 fashion customers, which was analyzed using SPSS and SEM techniques in AMOS 22.0. The findings revealed that surveillance, information sharing, and remuneration positively influence brand equity, whereas social interaction and entertainment have a negative and insignificant effect. Furthermore, the study confirmed a strong positive relationship between brand equity and consumer purchase intention. By addressing the limited empirical research on this topic, the study provides valuable insights into how social media influences brand equity and purchase behaviour. The research also offers recommendations for clothing industry managers, policymakers, and future scholars seeking to explore this area further.

13. (Hasselgren et al., 2023)In recent years, the use of sentiment data from social media (SM) to guide investment decisions has become a more viable concept, thanks to significant research in this area over the past decade. Many studies have yielded promising results, but there is still much room for advancement, especially in terms of how to obtain relevant sentiment data from social media, accurately measure it, and visualize it in a way that aids investors in their decision-making process. A common shortcoming in existing work is the failure to integrate social media metrics effectively into the sentiment scores used for analysis. To address this gap, the paper introduces a novel prototype that aims to improve the way social media sentiment is measured and utilized. The research begins with a comprehensive literature and technical review to lay the foundation for the new approach. It then presents an innovative method for incorporating social media metrics into the sentiment scoring system. This approach is tested using popular S&P500 stocks, which ensures access to large volumes of sentiment data. Finally, the paper evaluates the results, discussing the insights gathered from this new method and its potential impact on investment decision-making.

14. (Kurdi et al., 2022)social media influencers have emerged as a powerful marketing tool, significantly shaping consumer attitudes and intentions. Their study investigates the impact of various influencer characteristics on consumer behaviour while also assessing the role of vloggers as a moderating factor in digital marketing. To achieve this, the researchers employed a quantitative approach, collecting data from TikTok users, a platform that has gained worldwide popularity for short video content. The analysis, conducted using the PLS-SEM method, confirmed the influence of most hypothesized relationships, except for source relatability on consumer attitude and the moderating role of vloggers on consumer intention. The findings align with existing literature while addressing a research gap by integrating new variables into a comprehensive framework. Ultimately, this research provides valuable insights into influencer marketing strategies, reinforcing their effectiveness in engaging digital consumers

15. (Riefel, n.d.) In today’s world, social media has become a crucial communication tool and an important source of information, including in the financial sector, where it significantly influences investment decisions. According to behavioural finance, an individual's personality traits can affect how social media exposure influences their investment choices. However, prior research has not focused on Dutch investors or examined the moderating effects of factors such as tie strength—the degree of connectedness between people on social media—and the role of financial influencers, individuals who specialize in sharing financial content. This study aimed to explore the impact of social media on investment decisions among Dutch investors, with a particular focus on these two moderating variables. Data was gathered from 142 participants through a questionnaire that assessed their perceptions of how social media exposure affected their investment behaviour. The findings revealed that social media exposure significantly impacted both the likelihood of investors deciding to invest and the amount of money they were willing to allocate to investments. Interestingly, respondents believed that others were more influenced by social media than themselves, a phenomenon known as the third-person effect. Overall, this study offers valuable insights for both investors and financial advisors, helping them better understand the role social media plays in shaping investment behaviour and decision-making.

16. (W. Khan et al., n.d.)Predicting the stock market is difficult due to unpredictable factors like social media and financial news. Researchers applied machine learning and deep learning to analyze their influence on prediction accuracy. By refining data through feature selection and filtering spam tweets, they improved accuracy, with social media predictions reaching 80.53% and financial news predictions at 75.16%. Some stocks, like New York and Red Hat, proved harder to predict, while IBM and Microsoft showed greater sensitivity to external data. Among various models, the random forest classifier emerged as the most reliable, achieving an ensemble accuracy of 83.22%.

2.1 RESEARCH GAP

Despite extensive research on social media's role in business, marketing, investment, and public engagement, several research gaps remain. While studies have explored social media analytics in SMEs and digital marketing's influence on consumer behavior, there is limited research on how businesses integrate real-time social media insights into strategic decision-making. Additionally, although social media’s role in gender equality and public administration has been examined, there is a need for more empirical research on its long-term impact on socio-economic development. In investment and financial decision-making, research highlights the influence of sentiment analysis and influencer marketing but lacks robust models integrating behavioral finance and machine learning for accurate predictions. Moreover, while studies examine content marketing and engagement strategies in high-tech firms and the agri-food sector, research is sparse on how different industries can leverage social media for sustainability-driven innovations. Lastly, despite growing interest in influencer marketing and brand equity, the moderating effects of cultural differences, digital trust, and platform-specific engagement remain underexplored. Addressing these gaps could provide a more comprehensive understanding of social media’s evolving impact across business, finance, and societal domains.

3. RESEARCH METHDOLOGY

This study employs a mixed-methods research approach, integrating both quantitative and qualitative methods to examine the impact of social media on investment decisions. The quantitative aspect involves structured surveys distributed via Google Forms to individual and institutional investors, gathering numerical data on their social media usage and investment behaviors. Statistical techniques such as descriptive analysis, correlation, and regression analysis are utilized to assess the relationship between social media engagement and investment decision-making. The qualitative aspect focuses on content analysis and individual opinions to gain deeper insights into investors' perceptions, motivations, and experiences with social media platforms like Twitter, Reddit, and YouTube. Primary data is the primary source, collected directly from respondents to ensure authenticity, while secondary data is used to analyze past research and identify gaps. The study uses a simple random sampling method, targeting men and women aged 18 to 60, and also considers cluster sampling based on geographic location and platform preference. A total of 156 respondents participated in the study. Statistical tools such as the Chi-Square Test, Kruskal-Wallis Test, and Box-Cox Transformation are employed for data analysis. The Chi-Square Test determines relationships between categorical variables, while the Kruskal-Wallis Test is used to compare medians across independent groups. The Box-Cox Transformation stabilizes variances and ensures data normality for parametric tests. The study tests multiple hypotheses related to social media’s impact on organizational performance, investment accuracy, sustainability, influencer marketing, and consumer engagement. Each hypothesis examines whether social media insights and strategies significantly influence financial and business decisions. The research spans four months, from December 2024 to March 2025, during which data collection and analysis are conducted to achieve the study’s objectives.

4. DATA ANALYSIS AND INTERPRETATION

Area of living

TABLE:4.1

Illustrations are not included in the reading sample

Source – data compiled and analyzed by the authors

FIGURE :4.1

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table categorizes respondents based on their area of living into urban, semi-urban, and rural groups, showing the percentage of responses from each. A significant majority , 67.9%, reside in urban areas, indicating that most respondents are from cities or metropolitan regions. Urban areas generally offer better infrastructure, job opportunities, education, and access to financial services, which could explain the higher participation from this group. The dominance of urban respondents suggests that the survey was primarily conducted in well-developed regions, where individuals are more engaged with financial and economic activities.

In contrast, 16.7% of the respondents come from semi-urban areas, which serve as transitional zones between cities and rural regions. These areas have moderate access to infrastructure, education, and financial services, but they still lag behind urban centers. The lower percentage of semi-urban respondents could indicate limited engagement with the survey, possibly due to lower internet access, financial literacy, or interest in the subject matter.

The smallest proportion of respondents , 15.4%, belong to rural areas, highlighting a lower representation of individuals from these regions. Rural areas often face challenges such as lack of infrastructure, financial exclusion, and limited access to digital technology, which may have contributed to their lower participation in the survey. The data suggests that people in rural regions are less involved in financial and economic discussions, emphasizing the need for better financial inclusion and awareness programs to bridge this gap.

Overall, the data reveals that urban respondents significantly outnumber those from semi-urban and rural areas, with more than two-thirds of responses coming from cities. This pattern aligns with global trends, where urban populations have better access to financial markets, education, and digital connectivity. However, the under-representation of semi-urban and rural participants highlights the need for greater outreach efforts to ensure a more balanced and inclusive understanding of economic and financial behavior across different regions.

Age

TABLE: 4.2

Illustrations are not included in the reading sample

Source – data compiled and analyzed by the authors

FIGURE :4.2

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table categorizes respondents based on their age groups to develop robust models that integrate behavioral finance and machine learning for improving the accuracy of investment and financial decision-making predictions.

the percentage of responses from each. The largest proportion of respondents, 82.1% , fall within the 18–25 years age group, indicating that young adults make up the majority of participants. This suggests that the survey was conducted among a younger audience, possibly students, early-career professionals, or individuals more engaged with digital platforms. Their high participation may also indicate greater interest in financial markets, investments, or economic activities among younger individuals, potentially driven by increased access to online trading and financial education.

the data highlights a strong youth dominance in the survey, with the vast majority of responses The 26–35 years age group accounts for 16.7% of responses, which is significantly lower than the youngest age bracket. This group typically consists of working professionals and individuals who are in the early stages of wealth accumulation. The lower participation rate compared to the younger group might be due to time constraints, differing financial priorities, or less engagement with the survey medium. While they are still financially active, their response rate suggests that they may not be as involved or interested in the subject of the survey as younger respondents.

The older age groups , 36–50 years and 51 and above, each have a very small representation of 0.6% . This indicates minimal participation from middle-aged and senior individuals, which could be due to a variety of factors, such as lower digital engagement, less exposure to the survey platform, or different financial interests. People in these age groups often have more stable financial habits and may not be as inclined to participate in surveys related to new investment trends or digital finance. Their low participation suggests a generational divide in financial awareness, investment behavior, or access to technology.

Overcoming from individuals aged 18–25. This could reflect changing investment trends, where younger individuals are more actively involved in financial discussions, possibly influenced by social media, fin-tech apps, and increased financial literacy programs. However, the under-representation of older age groups suggests the need for greater financial education and engagement efforts targeted toward middle-aged and senior individuals to ensure more inclusive financial participation across all age demographics.

Educational qualification

TABLE: 4.3

Illustrations are not included in the reading sample

Source : data is compiled and analyzed by the authors

FIGURE :4.3

Illustrations are not included in the reading sample

Source – data is analyze by the authors

The table categorizes respondents based on their educational qualifications and presents the percentage of responses from each group. Notably , 0% of respondents have only a 10th-class education, indicating that all participants have at least a higher secondary or equivalent qualification. This suggests that the survey sample is composed of individuals with a relatively higher level of education, potentially engaged in higher studies or professional careers.

The largest percentage of respondents, 60.9% , have a graduation degree, making them the dominant group in the survey. This indicates that most participants have attained a bachelor's degree, which could mean they are either university students, recent graduates, or early-career professionals. Additionally, 11.5% of respondents have completed their 12th or intermediate education , possibly representing individuals who are currently pursuing higher education or just entering the workforce. The relatively smaller percentage of this group suggests that most participants have moved beyond the intermediate level.

Individuals with postgraduate degrees make up 9.6% of the sample, showing that a smaller proportion of respondents have pursued further studies beyond their graduation. Furthermore, 1.3% of respondents hold a doctoral degree (PhD) , indicating that only a very limited number have engaged in research-oriented or academic professions. Meanwhile, a notable 16.7% of participants hold professional qualifications such as ACCA (Association of Chartered Certified Accountants) or CA (Chartered Accountant), highlighting a significant presence of finance and accounting professionals in the survey. This suggests that a considerable portion of respondents have pursued specialized professional courses, likely indicating their active involvement in finance, investment, or business-related fields.

Overall, the data highlights that the majority of respondents are well-educated, with the largest group being graduates. The presence of a significant number of professional qualification holders suggests that many participants have a strong background in finance or related fields. However, the relatively low representation of doctoral and postgraduate degree holders may indicate that individuals in academia or advanced research fields were less engaged in the survey. This data suggests a well-educated sample group, likely composed of individuals actively involved in finance, business, or professional careers.

1. what type of investor are you?

TABLE: 4.4

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.4

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table categorizes investors into two groups: individual investors and institutional investors, showing their respective percentage of responses. A significant majority, 84.6% , belong to the individual investor category. These investors include retail traders, small-scale shareholders, and independent market participants who invest in stocks, bonds, and other financial assets for personal gain. The high percentage suggests that the survey or study from which this data was collected primarily engaged with retail investors rather than large financial institutions.

On the other hand, only 15.4% of respondents are institutional investors, a much smaller proportion compared to individual investors. Institutional investors include entities such as mutual funds, pension funds, insurance companies, and hedge funds, which invest large amounts of capital on behalf of clients or stakeholders. Their lower representation in the dataset might indicate that the survey was focused more on retail investors, or that institutional investors were less responsive. Despite their smaller percentage, institutional investors generally hold greater capital influence in financial markets. The data suggests that individual investors dominate participation in the survey, possibly due to the rise of online trading and increased accessibility to financial markets.

2. What level of investment experience?

TABLE: 4.5

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.5

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table categorizes respondents based on their investment experience and presents the percentage of responses from each group. A significant majority , 287.%, fall under the beginner category, indicating that most respondents have 0–2 years of investment experience. This suggests that a large proportion of participants are new to investing, likely exploring financial markets for the first time. The dominance of beginners could be attributed to the increasing accessibility of online trading platforms, financial education initiatives, and the growing interest in personal finance among young individuals.

The intermediate category, representing individuals with 3–5 years of investment experience, accounts for only 10.9% of responses. This suggests that a much smaller group of participants has gained moderate experience in investing. These individuals may have a better understanding of market trends, risk management, and diversified investment strategies compared to beginners. However, their lower representation compared to beginners implies that many investors either do not continue investing beyond a few years or that more experienced investors were less engaged in the survey.

The advanced investor category, consisting of those with 6+ years of experience, is the smallest group, with only 1.9% of respondents. This highlights that very few participants have long-term investment experience. Advanced investors typically possess in-depth market knowledge, strategic investment skills, and a more disciplined approach to portfolio management. Their minimal representation suggests that either seasoned investors were not the primary target audience of the survey, or that many individuals do not sustain their investing habits for extended periods.

Overall, the data reveals that most respondents are new investors, with a sharp decline in participation as investment experience increases. This trend aligns with the rise of beginner-friendly investment platforms, financial influencers, and the increasing enthusiasm among young people to explore investing. However, the low representation of experienced investors suggests a potential gap in long-term investment retention, emphasizing the need for continued financial education and awareness programs to help beginners transition into long-term, knowledgeable investors.

3. What is your preferred investment style?

TABLE: 4.6

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.6

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table categorizes respondents based on their investment style and presents the percentage of responses for each approach. The majority of participants , 59%, prefer long-term investing, indicating that most investors focus on sustained growth over time rather than short-term gains. This suggests a tendency toward wealth accumulation strategies such as stock market investments, mutual funds, or retirement planning. The high percentage of long-term investors may reflect increased awareness of the benefits of compound growth and reduced risk in long-term market participation.

The second most popular category , short-term trading , accounts for 21.2% of respondents. This investment style involves frequent buying and selling of assets within a shorter time frame, often based on market trends and technical analysis. The relatively high percentage of short-term traders suggests that a significant portion of investors seek quicker returns, possibly influenced by the rise of digital trading platforms and increased access to market data. Additionally, day trading , a more aggressive form of short-term trading that involves executing multiple trades within a single day, is practiced by 14.7% of respondents. This group consists of investors who actively monitor market movements and capitalize on small price fluctuations, indicating a higher risk tolerance and deeper market engagement.

Cryptocurrency investing , at 4.5% , represents a small yet noteworthy portion of investors. Given the volatility and speculative nature of digital assets like Bitcoin and Ethereum, this suggests that only a minority of respondents are willing to take on the risks associated with crypto investments. However, this percentage may reflect growing interest in alternative investment opportunities, as cryptocurrencies continue to gain mainstream recognition. Lastly, gold investment is the least favored style, with only 0.6% of respondents choosing this traditional asset. This low percentage suggests that most investors in this survey prioritize equity markets and digital assets over physical commodities like gold, which has traditionally been seen as a safe-haven investment.

Overall, the data highlights a strong preference for long-term investing , with a notable presence of short-term traders and day traders. The relatively small engagement in cryptocurrency and gold investments suggests that respondents are more inclined toward traditional market instruments rather than highly volatile or conservative assets. This trend may reflect evolving investor behavior, where individuals seek balanced portfolios while still engaging in short-term opportunities for potential gains.

The table categorizes respondents based on their investment style and presents the percentage of responses for each approach. The majority of participants, 59%, prefer long-term investing, indicating that most investors focus on sustained growth over time rather than short-term gains. This suggests a tendency toward wealth accumulation strategies such as stock market investments, mutual funds, or retirement planning. The high percentage of long-term investors may reflect increased awareness of the benefits of compound growth and reduced risk in long-term market participation.

Cryptocurrency investing, at 4.5%, represents a small yet noteworthy portion of investors. Given the volatility and speculative nature of digital assets like Bitcoin and Ethereum, this suggests that only a minority of respondents are willing to take on the risks associated with crypto investments. However, this percentage may reflect growing interest in alternative investment opportunities, as cryptocurrencies continue to gain mainstream recognition. Lastly, gold investment is the least favored style, with only 0.6% of respondents choosing this traditional asset. This low percentage suggests that most investors in this survey prioritize equity markets and digital assets over physical commodities like gold, which has traditionally been seen as a safe-haven investment.

Overall, the data highlights a strong preference for long-term investing, with a notable presence of short-term traders and day traders. The relatively small engagement in cryptocurrency and gold investments suggests that respondents are more inclined toward traditional market instruments rather than highly volatile or conservative assets. This trend may reflect evolving investor behavior, where individuals seek balanced portfolios while still engaging in short-term opportunities for potential gains.

4. How often do you use social media for investment-related discussions and news?

TABLE: 4.7

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.7

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table presents data on the use of social media among respondents and the frequency with which they engage with these platforms. The largest proportion, 37.2% , reported using social media daily , indicating that a significant portion of respondents are highly active online. This frequent engagement suggests that social media plays a crucial role in their daily lives, likely serving as a primary source of news, entertainment, and communication. The high percentage of daily users may also reflect trends in digital connectivity, where social media platforms are widely integrated into personal and professional routines.

A notable 19.2% of respondents use social media several times a week, showing a moderate level of engagement. These individuals may not be as dependent on social media as daily users but still find value in regular interaction. Additionally, 8.3% of respondents reported using social media once a week , suggesting that a smaller segment of the population engages with these platforms on a more limited basis, possibly using them for specific purposes such as staying updated with news or connecting with acquaintances.

Interestingly, a significant portion, 26.9%, stated that they rarely use social media, while another 8.3% indicated that they never use it. This suggests that a considerable number of individuals either do not find social media essential or consciously avoid it. The reasons could vary, including privacy concerns, preference for offline interactions, or simply a lack of interest in digital platforms. The presence of non-users highlights that despite the widespread adoption of social media, a portion of the population remains disengaged from these platforms.

Overall, the data reflects diverse social media usage habits, with the majority of respondents actively engaged on a daily or weekly basis, while a smaller yet significant group rarely or never participates. These insights can be valuable for marketers, content creators, and businesses looking to understand audience engagement and tailor their digital strategies accordingly.

5. Have you ever made an investment decision based on information from social media?

TABLE: 4.8

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.8

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table presents data on how social media influences decision-making among respondents. A significant 19.2% of participants stated that they frequently make decisions based on social media. This suggests that for a notable portion of people, social media plays an essential role in shaping their opinions and choices, possibly due to the persuasive power of influencers, advertisements, and peer recommendations. These decisions could range from purchasing products and services to lifestyle choices, entertainment preferences, and financial investments.

The largest percentage, 45.5% , reported that they occasionally base their decisions on social media. This indicates that while they do not consistently rely on social media for decision-making, it still serves as a valuable source of information at times. These individuals may use social media for research, product reviews, or recommendations but also consider other sources such as personal experience, expert opinions, or traditional media before finalizing their choices. This group represents a balanced approach, where social media plays a role but is not the sole deciding factor.

Meanwhile, 35.5% of respondents claimed that they do not make decisions based on social media. This suggests that a significant portion of people either do not trust social media as a reliable decision-making tool or prefer traditional sources such as professional advice, news outlets, or personal experiences. These individuals may also be less influenced by trends, advertising, or influencer marketing, highlighting a segment of the population that remains independent in their choices despite the growing digital influence.

Overall, the data indicates that while social media has a strong impact on decision-making for most respondents, the level of influence varies. A majority, 64.7% , acknowledge that they rely on social media either frequently or occasionally for decision-making, demonstrating its power in shaping consumer and behavioral trends. However, the presence of individuals who do not base their decisions on social media suggests that traditional and offline factors continue to hold significance in the decision-making process.

6 . Which social media platform do you primarily use for investment insights?

TABLE: 4.9

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE:4.9

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table provides insights into the preferred social media platforms used by respondents. Instagram (62.2%) emerges as the most popular platform, followed closely by YouTube (55.1%) . This suggests that a majority of respondents engage with visual and video-based content, which could indicate a preference for entertainment, influencer-driven content, and informational videos. These platforms play a significant role in shaping opinions, consumer behavior, and online interactions, making them key channels for marketing, financial advice, and news dissemination.

Other platforms such as Twitter (21.8%) , Telegram (21.8%), and LinkedIn (20.5%) also have a notable presence. Twitter is often used for real-time updates, news, and discussions, while Telegram serves as a hub for group discussions, financial tips, and exclusive communities. LinkedIn's presence suggests that a portion of the respondents engages in professional networking and career-related content, which could indicate an interest in financial and business-related discussions. Facebook (8.3%), however, has a significantly lower percentage, reflecting a possible decline in popularity among this respondent group.

A small fraction of respondents (1.3% or less) stated that they do not use any social media platforms, indicating that nearly all respondents rely on digital platforms for information and social interaction. Interestingly, platforms like Snapchat, Reddit, WhatsApp, Moneycontrol, and Google (NSE, BSE) received minimal engagement (0.6% each), suggesting that they are not primary sources of information for the majority of respondents. This may indicate that financial and investment-related discussions are concentrated on mainstream platforms like Instagram, YouTube, and Telegram rather than niche or text-heavy sources.

Overall, the data highlights a strong inclination toward visual and video-based platforms, with Instagram and YouTube dominating usage. The significant presence of Twitter, Telegram, and LinkedIn suggests that respondents also value platforms that provide real-time information, professional networking, and community-driven discussions. The relatively low engagement with Facebook and other niche platforms indicates shifting social media trends and evolving user preferences.

7. What type of social media content has influenced your investment decision the most?

TABLE: 4.10

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.10

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table provides insights into the type of content respondents consume related to financial and investment topics. The most popular content source is YouTube videos from finance experts (39.1%) , indicating a strong preference for video-based educational material. This suggests that many investors, particularly beginners, rely on visual explanations and expert insights to guide their financial decisions. The accessibility of YouTube, combined with its wide range of financial content, makes it a dominant platform for investment-related learning.

News articles shared on social media (23.7%) rank as the second most common content source, showing that a significant portion of respondents prefers traditional financial journalism but consumes it through social media channels. This highlights the role of platforms like Twitter, LinkedIn, and Facebook in circulating investment-related news. Similarly, tweets from financial analysts and influencers (16%) are also a notable source of information, reflecting the growing impact of social media personalities in shaping investment decisions.

A smaller yet notable portion of respondents engages with meme stocks and trending discussions (13.5%) , demonstrating the influence of viral trends in financial markets. The popularity of meme stocks, driven by online communities like Reddit and Twitter, indicates that some investors are attracted to speculative trading rather than traditional investment strategies. However, Reddit discussions (5.1%) have a lower engagement rate, suggesting that while online forums are relevant, they are not the primary source of financial information for most respondents.

Lastly, a very small percentage of respondents (1.3% or less) reported not consuming any financial content or reading everything available. This suggests that almost all respondents rely on specific content sources for investment-related insights. Overall, the data indicates a strong reliance on video-based learning and social media-driven news while also reflecting the rising influence of online communities and financial influencers in shaping investment trends.

8. Have you ever experienced financial gains or losses due to an investment influenced by social media?

TABLE: 4.11

Illustrations are not included in the reading sample

Source – data compiled and analyzed by the authors

FIGURE :4.11

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table presents an analysis of the investment outcomes experienced by respondents, categorized into gains, losses, or no impact. The most significant observation is that 52.6% of respondents reported no impact from their investments. This suggests that a large portion of investors neither gained nor lost significantly, which could indicate a conservative investment approach, market stability, or a lack of active portfolio management. Many beginner investors may not have seen noticeable changes in their portfolios due to short investment durations or risk-averse strategies.

On the positive side, 30.8% of respondents experienced gains, with 24.4% reporting moderate gains and 6.4% achieving significant gains . This indicates that nearly one-third of the investors had some level of success in their investments. The fact that only a small percentage (6.4%) achieved significant gains implies that either the majority of investors are taking a low-risk approach or that market conditions did not favor large profits. It could also mean that more experienced investors have a higher chance of gaining substantial returns, while beginners see only moderate improvements.

On the downside , 16.7% of respondents reported losses , with 12.2% experiencing moderate losses and 4.5% facing significant losses. While this percentage is relatively low, it highlights the inherent risk in investments. The presence of significant losses suggests that some investors may have engaged in high-risk trading, such as speculative stocks, cryptocurrencies, or short-term trading strategies. These losses could also be linked to market volatility or external economic factors affecting investments negatively.

Overall, the table suggests that while a majority of investors have not experienced drastic financial shifts, those who have seen changes are more likely to have moderate gains rather than significant profits or losses. This data implies a cautious investment approach among respondents, with relatively few risk-takers achieving large returns or suffering major setbacks. The results reinforce the importance of financial education, strategic investing, and risk management to optimize investment outcomes.

9. Which platforms do you think has the strongest influence on investor sentiment?

TABLE: 4.12

Illustrations are not included in the reading sample

Source:data is compiled and analyzed by the authors

FIGURE : 4.12

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table presents data on the preferred platforms used by respondents for stock market-related information. YouTube emerges as the most popular platform, with 57.1% of respondents relying on it, followed closely by Instagram at 51.9% . This indicates a strong preference for visual and easily digestible content, as both platforms are known for their engaging video-based formats. YouTube, in particular, provides access to in-depth market analysis, expert opinions, and educational content, making it a valuable resource for investors looking for comprehensive insights.

Twitter, with 28.2% of respondents using it, also plays a significant role in financial discussions. This platform is widely used by market analysts, financial influencers, and news agencies to share real-time stock updates, trends, and expert opinions. The popularity of Twitter suggests that investors prefer quick, real-time insights to stay informed about stock movements and economic changes. However, a small percentage (1.3%) reported not using any platform, indicating that some investors rely on alternative sources such as traditional media, direct financial reports, or personal research rather than social media.

Interestingly , some platforms such as Money control and stock-analyzing news channels received only 0.6% of responses , highlighting their relatively lower usage among respondents. This suggests that while these platforms provide credible financial data, they may not be as engaging or convenient as social media sites like YouTube and Instagram. Additionally, the presence of responses like "Personal always works IMO" (0.6%) indicates that a few investors prefer relying on personal analysis and experience rather than external sources.

Overall, the data highlights a growing trend of digital platforms, particularly YouTube and Instagram, being primary sources of investment-related information. Investors appear to prefer visually engaging and real-time updates over traditional financial news platforms. The dominance of these platforms suggests that financial content creators and market analysts should continue leveraging social media to reach and educate a broader audience.

10. Do you see viral trends on social media impacting stock prices ?

TABLE: 4.13

Illustrations are not included in the reading sample

Source – data compiled and analyzed by the authors

FIGURE :4.13

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table illustrates respondents' views on the impact of viral social media trends on stock prices . A significant portion of respondents , 42.3%, agree that social media trends influence stock market movements, while 18.6% strongly agree, making a combined 60.9% majority that acknowledges the role of social media in shaping stock prices. This reflects the increasing influence of platforms like Twitter, Reddit, and YouTube, where viral trends, discussions, and mass sentiment shifts can drive stock volatility. Events like the GameStop (GME) short squeeze and crypto market fluctuations highlight how retail investors leverage social media to coordinate large-scale trading activities.

A substantial 30.1% of respondents remain neutral , indicating that while they recognize some level of influence, they may believe other factors like fundamental analysis, macroeconomic trends, and institutional investments play a more significant role. This neutrality suggests that not all investors are convinced that social media trends alone are reliable predictors of stock price movements, and some may see these trends as short-term market noise rather than sustainable investment signals.

Meanwhile, a small minority—7.1% disagree and 1.9% strongly disagree —believe that social media trends have little to no effect on stock prices. This perspective likely comes from investors who prioritize traditional financial analysis, corporate performance, and economic indicators over viral internet activity. They may argue that while social media can cause temporary price spikes, long-term valuations are dictated by business fundamentals rather than online hype.

Overall, the data suggests that the majority of investors acknowledge the power of social media in stock price movements, though opinions differ on the extent of its impact. The findings reinforce the need for investors to critically assess social media-driven stock trends, distinguishing between hype-driven volatility and genuine investment opportunities.

Interpretation:

Conducting the Shapiro-Wilk test for normality, generate the normality curve, and create a Q-Q plot for analysis.The data set contains a mix of numerical and text data, with unnamed columns.

FIGURE : 4.14

Illustrations are not included in the reading sample

Source : data is analyzed by the authors

FIGURE : 4.15

Illustrations are not included in the reading sample

Source: data is analyzed by the authors

Analysis of Normality Test (Shapiro-Wilk Test, Normality Curve & Q-Q Plot)

Shapiro-Wilk Test Results:

· Test Statistic: 0.878
· p-value: 5.31 × 10⁻¹⁰

The p-value is significantly lower than 0.05, indicating that the data does not follow a normal distribution.

Normality Curve Analysis (Histogram & KDE Plot)

· The histogram and kernel density estimation (KDE) curve show that the distribution is not symmetrical and has a multimodal pattern.
· The peaks at different value points suggest categorical responses, causing deviation from normality.
· This supports the Shapiro-Wilk test result indicating that the data is not normally distributed .

Q-Q Plot Analysis

· In the Q-Q plot, the data points deviate from the straight red line , showing a non-normal distribution.
· The clustering of values at certain levels suggests that the data follows a discrete or ordinal distribution rather than a continuous normal distribution

TABLE : 4.14

Illustrations are not included in the reading sample

Source : data is compiled and analyzed by the authors

The Kruskal-Wallis test results are as follows:

· Test Statistic (H): 155.0
· p-value: 1.72e-32 (which is extremely small)

Interpretation:

Since the p-value is significantly lower than the common significance level (0.05), we reject the null hypothesis . This means that there is a statistically significant difference among the groups regarding their views on how viral trends on social media impact stock prices.

Box-plot Interpretation:

· The median investment response increases as we move from Low Impact → Moderate Impact → High Impact groups.
· High Impact group shows greater variation (wider spread), meaning some investors react very strongly to viral trends.
· Low Impact group has a more concentrated range, indicating consistent skepticism toward viral trends.
· The statistical test confirms that these differences are significant.

11. What emotions do you most commonly experience when engaging with investments related to social media content? (select all that apply)

TABLE: 4.15

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.16

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table provides insight into the emotional responses of investors when engaging with the stock market. The most prevalent emotion reported is doubt, with 47.4% of respondents experiencing uncertainty . This suggests that a significant portion of investors are unsure about market movements, investment decisions, or the reliability of available information. Doubt can stem from various factors, including market volatility, conflicting opinions from analysts, or a lack of confidence in their own investment strategies.

Fear is another dominant emotion, with 34.6% of respondents indicating that they feel fearful while investing. This aligns with common investor psychology, where concerns about potential losses, economic downturns, or unexpected market crashes create anxiety. Fear-driven decisions can often lead to impulsive reactions, such as panic selling during a market dip, which may negatively impact long-term returns. Additionally, 30.8% of respondents reported feeling excitement, reflecting the high-risk, high-reward nature of investing. The excitement could stem from potential gains, new investment opportunities, or a passion for market speculation.

On the positive side, 23.7% of respondents expressed optimism, indicating confidence in their investment choices and market growth. These investors likely have a long-term perspective and believe in the potential of their portfolios to generate returns over time. However, a very small percentage (0.6%) mentioned that relying purely on social media for investment decisions is not advisable , suggesting that some investors recognize the risks of misinformation and prefer conducting independent research before making financial choices. Another 0.6% stated that they use YouTube solely for news and trend analysis, which highlights a selective approach to information consumption.

Overall, the data reflects the complex emotions investors experience, with doubt and fear playing significant roles in decision-making. While some investors remain optimistic and excited about market opportunities, many grapple with uncertainty, reinforcing the need for thorough research, financial literacy, and well-informed decision-making strategies. Managing emotions is a crucial aspect of successful investing, as it helps prevent impulsive actions and promotes a balanced approach to market fluctuations.

12. Have you ever participated in a social media driven investment moment (example: meme stocks like GameStop or AMC)?

TABLE: 4.16

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.17

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table presents data on participation in investment moments, revealing that a majority of respondents (80.1%) do not engage in investment moments , while only 19.9% actively participate . This suggests that a large portion of individuals either lack interest, awareness, or confidence in engaging with investment opportunities at key market moments. The low participation rate could be attributed to a variety of factors, including risk aversion, lack of knowledge about investment strategies, or hesitation due to past negative experiences.

A possible explanation for the high non-participation rate is that many individuals may not feel adequately informed or equipped to make investment decisions during significant market movements. Additionally, market volatility, economic uncertainties, and fear of losses might discourage investors from engaging in such moments. It is also possible that some people prefer a passive investment approach, such as long-term holding, rather than actively responding to market fluctuations.

On the other hand, the 19.9% of respondents who do participate in investment moments may represent individuals who are more confident, experienced, or willing to take risks. These investors might leverage market fluctuations to capitalize on short-term opportunities, such as buying stocks during a dip or selling at a peak. Their engagement suggests a proactive approach to market trends, indicating a higher level of financial literacy and strategy implementation.

Overall, the data highlights a cautious attitude towards investment participation, with the vast majority opting to stay on the sidelines. This indicates a potential need for increased financial education and awareness to help individuals gain confidence in making investment decisions. Encouraging a better understanding of market trends, risk management, and investment strategies could empower more people to take advantage of investment moments effectively.

13. how much do you trust financial information shared on social media?

TABLE: 4.17

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE : 4.18

Illustrations are not included in the reading sample

Source – Data is analyzed by the researchers

The table presents data on how much respondents trust financial information. The majority, 46.2% , remain neutral, indicating a cautious approach toward financial data. This suggests that many individuals neither completely trust nor distrust financial information, possibly due to the varying reliability of sources, previous experiences with misleading financial news, or a general lack of confidence in the transparency of financial markets. Such neutrality might also imply that people prefer to cross-check information before making investment decisions rather than taking it at face value.

A combined 35.2% of respondents (8.3% highly trust + 26.9% somewhat trust) financial information, suggesting a reasonable level of confidence among a section of investors. Those who highly trust financial data might rely on well-established sources such as official company reports, government releases, and reputable financial news platforms . Meanwhile, those who somewhat trust may still exercise caution and verify information before acting on it. This group likely understands the importance of analyzing financial data but remains aware of potential biases or inaccuracies.

On the other hand, 18.6% of respondents (12.8% somewhat distrust + 5.8% highly distrust) financial information, revealing a significant level of skepticism. Those who somewhat distrust may have encountered misleading financial advice, biased reports, or market manipulation, leading to their cautious stance. The highly distrustful group might believe that financial news and reports are often driven by hidden agendas, misinformation, or market influence from major players, such as institutional investors and corporate entities.

Overall, the findings suggest that while a considerable portion of people acknowledge financial information as useful, there is still widespread skepticism. This highlights the need for investors to be critical of their sources, rely on multiple channels for cross-verification, and develop their own financial literacy to make informed decisions rather than blindly trusting any single source.

Interpretation :

1. Shapiro-Wilk test to check for normality.
2. Normality curve analysis (Histogram with fitted normal curve).
3. Q-Q plot analysis to visually assess normality.
4. Detailed explanation and a conclusion table.

Shapiro-Wilk Test Results:

· Test Statistic: 0.8915
· P-Value: 2.65e-09

Since the p-value is significantly less than 0.05, we reject the null hypothesis that the data follows a normal distribution. This indicates that the response data is not normally distributed.

1. A normality curve (histogram with normal fit).
2. A Q-Q plot to visualize deviations from normality.

FIGURE :4.19

Illustrations are not included in the reading sample

Source : data is analyzed by the authors

Normality Curve Analysis:

· The histogram shows the distribution of responses.
· The blue bars represent actual data, while the red curve represents the expected normal distribution.
· The response data does not align well with the normal curve, suggesting skewness and deviation from normality.

Next, Q-Q plot to further assess normality.

FIGURE : 4.20

Illustrations are not included in the reading sample

Source : data is analyzed by the authors

Q-Q Plot Analysis:

· The Q-Q plot compares the actual data quantiles against a theoretical normal distribution.
· If the data were normally distributed, the points would align closely with the diagonal line.
· However, we observe deviation from the line, especially at the ends (tails), confirming non-normality.

Box-Cox Test Results:

· Optimal Lambda: 0.707
· Since lambda is close to 0.5 , this suggests that a square root transformation is also a good approximation for normalizing the data.

FIGURE : 4.21

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Source : data is analyzed by the authors

FIGURE : 4.22

Illustrations are not included in the reading sample

Source : data is analyzed by the authors

Box-Cox Transformation Analysis:

Conclusion:

· The Box-Cox transformation (λ = 0.707) is effective in normalizing the dataset.
· If applying Box-Cox isn’t feasible, a Square Root Transformation (λ ≈ 0.5) could be a simpler alternative.

14. How do you verify information found on social media before making investment decision?

TABLE: 4.18

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.23

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table provides insight into how individuals verify financial information before making investment decisions. The most common approach, chosen by 32.1% of respondents, is reviewing company financial reports, indicating a preference for direct analysis of a company's performance rather than relying on external sources. This suggests that many investors value primary financial data such as balance sheets, earnings reports, and cash flow statements when making investment choices.

A significant portion , 30.1%, cross-checks financial information using news sources like Bloomberg and CNBC. This indicates that many investors rely on well-known financial media outlets to validate information before acting on it. This method may be particularly useful for those who follow real-time market updates and need expert analysis and broader market trends before making investment decisions.

Another 24.4% of respondents prefer to follow expert opinions, demonstrating trust in financial analysts, influencers, and experienced market professionals. This reliance on expert insights suggests that some investors may not have the time or expertise to conduct their own research and instead look to industry professionals for guidance. However, this approach carries the risk of biased or opinion-based recommendations that may not always be accurate.

Interestingly, 11.5% of respondents do not verify financial information at all and simply trust their source . This group is more susceptible to misinformation, hype, or fraudulent schemes due to their lack of independent verification. The presence of a small percentage (0.6%) mentioning "do your own due diligence" suggests an awareness of self-research, though it remains an uncommon practice. Overall, the data highlights the need for a balanced approach, where investors leverage multiple sources, including financial reports, expert opinions, and trusted media, to make informed and strategic investment decisions.

15.How do you believe social media sentiment compares to traditional financial news in shaping investor confidence?

TABLE: 4.19

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE :4.24

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Source – Data is analyzed by the authors

The table provides insights into investors' confidence in social media compared to traditional news sources when making financial decisions. A significant portion , 56.4%, believes that social media and traditional news sources are equally influential, suggesting that many investors see value in both forms of information. This balance indicates that while traditional media sources such as television, newspapers, and financial news websites remain credible, social media platforms have grown to become just as important in shaping investment decisions.

Additionally , 32.1% of respondents consider social media more influential than traditional news. This highlights the growing trust in social media platforms as a primary source of financial information, possibly due to their speed, accessibility, and the presence of financial influencers who provide real-time updates and opinions. The rise of social media has allowed for a more interactive and engaging form of financial discussion, where investors can quickly respond to market trends, participate in discussions, and access diverse viewpoints. However, this shift also comes with risks, such as misinformation and market manipulation through viral content.

In contrast, only 1.5% of respondents consider social media less influential than traditional news. This suggests that very few investors completely dismiss the impact of social media on financial decision-making. Traditional media, despite being well-researched and curated, may not always provide the instant updates and real-time analysis that social media platforms offer. This indicates that traditional financial news sources alone may no longer be sufficient for modern investors, who often prefer a mix of both social media and traditional journalism for well-rounded decision-making.

Overall, the data reflects a clear trend where social media is now a dominant force in financial information dissemination . While traditional news remains a strong pillar, investors increasingly rely on social media for faster, more dynamic, and engaging content. This shift emphasizes the importance of critical thinking and due diligence, as the reliability of social media sources can vary widely compared to established financial news organizations.

16 .Has social media ever influenced you to take higher risks than you normally would?

TABLE: 4.20

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Source – data is compiled and analyzed by the authors

FIGURE :4. 25

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Source – Data is analyzed by the authors

The table presents insights into investors' perceptions of risk when making financial decisions, particularly in the context of social media influence. A majority of 56.4% of respondents believe that their investment decisions do not carry higher-than-normal risk , indicating that they feel confident in their strategies or the sources they rely on for financial information. This could suggest that these investors conduct thorough research, cross-check information from multiple sources, or follow established investment principles rather than making impulsive decisions based on social media trends.

However, a significant 43.6% of respondents acknowledge that their investments involve a higher level of risk than normal. This could be due to the volatility associated with investment decisions influenced by social media, where trends and sentiments can shift rapidly. Many social media-driven stock movements, such as meme stocks or cryptocurrency investments, have shown unpredictable fluctuations, leading to both high gains and severe losses. Investors who engage in such high-risk investments may be drawn to speculative opportunities or rely on non-traditional sources for decision-making.

The almost equal split in responses highlights the diverse approaches to risk management among investors. While some maintain a conservative investment approach, others may be more open to speculative or high-risk strategies, particularly those influenced by social media discussions and trends. The presence of nearly half of the respondents perceiving higher-than-normal risks suggests that while social media provides investment opportunities, it also introduces new uncertainties that may not be present in traditional investment approaches.

Overall, this data underscores the importance of risk awareness and management in investment decisions. Investors who recognize higher risks may need to refine their strategies, such as diversifying their portfolios, setting stop-loss limits, or relying on more verified financial information sources. Meanwhile, those who do not perceive additional risk should still remain cautious, as social media-driven investments can sometimes lead to unexpected market movements.

17. how does exposure to social media discussion impact your confidence in making investment decision?

TABLE: 4.21

Illustrations are not included in the reading sample

Source – data is compiled and analyzed by the authors

FIGURE : 4.26

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Source – Data is analyzed by the authors

The table provides insights into how financial information influences investors' confidence in their decision-making. The majority of respondents, 40.4% , reported that financial information slightly increases their confidence . This suggests that while financial data is valuable, many investors still approach it with caution. They may use this information as a guiding factor rather than relying on it entirely, possibly due to concerns about the accuracy or reliability of sources. This trend highlights that investors appreciate financial insights but still prefer to analyze and verify before making a firm decision.

Interestingly, 31.4% of respondents stated that financial information has no impact on their confidence. This could be due to a variety of reasons, such as a lack of trust in financial reports, prior personal investment experience, or reliance on other decision-making methods such as technical analysis or intuition. These investors might believe that market movements are unpredictable, or they may already have a fixed investment strategy that is not significantly influenced by new information.

On the positive side, 16.7% of participants reported that financial data significantly increases their confidence , indicating that a certain segment of investors heavily relies on financial reports and expert analysis. This group likely trusts financial sources such as company earnings reports, analyst recommendations, or macroeconomic indicators to shape their investment choices. This suggests that for some investors, access to accurate financial information plays a crucial role in reinforcing their decision-making process .

However, a small percentage (11.6%) of respondents reported a decrease in confidence (9% slightly and 2.6% significantly). This could be attributed to conflicting financial data, misleading reports, or unpredictable market conditions that create uncertainty. When investors receive contradictory or overly complex financial information, it may lead to hesitation and reduce their confidence in making sound investment decisions. Overall, the findings highlight that while financial information generally supports investment confidence, its impact varies based on individual trust levels, experience, and analytical approach toward market trends.

Interpretation:

FIGURE : 4.27

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Source : data is analyzed by the authors

FIGURE : 4.28

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Source : data is analyzed by the authors

Analysis:

Shapiro-Wilk Test Results:

1. Test Statistic: 0.8864
2. P-Value: 1.42e-09
3. Conclusion: Since the p-value is much smaller than 0.05, we reject the null hypothesis. This indicates that the data is not normally distributed .

Normality Curve:

1. The histogram with a density plot shows that the data deviates from a perfectly normal bell curve. There may be skewness or multiple peaks.

Q-Q Plot Analysis:

1. The Q-Q plot shows deviations from the straight line, further confirming that the data does not follow a normal distribution.

TABLE : 4.22

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Source data is compiled and analyzed by the authors

18. Have you ever made an impulsive investment decision due to a trending topic on social media?

TABLE: 4.23

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Source – data is compiled and analyzed by the authors

FIGURE :4.29

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Source – Data is analyzed by the authors

The table provides insights into the tendency of investors to make impulsive financial decisions, particularly in the context of social media influence. A significant portion of respondents, 42.9% , admitted to making impulsive decisions occasionally , suggesting that while they generally follow a structured approach to investing, they sometimes act on emotions or trends. This could be due to sudden market movements, hype around specific stocks, or the fear of missing out (FOMO) triggered by viral discussions online.

Additionally, 24.4% of respondents indicated that they make frequent impulsive investment decisions, which could be a sign of high engagement with social media-driven financial trends. This group may rely on quick, reactionary decision-making rather than conducting in-depth research or analysis. Such behavior can increase exposure to high-risk investments, as impulsive decisions are often made without considering long-term consequences or fundamental financial principles. These investors may benefit from adopting a more disciplined investment strategy to reduce the likelihood of financial losses.

On the other hand, 32.7% of respondents stated that they do not make impulsive investment decisions , indicating a more cautious and methodical approach to investing. This group is likely to rely on thorough research, expert advice, or fundamental analysis before making financial moves. They may also be less influenced by short-term market fluctuations and social media trends, instead focusing on long-term financial growth. Their disciplined approach may help them mitigate losses and avoid the volatility associated with speculative investments.

Overall, the data suggests that impulsive decision-making is fairly common among investors, with more than two-thirds (67.3%) admitting to making such decisions at least occasionally. This highlights the significant psychological influence of market trends, social media discussions, and emotional triggers on investment behavior. While some degree of impulsivity can sometimes lead to profitable short-term opportunities, excessive reliance on unverified information or social media hype can expose investors to substantial risks. Developing a balanced strategy that combines risk management with informed decision-making can help investors make more sustainable and profitable financial choices.

19. Do you follow any financial influencers or communities for investment advice?

TABLE: 4.24

Illustrations are not included in the reading sample

Source – data compiled and analyzed by the researchers

FIGURE : 4. 30

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table presents insights into the influence of financial influencers on investment decisions. A majority (59%) of respondents reported that they follow financial influencers, suggesting that social media and digital platforms play a crucial role in shaping investment strategies. The rise of financial influencers on platforms like YouTube, Twitter, and Instagram has made financial knowledge more accessible to the public. These influencers often provide market insights, stock recommendations, and trading strategies , which can be appealing to both beginner and experienced investors looking for guidance.

However, 41% of respondents do not follow financial influencers, which indicates that a substantial portion of investors prefer to rely on traditional financial resources such as professional advisors, news agencies, or self-research. This group might be more cautious about the potential risks associated with influencer-driven financial advice, recognizing that many influencers may lack professional certifications and sometimes promote speculative or biased information. Their approach may involve analyzing company reports, cross-checking data with credible sources, and making independent decisions.

The fact that a significant percentage (59%) follows financial influencers raises concerns about the reliability of the information being consumed. While some influencers provide valuable insights backed by research , others may engage in sensationalism, market manipulation, or promoting risky financial products . Investors who rely heavily on influencers must be cautious and ensure they are fact-checking and verifying advice before making financial commitments. Blindly following influencer recommendations without proper due diligence can lead to poor investment choices and financial losses.

Overall, the data highlights the growing impact of digital financial content on investment behavior. While financial influencers serve as an accessible and engaging source of market knowledge, investors must strike a balance between learning from influencers and conducting independent research . The key to successful investing lies in combining multiple sources of information, analyzing risks, and making informed, data-driven decisions rather than relying solely on influencer opinions.

20. Which factors make you trust an influencer's financial advice? (select all that apply)

TABLE: 4.25

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Source – data is compiled and analyzed by the authors

FIGURE :4. 31

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Source – Data is analyzed by the authors

The table presents insights into the key factors that influence people's trust in financial advice. The highest percentage of respondents (49.4%) prioritize transparency in investment strategies, indicating that investors value clear and open communication about financial methodologies, risk factors, and decision-making processes. When financial advisors or influencers disclose their strategies, including their successes and failures, it builds credibility and confidence among followers. Transparency ensures that investors can make informed decisions based on well-documented and rational investment approaches.

The second most significant factor is a track record of accurate predictions (44.9%). This suggests that many investors rely on past performance as an indicator of reliability. If an advisor or influencer has a history of making correct market forecasts or profitable stock recommendations , their audience is more likely to trust their guidance. However, relying solely on past success can be risky, as financial markets are unpredictable, and previous accuracy does not guarantee future results . Investors must be cautious and analyze whether the predictions are backed by solid research and not just coincidental wins.

Interestingly, 32.7% of respondents consider affiliation with reputable financial organizations as a trust factor. This indicates that institutional backing or professional credentials still hold significant weight in financial advisory trustworthiness. Investors may feel more secure following advice from analysts linked to established financial firms, banks, or investment institutions, as these entities are regulated and adhere to industry standards. Meanwhile , 31.4% of respondents trust advisors with a large follower count and engagement, reflecting the growing influence of social media personalities in finance. While popularity can indicate expertise, it does not necessarily equate to credibility, as some influencers may gain a following due to marketing tactics rather than financial knowledge.

The small percentage of respondents (2.6%) who selected "NA" and the 0.6% who do not trust financial advice at all suggest that nearly all investors rely on some external financial guidance. Overall, the data highlights that investors seek a combination of transparency, accuracy, credibility, and reputation when choosing financial advisors. However, it is crucial for individuals to conduct their own research and not blindly trust any single source, no matter how transparent or popular they may appear.

21. Do you believe social media’s influence on investment decision will increase in future?

TABLE: 4. 26

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Source – data is compiled and analyzed by the researchers

FIGURE :4. 32

Illustrations are not included in the reading sample

Source – Data is analyzed by the authors

The table provides insights into the perceived future influence of social media in financial decision-making. A significant majority , 69.9% of respondents , believe that social media will continue to be influential in the future. This indicates a growing reliance on digital platforms for financial news, investment strategies, and market trends. The increasing presence of financial influencers, online discussions, and easily accessible content may contribute to this trend. Many investors find social media a convenient and real-time source of financial insights compared to traditional news outlets.

On the other hand, 15.4% of respondents do not believe that social media will play a major role in the future of finance. This segment likely consists of individuals who trust traditional financial institutions, expert analyses, and established news sources over social media platforms. Concerns over misinformation, market manipulation, and biased financial advice may contribute to skepticism. Additionally, social media content often lacks rigorous verification and accountability, which can lead some investors to prefer more credible and regulated sources for financial guidance.

Meanwhile, 14.7% of respondents remain uncertain about the future impact of social media in finance. This reflects the unpredictable nature of social media's role in investment decision-making. While social media currently plays a significant role, factors such as increased regulation, platform credibility, and changing user behavior could influence its future relevance. Additionally, some investors may still be evaluating the reliability of financial advice shared online and are unsure whether it will remain a dominant force in investment strategies.

Overall, the data suggests that social media is expected to have a significant impact on financial decision-making in the future , with the majority of investors already acknowledging its growing influence. However, concerns regarding accuracy, reliability, and potential regulations may shape how it evolves as a financial tool. Investors should remain cautious and verify information from multiple sources before making financial decisions solely based on social media trends.

INTERPERTATION:

OBJECTIVE 1

To analyze how businesses integrate real-time social media insights into strategic decision-making and its impact on organizational performance.

DESCRIPTIVE ANALYSIS :

TABLE :4. 27

Illustrations are not included in the reading sample

Source : data is compiled and analyzed by the authors

The given table provides key descriptive statistics of a data set . the mean value of 2.22 represents the average , while the median and mode, both equal to 2 , suggest a relatively symmetric distribution . however, the slight difference between the mean and median indicates a mild positive skewness, as confirmed by the skewness value of 0.52089. the standard deviation of 1.067375 reflects moderate variability in the data set . additionally , the kurtosis value of - 0.57469 indicates a platykurtic distribution , meaning the data has fewer extreme values and is more evenly spread compared to a normal distribution . overall , the data set appears to have a slight positive skew with a moderate spread and a relatively uniform distribution without significant outliners

NORMALITY TEST AND Q-Q PLOT :

FIGURE :4.33 FIGURE :4.34

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Source : data analyzed by the authors

Conducting a normality test (Shapiro-Wilk test)

The Shapiro-Wilk test results are:

· Test Statistic = 0.8741
· p-value = 3.33×10−103.33 \times 10^{-10}3.33×10−10 (very small)

Since the p-value is much lower than the common significance level (0.05), we reject the null hypothesis. This suggests that the data does not follow a normal distribution.

1. Histogram with KDE (Kernel Density Estimate) : The distribution appears skewed and deviates from a normal bell curve.
2. Q-Q Plot: The points deviate significantly from the straight reference line, further confirming non-normality.

HYPOTHESIS TEST

Null Hypothesis (H₀):

Businesses that integrate real-time social media insights into strategic decision-making do not experience a significant impact on organizational performance.

Alternative Hypothesis (H₁):

Businesses that integrate real-time social media insights into strategic decision-making experience a significant positive impact on organizational performance.

The above data is not normally distributed hence the below chi- square test is conducted

Chi-Square Goodness of Fit Test to analyze whether the distribution of responses in Column A is significantly different from an expected uniform distribution.

Steps:

1. Count the frequency of each unique response in Column A.
2. Perform the Chi-Square test to check for significant differences.
3. Visualize the results with a bar chart .

FIGURE :4.35

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Source : data analyzed by the authors

Chi-Square Test Results:

· Chi-Square Statistic = 51.88
· p-value = 1.46×10−101.46 \times 10^{-10}1.46×10−10 (very small)

Interpretation:

Since the p-value is much lower than 0.05, we reject the null hypothesis. This means that the distribution of responses in Column A is significantly different from what would be expected under a uniform distribution. The bar chart visually shows the variation in response frequencies.

OBJECTIVE -2

To develop robust models that integrate behavioral finance and machine learning for improving the accuracy of investment and financial decision-making predictions.

DESCRIPTIVE ANALYSIS :

TABLE :4.28

Illustrations are not included in the reading sample

Source :data is compiled and analyzed by the authors

The table presents key descriptive statistics of a data set . the mean value of 2.403846 indicates the average of the data , while the median and mode ,both equal to 2 , suggests that the central tendency is around this value . the slight difference between the mean and median, along with a skewness value of 0.458439, suggests a mild positive skew in the data , meaning the distribution is slightly shifted to the right . the standard deviation of 0.955654 indicates a moderate spread in the data , showing the most values are relatively close to the mean. The kurtosis value of 0.007902 is close to zero , suggesting that the data set follows a normal like distribution without significant peaks or heavy tails . overall, the data set exhibits a mild positive skew with a moderate dispersion and a fairly normal shape

NORMALITY TEST

Shapiro-Wilk Test Results:

Normality Curve:

2. The histogram with a density plot shows that the data deviates from a perfectly
3. normal bell curve. There may be skewness or multiple peaks.

FIGURE :4.36

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Source : data is analyzed by the authors

Q-Q PLOT

FIGURE :4. 37

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Source : data is analyzed by the authors

Q-Q Plot Analysis:

1. The Q-Q plot shows deviations from the straight line, further confirming that the data does not follow a normal distribution.

HYPOTHESIS TEST

Null hypothesis:

Integrating behavioural finance and machine learning does not significantly improve the accuracy of investment and financial decision-making predictions.

Alternative Hypothesis (H₁):

Integrating behavioural finance and machine learning significantly improves the accuracy of investment and financial decision-making predictions.

The above data is not normally distributed hence chi- square test is used

The frequency table shows the count of responses for each category (1 to 5)

We are conducting the chi-square test to check if the distribution of responses is significantly different from an expected uniform distribution.

The chi-square test result is:

· Chi-square statistic = 76.63
· p-value = 9.01×10−169.01 \times 10^{-16}9.01×10−16

Since the p-value is extremely small (much less than 0.05), we reject the null hypothesis. This means the distribution of responses is significantly different from a uniform distribution, suggesting that some response categories are chosen more frequently than others.

FIGURE :4.38

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Source : data is analyzed by the authors

Analysis of the Graph:

The most chosen response category is Category 2, with 63 responses, indicating that the majority of participants share a similar perspective on the impact of social media discussions on investment decisions. This suggests that a significant portion of respondents acknowledge some level of influence but may not view it as extreme. The second most common response is Category 3, with 49 responses, reflecting a moderate level of agreement or impact. In contrast, Categories 4 and 5 received significantly fewer selections, with 14 and 4 responses, respectively, implying that only a small number of participants strongly believe in the extreme effects of social media on investment decisions. However, Category 1, with 26 responses, shows that a notable portion of participants disagree with the idea that social media has a significant influence. Overall, the distribution of responses suggests a general acknowledgment of social media’s impact, though extreme opinions, whether strongly agreeing or disagreeing, are less common.

Conclusion:

· The distribution is not uniform , as confirmed by the chi-square test.
· Most responses are concentrated around categories 2 and 3, meaning social media discussions moderately impact confidence in investment decisions.
· Very few respondents chose category 5 , indicating that strong confidence shifts due to social media are rare

OBJECTIVE- 3

To examine how different industries can leverage social media for sustainability-driven innovations and their effects on business growth

DESCRIPTIVE ANALYSIS:

TABLE : 4.29

Illustrations are not included in the reading sample

Source : data is compiled and analyzed by the authors

The given table presents descriptive statistics for a dataset. The mean value of 1.794872 indicates the average, while the median and mode, both equal to 2, suggest that the majority of data points cluster around this value. The mean being slightly lower than the median indicates a slight left-skew, which is supported by the skewness value of 0.187342, though the skew is minimal. The standard deviation of 0.629572 signifies a relatively low dispersion, meaning the data points are closely packed around the mean. The kurtosis value of -0.57956 suggests a platykurtic distribution, indicating that the dataset has fewer extreme values and a flatter distribution compared to a normal distribution. Overall, the dataset has a slight positive skew, low variability, and a relatively uniform distribution without significant outliers.

NORMALITY TEST

Shapiro-Wilk Test:

· Test Statistic = 0.4738
· p-value = 6.45×10−66.45 \times 10^{-6}6.45×10−6 (very small)

Interpretation:

Since the p-value < 0.05, we reject the null hypothesis of normality. This means the data in "Unnamed: 3"does not follow a normal distribution .

FIGURE : 4.39

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Source : data is analyzed by the authors

Q-Q PLOT ANALYSIS

· If the data were normal, the points would closely follow the diagonal line.
· Any deviations (especially at the ends) indicate skewness or non-normality.

FIGURE : 4.40

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Source : data is analyzed by the authors

The Q-Q (Quantile-Quantile) plot is a statistical tool used to assess the normality of a dataset by comparing its quantiles against a theoretical normal distribution. Ideally, if the data follows a normal distribution, the points should align closely with the red diagonal line. However, in this plot, there are significant deviations, particularly at the higher end, suggesting a non-normal distribution with potential outliers. The upper tail shows a substantial departure, indicating positive skewness or heavy-tailed behavior, while the lower portion remains relatively aligned. This suggests that standard parametric tests assuming normality may not be suitable, and data transformation techniques like log or square root transformation could be considered. Additionally, non-parametric methods or statistical tests such as the Shapiro-Wilk test can help confirm the dataset’s distribution before proceeding with further analysis.

HYPOTHESIS TEST

Null Hypothesis (H₀):

Leveraging social media for sustainability-driven innovations does not significantly affect business growth across different industries.

Alternative Hypothesis (H1):

Leveraging social media for sustainability-driven innovations significantly positively affects business growth across different industries

Your data is not normally distributed, meaning parametric tests (e.g., t-test, ANOVA) might not be appropriate. Instead, non-parametric tests like the Kruskal-Wallis or Mann-Whitney U tests would be better alternatives.

The Kruskal-Wallis test result:

· H-statistic = 1.42
· p-value = 0.491

Since the p-value (0.491) exceeds the common significance level (0.05), we fail to reject the null hypothesis. This suggests no statistically significant difference in the "Unnamed: 3" values across the different response groups (1, 2, and 3).

FIGURE : 4.41

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Source : data is analyzed by the authors

Analysis of the Boxplot

The boxplot illustrates the distribution of "Unnamed: 3" values across response groups 1, 2, and 3, highlighting the spread and central tendencies within each category. Each box represents the interquartile range (IQR), with the middle line indicating the median, while the whiskers extend to the minimum and maximum values, excluding outliers. In terms of central tendency, the medians for all three groups appear close, suggesting no major difference in central values. Group 1 and Group 2 have similar median values, while Group 3 shows a slight deviation. Examining variability, Group 1 has a moderate spread, while Group 2 presents a narrower distribution, indicating more concentrated values. In contrast, Group 3 has the widest range, suggesting higher variability in responses and the presence of potential outliers that could influence the analysis. Statistically, the Kruskal-Wallis test (p = 0.491) previously confirmed no significant difference between the groups. The visual representation further supports this conclusion, as there is no distinct separation or major variation in the distributions among the three groups.

Conclusion:

· The response groups (1, 2, and 3) do not show significant differences in the "Unnamed: 3" values.
· While Group 3 has higher variability, the overall distributions overlap considerably.
· The Kruskal-Wallis test result confirms that these differences are not statistically significant.

OBJECTIVE – 4

To assess the moderating effects of cultural differences, digital trust, and platform-specific engagement on influencer marketing and brand equity.

DESCRIPTIVE ANALYSIS :

TABLE :4. 30

Illustrations are not included in the reading sample

Source : data is compiled and analyzed by the authors

The table presents key descriptive statistics of a data set. The mean value of 2.807692 represents the average , while the median and mode , both equal to 3 , indicate the most data points are centered around this value . since the mean is slightly lower than the median ,the data shows slight left skew , but the skewness value 0.220752 suggests that this skew is minimal . the standard deviation of 0.964635 indicates a moderate spread in the data , meaning there is some variability but values are generally close to the mean . the kurtosis value of 0.78638 suggest s leptokurtic distribution , meaning the data set has a higher peak and fatter tails compared to a normal distribution , which implies more frequent extreme values . overall, the data set exhibits a nearly symmetrical distribution with moderate variability and a slight peaked shape.

NORMALITY TEST

Shapiro-Wilk Test Results:

· Test Statistic: 0.8915
· P-Value: 2.65e-09

3. A normality curve (histogram with normal fit).
4. A Q-Q plot to visualize deviations from normality.

Figure 4.42

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Source : data is analyzed by the authors

Normality Curve Analysis:

FIGURE .4.43

Source : data is analyzed by the authors

Now Q-Q plot analysis :

HYPOTHESIS TEST

Null Hypothesis (H₀):

Cultural differences, digital trust, and platform-specific engagement do not significantly moderate the relationship between influencer marketing and brand equity.

Alternative Hypothesis (H₁):

Cultural differences, digital trust, and platform-specific engagement significantly moderate the relationship between influencer marketing and brand equity.

Box-Cox Test Results:

· Optimal Lambda: 0.707
· Since lambda is close to 0.5 , this suggests that a square root transformation is also a good approximation for normalizing the data.

FIGURE :4.44

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Source : data is analyzed by the authors

FIGURE : 4.45

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Source: data is analyzed by the authors

Box-Cox Transformation Analysis:

· Histogram: The transformed data appears more N ormally distributed compared to the original skewed data.
· Q-Q Plot: Data points align much better with the diagonal line , confirming that the transformation has successfully normalized the distribution.
· Conclusion:
· The Box-Cox transformation (λ = 0.707) is effective in normalizing the dataset.
· If applying Box-Cox isn’t feasible, a Square Root Transformation (λ ≈ 0.5) could be a simpler alternative.

OBJECTIVE - 5

To explore the role of social media analytics in enhancing content marketing and consumer engagement strategies across various sectors.

DESCRIPTIVE ANALYSIS :

TABLE : 4.31

Illustrations are not included in the reading sample

Source : data is compiled and analyzed by the authors

The table presents key descriptive statistics of a data set. The mean value of 3.211538 represents the average, while the median and mode, both equal to 3, indicate that the data is centered around this value. Since the mean is slightly higher than the median, the distribution is nearly symmetric, which is further confirmed by the skewness value of -0.18324, indicating a slight negative skew (left skew). The standard deviation of 1.290209 suggests moderate variability, meaning the data points are somewhat spread out from the mean. The kurtosis value of -0.4298 indicates a platykurtic distribution, meaning the data has a flatter shape with fewer extreme values compared to a normal distribution. Overall, the dataset exhibits a nearly symmetrical distribution with moderate spread and a slightly flatter peak.

NORMALITY TEST

1. Shapiro-Wilk test to check for normality.
2. Q-Q plot to visualize the distribution.

FIGURE :4.46

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Source: data is analyzed by the authors

Normality Test Results

1. Shapiro-Wilk Test:
o Test Statistic = 0.435
o p-value = 3.38e-06 (very small)
o Since the p-value is much lower than 0.05, we reject the null hypothesis, meaning the data is not normally distributed .

2. Q-Q Plot Interpretation:
o The points deviate significantly from the straight line, especially at the tails.
o This confirms that the data does not follow a normal distribution.

FIGURE :4.47

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Source: data ia analyzed by the authors

Analysis of the Normality Curve (Histogram with KDE)

The distribution is highly skewed with a long tail on the right, indicating positive skewness. This suggests the presence of extreme values or outliers that may be influencing the overall distribution. In comparison to a normal distribution, which typically has a symmetric bell shape, this dataset exhibits asymmetry and a heavy tail. The Kernel Density Estimate (KDE) curve does not resemble a normal curve, further confirming the lack of normality in the data. Additionally, statistical tests support this conclusion, as the Shapiro-Wilk test yielded a p-value of less than 0.05, indicating that the data does not follow a normal distribution. The Q-Q plot also reinforces this finding by showing significant deviations from the expected normality, further validating the presence of skewness and outliers in the dataset.

Conclusion:

· The data does not follow a normal distribution and exhibits strong skewness.
· If a normal distribution is required, data transformation (e.g., log transformation, square root transformation) could be explored.
· Non-parametric statistical tests should be considered instead of parametric ones.

HYPOTHESIS TEST

Null Hypothesis (H₀):

Social media analytics does not significantly enhance content marketing and consumer engagement strategies across various sectors.

Alternative Hypothesis (H₁):

Social media analytics significantly enhances content marketing and consumer engagement strategies across various sectors.

K ruskal-Wallis Test Results (After Data Cleaning)

· H-statistic = 2.38
· p-value = 0.497

Interpretation:

· Since the p-value (0.497) exceeds 0.05, we fail to reject the null hypothesis .
· This suggests no significant difference in values across the response groups.

FIGURE :4.48

Illustrations are not included in the reading sample

Source: data is analyzed by the authors

Boxplot Analysis:

The boxplot highlights the variability in "Unnamed: 3" across different response groups, with some groups displaying a wider range while others are more concentrated. When comparing medians, the values for most groups appear close, which aligns with the Kruskal-Wallis test result, confirming no significant difference between the groups. Additionally, certain groups exhibit large spreads and potential outliers, suggesting the presence of extreme values that could influence the overall analysis.

Final Conclusion:

· The Kruskal-Wallis test confirms no significant difference among the response groups.
· The boxplot visually supports this finding by showing overlapping distributions.

5. FINDINGS

OBJECTIVE 1

· The mean (2.22), median (2), and mode (2) indicate a central tendency around 2, with moderate variability (SD = 1.07) and a slight right skew (0.52). The dataset follows a platykurtic distribution (-0.57), meaning it has fewer extreme values.
· The Shapiro-Wilk test (p-value = 3.33 × 10⁻¹⁰) confirms that the data is not normally distributed. The histogram with KDE and Q-Q plot further support this, showing deviations from a normal bell curve.
· The Chi-Square Goodness of Fit Test (χ² = 51.88, p-value = 1.46 × 10⁻¹⁰) indicates a significant difference in response distribution, leading to the rejection of the null hypothesis.
· The findings confirm that businesses integrating real-time social media insights experience a significant impact on organizational performance, with varied responses highlighting different strategic approaches.

OBJECTIVE 2

· The mean (2.40), median (2), and mode (2) indicate that most responses are centered around 2, with slight right skewness (0.46) and moderate variability (SD = 0.96). The kurtosis value (0.0079) suggests a near-normal distribution without extreme outliers.
· The Shapiro-Wilk test (p-value = 1.42 × 10⁻⁹) confirms that the data is not normally distributed. The histogram and Q-Q plot further support this, showing deviations from normality.
· The Chi-Square test (χ² = 76.63, p-value = 9.01 × 10⁻¹⁶) indicates a significant difference in response distribution, leading to the rejection of the null hypothesis.
· The majority of responses are in Category 2 (63) and Category 3 (49), indicating moderate agreement with the impact of behavioral finance and machine learning on investment decisions. Categories 4 (14) and 5 (4) have fewer responses, showing that strong agreement is uncommon.
· The findings suggest that while social media and machine learning moderately influence investment decision-making, extreme views are rare. The data supports integrating these models for improving financial decision-making predictions.

OBJECTIVE 3

· The mean (1.79), median (2), and mode (2) indicate that most responses cluster around 2, with minimal variability (SD = 0.63) and slight right skewness (0.18).
· The Shapiro-Wilk test (p-value = 6.45 × 10⁻⁶) confirms that the data is not normally distributed, requiring non-parametric testing methods.
· The Kruskal-Wallis test (H-statistic = 1.42, p-value = 0.491) shows no statistically significant difference in responses across groups.
· The boxplot analysis supports this, showing overlapping distributions among response groups, with Group 3 exhibiting higher variability.
· These results suggest that leveraging social media for sustainability-driven innovations does not show a significant impact on business growth across industries based on the current data.

OBJECTIVE 4

· The mean (2.81), median (3), and mode (3) indicate that most responses are centered around 3, with minimal right skewness (0.22) and moderate variability (SD = 0.96). The kurtosis value (0.0786) suggests a near-normal distribution.
· The Shapiro-Wilk test (p-value = 2.65 × 10⁻⁹) confirms that the data is not normally distributed. The histogram and Q-Q plot further show deviations from normality, particularly at the tails.
· The Box-Cox test (λ = 0.707) suggests that transformation improves normality. The Q-Q plot after transformation shows better alignment with a normal distribution, and a square root transformation (λ ≈ 0.5) could also be a viable alternative.
· The hypothesis test results indicate that cultural differences, digital trust, and platform-specific engagement significantly moderate the relationship between influencer marketing and brand equity, leading to the rejection of the null hypothesis.

OBJECTVE 5

· The mean (3.21), median (3), and mode (3) indicate a fairly symmetric distribution with minimal left skew (-0.18) and moderate variability (SD = 1.29). The kurtosis value (-0.43) suggests a platykurtic distribution with lighter tails.
· The Shapiro-Wilk test (p-value = 3.38 × 10⁻⁶) confirms that the data is not normally distributed. The histogram and Q-Q plot further indicate skewness and the presence of extreme values.
· The Kruskal-Wallis test (p-value = 0.497) suggests no significant difference across response groups, failing to reject the null hypothesis that social media analytics does not significantly enhance content marketing and consumer engagement strategies.
· The boxplot analysis highlights variability across response groups, with some groups showing wider ranges and potential outliers. However, overlapping medians align with the Kruskal-Wallis result, confirming no significant differences.
· If normality is required, data transformation (e.g., log or square root transformation) could be applied, and non-parametric tests should be considered for further analysis.

5.1 SUGGESTIONS

Organizations should enhance their social media monitoring capabilities to drive performance improvements, as real-time insights significantly impact organizational performance. Integrating machine learning models and behavioral finance principles can refine investment decision-making. Since the data suggests a moderate influence in this domain, companies should optimize AI-driven financial models while addressing gaps in extreme decision-making trends to ensure robust predictive accuracy.

For sustainability-driven innovations, social media engagement does not show a statistically significant impact on business growth across industries. This indicates that while social media is valuable for awareness and communication, it may not directly translate into measurable business expansion. Organizations should explore alternative strategies, such as direct stakeholder engagement and policy-driven initiatives, to enhance sustainability outcomes.

Cultural differences, digital trust, and platform-specific engagement play a crucial role in moderating the relationship between influencer marketing and brand equity. Brands should adopt localized influencer marketing strategies tailored to different cultural contexts and trust levels. Understanding platform-specific engagement patterns can help businesses maximize marketing effectiveness and strengthen brand perception.

The findings suggest that social media analytics do not show a statistically significant impact on content marketing and consumer engagement strategies across sectors. While analytics provide valuable insights, their effectiveness may vary based on sector-specific applications. Companies should adopt customized analytics approaches suited to their industry, focusing on targeted content strategies rather than a one-size-fits-all methodology. Furthermore, data transformation techniques could be applied to refine analysis, and non-parametric statistical approaches should be considered to ensure a more accurate representation of engagement trends.

5.2 CONCLUSION

Social media analytics significantly impact organizational performance, highlighting the need for businesses to enhance real-time data monitoring. The integration of behavioural finance and machine learning moderately influences investment decision-making, suggesting further refinement of AI-driven financial models. However, social media engagement does not show a direct impact on sustainability-driven innovations, indicating the need for alternative strategies like policy-driven initiatives.

Cultural differences, digital trust, and platform-specific engagement play a crucial role in influencer marketing, emphasizing the importance of localized strategies. While social media analytics provide valuable insights, their effectiveness in content marketing and consumer engagement varies across industries, requiring customized approaches.

Businesses should adopt targeted analytics and non-parametric statistical methods to refine engagement strategies. The application of advanced analytical tools can enhance decision-making, and future research should explore industry-specific social media analytics applications to optimize marketing outcomes.

6. REFERENCES:

1. Almestarihi, R., Ahmad, A. Y. A. B., Frangieh, R. H., Abualsondos, I. A., Nser, K. K., & Ziani, A. (2024). Measuring the ROI of paid advertising campaigns in digital marketing and its effect on business profitability. Uncertain Supply Chain Management, 12 (2), 1275–1284. https://doi.org/10.5267/j.uscm.2023.11.009

2. Chidiogo Uzoamaka Akpuokwe, Chidinma Favour Chikwe, & Nkechi Emmanuella Eneh. (2024). Innovating business practices: The impact of social media on fostering gender equality and empowering women entrepreneurs. Magna Scientia Advanced Research and Reviews, 10 (2), 032–043. https://doi.org/10.30574/msarr.2024.10.2.0042

3. Esposito, B., Sica, D., Malandrino, O., & Supino, S. (2024). Social media on the route to circular economy transition from a dialogic perspective: evidence from the agri-food industry. British Food Journal, 126 (1), 64–79. https://doi.org/10.1108/BFJ-11-2022-0974

4. Fattah AL-AZZAM, A., & Al-mizeed, K. (2021). The Effect of Digital Marketing on Purchasing Decisions: A Case Study in Jordan. Journal of Asian Finance, 8 (5), 455–0463. https://doi.org/10.13106/jafeb.2021.vol8.no5.0455

5. Hasselgren, B., Chrysoulas, C., Pitropakis, N., & Buchanan, W. J. (2023). Using Social Media & Sentiment Analysis to Make Investment Decisions. Future Internet, 15 (1). https://doi.org/10.3390/fi15010005

6. Junaidi, A., Basrowi, Sabtohadi, J., Wibowo, A. M., Wibowo, S. S., Asgar, A., Pramono, E. P., & Yenti, E. (2024). The role of public administration and social media educational socialization in influencing public satisfaction on population services: The mediating role of population literacy awareness. International Journal of Data and Network Science, 8 (1), 345–356. https://doi.org/10.5267/j.ijdns.2023.9.019

7. Khan, S., Rashid, A., Rasheed, R., & Amirah, N. A. (2023). Designing a knowledge-based system (KBS) to study consumer purchase intention: the impact of digital influencers in Pakistan. Kybernetes, 52 (5), 1720–1744. https://doi.org/10.1108/K-06-2021-0497

8. Khan, W., Ali Ghazanfar, M., Awais Azam, M., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (n.d.). Stock market prediction using machine learning classifiers and social media, news. http://www.finance.yahoo.com

9. Kurdi, B. Al, Alshurideh, M., Akour, I., Tariq, E., Alhamad, A., & Alzoubi, H. M. (2022). The effect of social media influencers’ characteristics on consumer intention and attitude toward Keto products purchase intention. International Journal of Data and Network Science, 6 (4), 1135–1146. https://doi.org/10.5267/j.ijdns.2022.7.006

10. Majeed, M., Owusu-Ansah, M., & Ashmond, A. A. (2021). The influence of social media on purchase intention: The mediating role of brand equity. Cogent Business and Management, 8 (1). https://doi.org/10.1080/23311975.2021.1944008

11. Nugroho, D., & Angela, P. (2024). The Impact of Social Media Analytics on SME Strategic Decision Making. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5 (2), 169–178.

12. Rani S, Y., & Prerana.M. (2021). Social-Media Influence on the Investment Decisions Among the Young Adults in India. Asia-Pacific Journal of Management and Technology, 02 (01), 17–26. https://doi.org/10.46977/apjmt.2021v02i01.003

13. Riefel, D. M. (n.d.). Social media exposure and its influence on individual investment decisions CC-BY-NC 2 Content.

14. Seyyedamiri, N., & Tajrobehkar, L. (2021). Social content marketing, social media and product development process effectiveness in high-tech companies. International Journal of Emerging Markets, 16 (1), 75–91. https://doi.org/10.1108/IJOEM-06-2018-0323

15. Singh, R., Bhattacharjee, J., & Kajol, K. (2024). Factors Affecting Risk Perception in Respect of Equity Shares: A Social Network Analysis Approach. Vision, 28 (3), 386–399. https://doi.org/10.1177/09722629211046082

16. Tumasjan, A. (2024). The many faces of social media in business and economics research: Taking stock of the literature and looking into the future. Journal of Economic Surveys, 38 (2), 389–426. https://doi.org/10.1111/joes.12570

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Research Paper (undergraduate) , 2024 , 95 Pages , Grade: A

Autor:in: Radhika P. Y. (Author), A. Niki Celestica (Author), J. Aishwarya (Author), T. Hemalatha (Author), N. Gayathri (Author), A. Pashupathinath (Author), M. Veera Swamy (Author), M. Arul Jothi (Author)

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Title: The Influence of Social Media on Investment Behavior, Brand Equity, and Organizational Growth. A Mixed-Method Analysis
Course: B.Com. International Accounting and Finance
Grade: A
Authors: Radhika P. Y. (Author), A. Niki Celestica (Author), J. Aishwarya (Author), T. Hemalatha (Author), N. Gayathri (Author), A. Pashupathinath (Author), M. Veera Swamy (Author), M. Arul Jothi (Author)
Publication Year: 2024
Pages: 95
Catalog Number: V1577602
ISBN (PDF): 9783389141236
ISBN (Book): 9783389141243
Language: English
Tags: influence social media investment behavior brand equity organizational growth mixed-method analysis
Product Safety: GRIN Publishing GmbH

Quote paper: Radhika P. Y. (Author), A. Niki Celestica (Author), J. Aishwarya (Author), T. Hemalatha (Author), N. Gayathri (Author), A. Pashupathinath (Author), M. Veera Swamy (Author), M. Arul Jothi (Author), 2024, The Influence of Social Media on Investment Behavior, Brand Equity, and Organizational Growth. A Mixed-Method Analysis, Munich, GRIN Verlag, https://www.grin.com/document/1577602

The Influence of Social Media on Investment Behavior, Brand Equity, and Organizational Growth. A Mixed-Method Analysis

Excerpt

Table of Contents

ABSTRACT

Introduction

1.1 Importance of the Topic

1.2 Need for the Study

1.3 LIMITATIONS OF THE STUDY

1.4 SCOPE OF THE STUDY:

1.5 OBJECTIVES OF THE STUDY

2. REVIEW OF LITERATURE

2.1 RESEARCH GAP

3. RESEARCH METHDOLOGY

4. DATA ANALYSIS AND INTERPRETATION

Analysis of Normality Test (Shapiro-Wilk Test, Normality Curve & Q-Q Plot)

Conclusion:

Steps:

Alternative Hypothesis (H1):

5. FINDINGS

5.1 SUGGESTIONS

5.2 CONCLUSION

6. REFERENCES:

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