Generational Insights into Herding Behavior: The Moderating Role of Investment Experience in Shaping Decisions Among Generations X, Y, and Z

Abstract

Understanding generational differences in herding behavior is crucial for policymakers, financial educators, and market regulators, particularly in emerging markets where retail investor participation is rapidly growing. This study investigates the influence of herding behavior on investment decision-making among Generations X, Y, and Z in Indonesia, as well as the moderating role of investment experience. Using a multi-group structural equation modeling (SEM) approach with data from 1293 retail investors, the research compares behavioral tendencies across cohorts. Results reveal that herding behavior has a positive and significant impact on investment decision-making in all generations, with the strongest effect observed in Generation X, followed by Generation Z and Generation Y. Investment experience significantly weakens herding behavior’s influence for Generation X but shows no significant moderating effect for Generations Y and Z, suggesting that psychological and social influences, particularly from digital platforms, may outweigh experiential learning in younger cohorts. These findings align with behavioral finance theory, which explains herding as a cognitive and emotional bias heightened by market uncertainty. The results provide practical implications for designing targeted financial education programs and regulatory measures to promote independent decision-making and reduce susceptibility to biased market information, especially among younger generations in digitally driven investment environments.

1. Introduction

The capital market plays a pivotal role in facilitating efficient capital allocation and stimulating national economic growth (Sachdeva & Lehal, 2024). In particular, stock investments have become a critical instrument for both individual wealth creation and broader financial market stability (Ah Mand et al., 2023). In Indonesia, the growth of stock investors has increased significantly every year. Indonesian investors project that they will number 15,161,166 million stock investors in 2025 (IDX, 2025). This demographic shift introduces not only quantitative growth but also significant qualitative differences in investor behavior. In line with the growing prominence of behavioral finance, herding behavior has emerged as a recurrent phenomenon, especially among retail investors. Defined as the tendency to mimic others’ investment actions without individual analysis (Banerjee, 1992), herding behavior can lead to irrational decision-making, excessive speculation, and even systemic market risks such as bubbles and crashes (Ayoub & Balawi, 2022; Komalasari et al., 2022). This behavior is particularly pronounced in emerging markets like Indonesia, where information asymmetry and financial literacy remain key issues (Simamora et al., 2024).

Prior studies have confirmed the influence of herding behavior on investment de-cisions (Ahmad & Wu, 2022; Javaira & Hassan, 2015), and recent research acknowledges the role of demographic characteristics in shaping investor psychology (Goyal & Kumar, 2021; Marjerison et al., 2021). While a growing body of literature explores behavioral biases in investment decisions, most studies treat investors as a homogenous group, overlooking intergenerational diversity and psychographic segmentation. Moreover, although investment experience is recognized as a potential buffer against irrational behaviors such as herding (Aziz et al., 2024; Perveen et al., 2020), few empirical studies have examined its moderating role across distinct generational cohorts. Most notably, multi-group comparative analyses involving Generations X, Y, and Z remain scarce, particularly in the context of emerging economies.

Despite the proliferation of behavioral finance studies, there is limited empirical understanding of how herding behavior manifests differently across generational lines, and how investment experience moderates such behavior in these groups. Given the distinct psychological, technological, and social characteristics of Generations X, Y, and Z, there is a strong theoretical rationale to expect variation in both herding tendencies and their consequences on investment decisions. This study addresses this gap by adopting a generational lens and testing the moderating effect of investment experience on the herding–decision-making relationship. The investigation is especially relevant for countries like Indonesia, where digital financial platforms have rapidly altered the investor landscape, particularly among younger cohorts.

Although herding behavior has been widely studied as a key factor influencing investment decisions, most prior research tends to treat retail investors as a homogeneous group, overlooking potential generational differences in behavioral responses. In reality, each generation, X, Y, and Z, exhibits distinct psychological traits and investment approaches, which may lead to varying degrees of susceptibility to herding behavior. Furthermore, while investment experience is believed to play a critical role in reducing irrational tendencies such as herding, limited empirical studies have examined its moderating effect across different generational groups. This gap is particularly evident in the context of emerging markets like Indonesia, where rapid digital transformation and a growing investor base amplify the importance of behavioral in-sights. Thus, this study addresses the core research problem: how does herding behavior influence investment decisions among Generations X, Y, and Z, and to what extent does investment experience moderate this relationship within each generational group?

Understanding generational differences in behavioral tendencies is critical for policymakers, financial educators, and market regulators. Insights from this study can inform targeted investor education, behavioral risk management, and customized interventions that align with the cognitive and experiential profiles of each generation. By introducing a multi-group comparative approach, this study contributes three key novelties to the existing literature on behavioral finance and herding behavior. First, it adopts a generational comparative lens by examining and contrasting the herding tendencies of Generations X, Y, and Z, an approach that departs from the common treatment of retail investors as a homogeneous group and instead highlights intergenerational differences in psychological and social investment drivers within Indonesia’s capital market. Second, the study investigates the moderating role of investment experience, an often-overlooked factor, in shaping the strength of herding behavior across different generations, providing empirical evidence on how experience may buffer irrational tendencies differently across age cohorts. Third, the research contributes to the limited body of knowledge focused on emerging markets by contextualizing the analysis in Indonesia, a country characterized by rapid digital transformation, a burgeoning retail investor base, and distinct demographic shifts, thereby enhancing the practical relevance and policy implications of the findings.

To address the gaps identified in previous studies, particularly the lack of generationally segmented analyses and the overlooked moderating role of investment experience, this study seeks to answer the following research questions:

• To what extent does herding behavior influence investment decision-making among retail investors?
• Does the impact of herding behavior on investment decision-making differ across generational cohorts (Generation X, Y, and Z)?
• To what extent does investment experience moderate the relationship between herding behavior and investment decision-making?
• Does the moderating effect of investment experience vary across different generations of investors?

The remainder of this paper is structured as follows. Section 2 presents a review of relevant literature and the development of research hypotheses. Section 3 outlines the research methodology, including sample selection, data collection, and analytical techniques. Section 4 discusses the results of the empirical analysis. Section 5 provides a comprehensive discussion of the findings in light of existing literature. Finally, Section 6 concludes the study with implications, limitations, and suggestions for future research.

2. Literature Review

Investor behavior in the modern market continues to evolve over time (Cen, 2021). The influence of financial technology, particularly through the emergence of various FinTech-based investment platforms, has transformed the way investors engage in investment activities. This transformation has occurred alongside the increasingly intensive use of technology, which was initially introduced to provide convenience but in reality has also created new challenges for investors (Shahzad et al., 2024). Several studies reveal that technological advancements in investment often cause investors to lose confidence in their own judgment and become more vulnerable to behavioral biases (Abdeldayem & Aldulaimi, 2025). This is reflected, for instance, in the tendency of investors to act more speculatively, make hasty decisions, and follow market trends without sufficient fundamental analysis (Halim & Pamungkas, 2023). Empirical evidence from previous studies indicates that technology can play a dual role (Mishra & Kumar, 2025). On the one hand, technology helps reduce behavioral biases by providing more balanced information. On the other hand, technology also has the potential to accelerate the spread of biases if not used properly.

Behavioral Finance challenges classical finance by integrating psychological insights into investor decision-making. Masood (2024) explores this intersection, showing that cognitive biases, such as overconfidence, anchoring, herding, and loss aversion, are significantly amplified during periods of market volatility, leading to irrational investment behavior and destabilized market outcomes. Masood (2024) argues these biases underscore the importance of tools like behavioral nudges and AI-based advisory systems to help investors combat emotional decision-making. Building on this, C. Chen et al. (2023) develop a theoretical model capturing how feedback trading and investor sentiment drive abnormal volatility. Their framework incorporates four key effects, value, cognitive bias, sentiment shock, and inductive trading, to explain persistent price swings beyond fundamental values. Their findings illustrate that psychological influences systematically contribute to market inefficiencies. Reflecting on volatility, Parashar et al. (2024) uses a GARCH framework to analyze how cognitive biases, including herding, confirmation bias, and loss aversion, tend to exacerbate stock market volatility and sometimes diverge from economic growth paths. Their work underscores the need for an interdisciplinary approach that blends behavioral insights with advanced econometric modeling. Lastly, Dixit (2024) offers empirical evidence linking investor psychology with market dynamics, demonstrating that biases like overconfidence and herding intensify volatility, especially during extreme market conditions, while loss aversion can sometimes act as a mitigating force. Using both quantitative and qualitative methods, this study highlights how psychological factors, particularly when magnified by digital platforms, shape market outcomes and investor behavior. These studies validate Behavioral Finance as a mature theoretical framework that bridges finance, psychology, and quantitative analysis. They establish a strong foundation for research exploring generational differences, herding behavior, and moderating factors like investment experience, especially in emerging markets.

Herding behavior in financial markets refers to a tendency where individuals follow the investment actions of others, often without conducting their own analysis. This behavioral pattern emerges when investors presume that others possess superior information, leading to imitative behavior that can cause deviations from fundamental asset values and amplify market volatility. The phenomenon is especially prevalent among retail investors, who may lack experience or access to reliable market information (Parashar et al., 2024; Shahzad et al., 2024). Scholars have identified several key drivers behind herding. Informational cascades occur when investors disregard their private signals in favor of public actions, assuming the crowd possesses better judgment (Masood, 2024). Reputational concerns also play a significant role, where market participants imitate others to avoid professional or social scrutiny if their independent decisions lead to losses (C. Chen et al., 2023). Moreover, the influence of social networks and digital platforms intensifies herding through psychological mechanisms such as fear of missing out (FoMO) and peer pressure (Ivantchev & Ivantcheva, 2024). Recent empirical studies underscore the relevance of herding behavior in emerging markets. For example, in Indonesia, heuristic biases and social influence factors like FoMO significantly shape retail investor decisions, reinforcing herd tendencies (S. Gupta & Shrivastava, 2022). Similarly, across major stock exchanges, herding has been associated with increased volatility and market inefficiency, particularly during periods of crisis or uncertainty (Shahzad et al., 2024). These findings suggest that herding is not only a persistent behavioral bias but also a systemic issue with implications for investor welfare and market stability.

Herding behavior is understood as a consequence of cognitive and emotional biases, such as FoMO or the desire to avoid regret, which drive investors to mimic others’ actions without conducting thorough analysis (Ivantchev & Ivantcheva, 2024; Jowey et al., 2024; Madaan & Singh, 2019). This behavior can be either rational, based on information, or irrational, driven by emotions or social pressure (Başarir & Yilmaz, 2019). Herding significantly influences investment decision-making, as it may help novice investors reduce uncertainty by following the choices of more experienced peers (Shantha, 2019a). However, herding can also lead to market inefficiencies, including asset bubbles and mass panic, which contribute to heightened price volatility (Ayoub & Balawi, 2022).

The tendency to exhibit herding behavior can vary significantly depending on market conditions (Shantha, 2019b). Different market phases, such as bull and bear markets, influence investors’ propensity to herd, particularly in developing countries (Aziz et al., 2024). Supporting this, Gusni et al. (2024) examined herding behavior among investors in Indonesia, Malaysia, and Thailand, revealing that herding tendencies are more prominent during bear markets. Investors who engage in herding often exhibit groupthink tendencies, displaying a preference for conformity over independent decision-making. This behavior is frequently rooted in a lack of confidence, where individuals defer to the collective because they doubt their own investment judgments. Such herd-driven actions are also fueled by a desire to avoid being wrong alone, as imitating others helps diffuse accountability and psychological risk (Almansour et al., 2023).

The development of digital platforms in the financial sector has brought significant changes, offering various benefits such as ease of access, efficiency, and investment product diversification. However, this development also highlights that digital transformation introduces new risks that investors must anticipate. D. Kumar et al. (2025) show that the intensive use of financial technology can increase vulnerability to financial fraud and the misuse of information. Therefore, adequate detection systems and digital security measures are required to maintain investor trust while strengthening the sustainability of technology-based investment platforms. In addition, although investment technology opens many opportunities for investors, this condition can also become a weakness if not accompanied by sufficient knowledge (Simonn, 2025). Beginner investors often enter the market with limited understanding of risks, valuation, and fundamental analysis. This situation creates a “knowledge gap,” where ease of access is not matched by the ability to make wise investment decisions. The role of technology, such as investment platforms, is crucial in preventing herding. Adequate security systems not only serve to protect investors from the risk of financial fraud but also prevent the misuse of information that could reinforce behavioral biases such as herding (Jafri et al., 2024). The integration of financial fraud detection with cybersecurity mechanisms has been proven to reduce investors’ vulnerability to misleading information. Thus, the implementation of secure technology contributes to mitigating the tendency toward herding behavior, enabling investment decisions to be made more rationally.

Generational cohorts, namely Generation X (born 1965–1980), Millennials or Generation Y (1981–1996), and Generation Z (1997–2012), differ significantly in their investment approaches due to varying life stages, technological exposure, and socio-economic contexts (Rodrigues & Gopalakrishna, 2024). Research has consistently shown that Millennials and Generation Z display higher financial risk tolerance compared to Generation X, a trend attributable to their greater openness to new experiences and digital fluency) (Rodrigues & Gopalakrishna, 2024). Personality analyses further reveal that traits such as openness, extraversion, and neuroticism significantly predict generational risk behavior, Millennials and Gen Z exhibit stronger correlations between these traits and risk tolerance than do older cohorts (Rahman & Gan, 2020). Psychologically, each generation displays unique orientations toward financial decision-making. Research highlights that younger cohorts (Millennials and Gen Z) are more susceptible to cognitive biases like overconfidence, availability heuristics, and the Dunning–Kruger effect, while older groups such as Gen X demonstrate greater status quo and confirmation bias (Da Silva et al., 2024). Empirical surveys from emerging markets corroborate these generational divergences. A study in India found that Gen Z displayed the highest risk tolerance, followed by Millennials and then Gen X, illustrating a clear gradient of increasing risk appetite among younger investors (Savithri & Rajakumari, 2025). Younger generations are more risk-tolerant and psychologically predisposed to behavioral biases, facilitated by their deep engagement with digital tools. In contrast, Generation X exhibits more conservative, status quo-aligned decision-making, suggesting that generational identity is a fundamental determinant of investment behavior, a key insight for understanding intergenerational differences in herding and the moderating effects of investment experience.

A. Kumar (2024) found that Generation X investors often follow decisions made by more experienced peers or those with superior information access, driven by a desire to minimize risk and maintain financial stability. They are also more prone to herding in traditional investment instruments such as blue-chip stocks and real estate, favoring assets perceived as safe and stable (Antwi & Naanwaab, 2022). In contrast, Generation Y investors are more susceptible to herding due to social media influence and FoMO (Altaf & Jan, 2023). Martaningrat and Kurniawan (2024) observed that Gen Y tends to be influenced by online communities and financial influencers, and shows greater risk tolerance compared to Gen X, often making more aggressive investment decisions (Killins, 2023). Meanwhile, Generation Z exhibits even stronger herding behavior, often following others’ investment choices without conducting thorough analysis (Maheshwari & Samantaray, 2025). This pattern is largely attributed to Gen Z’s deep exposure to digital media and their reliance on social media trends and influencer opinions (Ahuja & Grover, 2023). The adoption patterns of young investors, such as Generation Z, share similarities with the use of other digital financial services, such as mobile banking. This is because, in utilizing technology-based platforms, this generation tends to emphasize aspects of accessibility, application convenience, and reliance on technology. Findings by Addula (2025) clarify that factors such as social influence, compatibility, digital self-efficacy, and perceived cost play an important role in driving the intention and decision to use digital services. Accordingly, the adoption of similar financial platforms can serve as a trigger for Generation Z to invest through digital technology. Nevertheless, although this trend indicates a significant opportunity, an in-depth understanding of how these factors contribute within the context of digital investment remains limited. Furthermore, reliance on social influence makes this generation relatively vulnerable to herding behavior.

In the realm of stock investing, herding behavior occurs when investors choose to follow the majority decision to avoid the risk of making incorrect individual choices (Rahayu et al., 2021). A study by Chiang and Zheng (2010) indicates that herding behavior is more prevalent in emerging markets than in developed countries, as investors in these markets tend to rely more on collective information than on individual analysis. However, the influence of herding behavior on investment decisions can be either strengthened or weakened by an investor’s level of experience (Aziz et al., 2024). Investment experience refers to the expertise and knowledge an investor possesses when making investment decisions (Krische, 2019). Experienced investors are generally more rational in analyzing market information and are less susceptible to herding behavior (Shantha, 2019a). However, Cheng (2018) points out that experienced investors can also fall prey to confirmation bias, as they may ignore information that contradicts their established beliefs. According to Fernández et al. (2011), the effect of herding behavior can be magnified by increasing uncertainty, prompting investors to seek certainty through the actions of others. Thus, investment experience plays a crucial role in moderating the impact of herding behavior, depending on how investors leverage their expertise to manage risks and make informed decisions (Koç Ustali et al., 2025).

Research has shown that investors with significant experience are better equipped to assess investment risks and opportunities, making them more likely to conduct independent analyses rather than simply following the crowd (Niculaescu et al., 2023). In contrast, inexperienced investors are more vulnerable to herding behavior due to their limited information and uncertainty in decision-making (Ahmad & Wu, 2022; Daud et al., 2023). Therefore, investment experience is a key factor in moderating the relationship between herding behavior and investment decision-making. Trisno and Vidayana (2023) note that novice investors tend to be more influenced by herding behavior, largely due to their lack of knowledge and confidence. Conversely, more experienced investors generally act more rationally and are less swayed by herding behavior, as they possess superior analytical skills and greater confidence in their investment decisions (Shantha, 2019a).

In summary, the literature clearly establishes the influence of herding behavior on investment decisions and suggests generational differences in susceptibility. Moreover, investment experience emerges as a moderating variable that can either amplify or mitigate the impact of herding. However, few studies have empirically examined the interaction between these variables within a unified model, particularly in the context of Indonesia’s rapidly evolving investor landscape.

Building upon this theoretical foundation, to examine the influence of herding behavior on investment decision-making, this study formulates one main hypothesis and several sub-hypotheses based on generational differences. The main hypothesis (H1) addresses the overall impact of herding behavior, while the sub-hypotheses (H1a, H1b, and H1c) explore whether this relationship holds true within specific generational cohorts, namely Generation X, Y, and Z.

• Main hypothesis

H1. 

Herding behavior has a positive impact on investment decision-making.

2.

Sub-Hypotheses

• H1a: Herding behavior has a positive impact on investment decision-making among Generation X investors.
• H1b: Herding behavior has a positive impact on investment decision-making among Generation Y investors.
• H1c: Herding behavior has a positive impact on investment decision-making among Generation Z investors.

In addition, this study investigates whether investment experience moderates the relationship between herding behavior and investment decision-making. The second main hypothesis (H2) considers the moderating effect of investment experience across all investors, while the sub-hypotheses (H2a, H2b, and H2c) assess this moderating effect within each generational group.

• Main hypothesis

H2. 

Investment experience moderates the relationship between herding behavior and investment decision-making.

2.

Sub-Hypotheses

• H2a: Investment experience moderates the relationship between herding behavior and investment decision-making among Generation X investors.
• H2b: Investment experience moderates the relationship between herding behavior and investment decision-making among Generation Y investors.
• H2c: Investment experience moderates the relationship between herding behavior and investment decision-making among Generation Z investors.

In summary, the literature highlights the significant influence of herding behavior on investment decision-making, particularly during periods of market uncertainty. Previous studies have acknowledged that psychological and generational factors may shape how investors respond to market trends and peer influence. However, the role of investment experience as a moderating variable remains underexplored, especially across different generational cohorts. By incorporating both the main and sub-hypotheses, this study aims to fill this gap and provide a more nuanced understanding of how behavioral biases interact with experiential and generational factors in shaping investment decisions. This framework sets the foundation for the empirical analysis that follows.

3. Results

Based on the respondent profile shown in

Table 1. Convergent Validity.

Indicator Notation	Loading Factor All Model	Loading Factor Gen X	Loading Factor Gen Y	Loading Factor Gen Z
HB1	0.826	0.854	0.827	0.805
HB2	0.859	0.899	0.819	0.851
HB3	0.792	0.853	0.751	0.781
IE1	0.803	0.871	0.840	0.864
IE2	0.876	0.879	0.882	0.862
IE3	0.874	0.874	0.863	0.851
IE4	0.868	0.843	0.849	0.864
IE5	0.807	0.857	0.791	0.864
IDM1	0.865	0.876	0.864	0.862
IDM2	0.868	0.876	0.863	0.851
IDM3	0.869	0.887	0.880	0.864
IDM4	0.844	0.901	0.830	0.738
IDM5	0.822	0.855	0.853	0.864

Note: IE = Investment Experience, HB = Herding Behavior, IDM = Investor Decision-Making. Source: Data Processed, 2025.

, the results of the descriptive analysis indicate several key demographic patterns. In terms of sex, the majority of respondents were male, suggesting that men may have a higher level of participation or interest in the subject of the study compared to women. This gender distribution provides insight into potential behavioral or perceptual differences that may arise in subsequent analyses.

Further analysis reveals that the generational composition was fairly balanced, with Generation Z (36%) slightly outnumbering Generation Y (35%) and Generation X (32%). This indicates a broad representation across age groups, which may reflect varying levels of experience, preferences, or motivations relevant to the study. The educational background of respondents shows that most participants had attained at least a bachelor’s degree, with 52% holding this qualification, suggesting a relatively high level of educational attainment among the sample. In terms of monthly income, the largest portion of respondents fell within the IDR 1,000,000 to IDR 10,000,000 range, reflecting a dominant representation from the lower to middle-income groups. Lastly, with regard to investment experience, the majority of respondents had between 6 to 10 years of experience, indicating a mature group of participants with practical exposure to financial or investment-related activities. These demographic findings provide a solid foundation for interpreting the results in the context of the respondents’ background characteristics.

3.1. Outer Model

In the partial least squares structural equation modeling (PLS-SEM) analysis conducted with SmartPLS, the outer model represents the relationship between latent constructs and the indicators that measure them (Sarstedt et al., 2020). The outer model is essential for testing the validity and reliability of the measurements, ensuring that the latent constructs are accurately represented by the indicators used (J. J. F. Hair et al., 2014). The evaluation of the outer model includes tests for convergent validity, discriminant validity, and reliability.

3.1.1. Convergent Validity

Convergent validity is assessed by examining the loading factor of each indicator with the latent variable it measures (J. F. Hair et al., 2019). The results displayed all indicators have loading factors above 0.70, indicating that each indicator significantly contributes to measuring its respective construct. The loading factor values in the overall model range from 0.792 to 0.876, indicating a strong relationship between the indicators and the latent variables they measure. Additionally, convergent validity is tested across generation groups (Generation X, Generation Y, and Generation Z) to confirm that the model remains valid for each group of respondents. Therefore, all indicators in this study demonstrate good convergent validity, making them suitable for further analysis in the structural model. The results of the convergent validity are shown in Table 1.

3.1.2. Discriminant Validity

Discriminant validity is used in this study to ensure that each construct measured in the model addresses distinct concepts and does not overlap with others. This is tested using the Fornell-Larcker Criterion, which states that the square root of the average variance extracted (AVE) must be greater than the correlation between constructs (Cheung et al., 2024). The analysis indicates that all variables in the model have a higher AVE square root value than their correlations with other variables. The results are shown in

Table 2. Discriminant Validity.

	Herding Behavior	Investment Experience	Investor Decision-Making
Herding Behavior	0.826	-	-
Investment Experience	0.675	0.846	-
Investor Decision-Making	0.689	0.852	0.854
Fornell-Larcker Criterion Values of Generation X Group
Herding Behavior	0.869	-	-
Investment Experience	0.734	0.879	-
Investor Decision-Making	0.778	0.865	0.865
Fornell-Larcker Criterion Values of Generation Y Group
Herding Behavior	0.800	-	-
Investment Experience	0.514	0.846	-
Investor Decision-Making	0.515	0.845	0.858
Fornell-Larcker Criterion Values of Generation Z Group
Herding Behavior	0.812	-	-
Investment Experience	0.791	0.813	-
Investor Decision-Making	0.792	0.848	0.837

Source: Data Processed, 2025.

.

3.1.3. Reliability Test

The reliability test measures the consistency of the measurement instruments used in this research questionnaire. The reliability of each construct is assessed using the values of Cronbach’s alpha, composite reliability (CR), and AVE. The analysis for the overall model and each generation group reveals that all constructs meet these reliability criteria: (1) Cronbach’s alpha > 0.7, indicating good internal consistency for each construct; (2) CR > 0.7, confirming that all indicators consistently measure their respective constructs; and (3) AVE > 0.5, indicating that each construct has satisfactory reliability (Cheung et al., 2024). Thus, each construct has an adequate level of variance to explain the latent variables. The results are presented in

Table 3. Reliability Test.

	Cronbach’s Alpha	Composite Reliability	Average Variance Extracted (AVE)	Result
Reliability Test for All Model
Herding Behavior	0.767	0.866	0.683	Reliability confirmed
Investment Experience	0.900	0.926	0.716	Reliability confirmed
Investor Decision-Making	0.907	0.931	0.729	Reliability confirmed
Reliability Test for Generation X Group
Herding Behavior	0.837	0.902	0.755	Reliability confirmed
Investment Experience	0.926	0.944	0.773	Reliability confirmed
Investor Decision-Making	0.916	0.937	0.748	Reliability confirmed
Reliability Test for Generation Y Group
Herding Behavior	0.718	0.842	0.640	Reliability confirmed
Investment Experience	0.900	0.926	0.715	Reliability confirmed
Investor Decision-Making	0.910	0.933	0.736	Reliability confirmed
Reliability Test for Generation Z Group
Herding Behavior	0.742	0.853	0.660	Reliability confirmed
Investment Experience	0.869	0.906	0.661	Reliability confirmed
Investor Decision-Making	0.893	0.921	0.701	Reliability confirmed

Source: Data Processed, 2025.

.

3.2. Inner Model

In PLS-SEM analysis using Smart-PLS 03 software, the inner model test represents the relationships between latent constructs in the study. It illustrates how independent variables impact dependent variables within the research model (J. F. J. Hair et al., 2014).

3.2.1. R-Squared (R²) Value

The R² analysis in this study was used to evaluate how much of the variability in the dependent variables can be explained by the independent variables in the research model (J. F. Hair et al., 2021). According to the analysis results, the R² value for Investor Decision-Making in the overall model was 0.751, indicating that the independent variables can account for 75.1% of the variability in Investor Decision-Making. The remaining variability is influenced by other factors not included in the model. Specifically, Generation X has an R² of 0.800, meaning that independent variables explain 80% of the variability in Investor Decision-Making. Generation Y has an R² of 0.723, indicating that independent factors explain 72.3% of the variability in investment decisions. Generation Z has an R² of 0.760, indicating that the variables in this model can explain 76% of the variability in Investor Decision-Making. Generally, a high R² value (>0.70) signifies that the research model has a strong predictive ability to explain investment decisions across generational groups. This suggests that the factors considered in this study significantly influence investment decision-making among respondents. The R² values for this study are summarized in

Table 4. R-squared (R²) Test.

	R-Squared (R2)	Adjusted R-Squared
R2 Result of All Model
Investor Decision-Making	0.751	0.751
R2 Results of Generation X Group
Investor Decision-Making	0.800	0.798
R2 Results of Generation X Group
Investor Decision-Making	0.723	0.721
R2 Results of Generation X Group
Investor Decision-Making	0.760	0.758

Source: Data Processed, 2025.

.

3.2.2. Hypothesis Test

Hypothesis testing in PLS-SEM aims to investigate the relationships between latent variables in the structural model. This test relies on the p-value associated with each hypothesis. A hypothesis is accepted in this study if the p-value is less than 0.05, indicating statistical significance at a 95% confidence level (Sarstedt et al., 2019).

Table 5. Hypothesis Test.

Hypothesis Notation	Hypothesis All Modell	β	p Values	Results
H1	Herding Behavior => Investor Decision-Making	0.197	0.000	Accepted
H2	Herding Behavior => Investor Decision-Making => Investment Experience	−0.025	0.015	Accepted
	Hypothesis Gen X Group
H1a	Herding Behavior => Investor Decision-Making	0.284	0.000	Accepted
H2a	Herding Behavior => Investor Decision-Making => Investment Experience	−0.059	0.000	Accepted
	Hypothesis Gen Y Group
H1b	Herding Behavior => Investor Decision-Making	0.107	0.001	Accepted
H2b	Herding Behavior => Investor Decision-Making => Investment Experience	−0.004	0.822	Rejected
	Hypothesis Gen Z Group
H1c	Herding Behavior => Investor Decision-Making	0.318	0.000	Accepted
H2c	Herding Behavior => Investor Decision-Making => Investment Experience	−0.031	0.146	Rejected

Source: Data processed, 2025.

presents the results of the research hypothesis testing, including the beta coefficient value (β), which reflects the strength and direction of the relationship between independent and dependent variables, along with the p-value, which indicates the statistical significance of these relationships.

Based on Table 5, the hypothesis testing results for H1, H1a, H1b, and H1c show p-values of 0.000 for H1, 0.000 for H1a, 0.001 for H1b, and 0.000 for H1c, respectively. All of these p-values are smaller than 0.05, indicating that herding behavior has a positive and significant effect on investor decision-making, both in the overall model and within the Generations X, Y, and Z groups. Furthermore, the hypothesis testing results for H2, H2a, H2b, and H2c show p-values of 0.015 for H2, 0.000 for H2a, 0.822 for H2b, and 0.146 for H2c. This indicates that investment experience significantly moderates the relationship between herding behavior and investor decision-making only in the overall model and within Generation X (p < 0.05), with a negative direction of influence (β = −0.025 and β = −0.059). In Generations Y and Z, this moderating effect is not significant (p-value > 0.05), suggesting that investment experience does not play an important role as a moderating factor in these two groups.

4. Discussion

4.1. Herding Behavior Effect on Investment Decision-Making

Across all models, herding behavior positively and significantly affects investment decision-making (β = 0.197; p = 0.000). This finding indicates that as herding behavior increases, investors are more likely to make decisions based on the actions of the majority within Generations X, Y, and Z. This can be explained by the fact that the current investment market, especially in developing countries characterized by high volatility and uncertainty, encourages investors to follow the majority. Interestingly, this phenomenon occurs across all investor generations. Although Generation X is considered to have sufficient investment experience, they still tend to adopt a safer approach by following the market majority. These findings align with the perspective of behavioral finance, which asserts that herding behavior can occur in any generation due to various factors (Banerjee, 1992). One of these factors is market uncertainty, which triggers cognitive biases whereby investors tend to disregard personal analysis or fundamental information and choose to follow the majority’s decisions. In addition, the indicator of dependence on external advice or recommendations when making investment decisions becomes a key factor that strengthens the influence of herding behavior across all generational groups. This condition shows that investors, whether experienced or novice, increasingly rely on external recommendations, such as market analysts, financial influencers, or online communities, rather than conducting independent analysis.

This result aligns with the study by Yang et al. (2025), which demonstrated that herding behavior significantly impacts investment decisions, particularly among retail investors who may lack confidence in their independent analyses. Furthermore, this finding supports the research by (Shah et al., 2024), which noted that investors tend to imitate the decision-making processes of others, especially in developing countries like Indonesia (Rahayu et al., 2021). Xu (2017) emphasizes that herding behavior often arises from cognitive biases, such as regret aversion, that drive individuals to follow the majority to avoid regret. From a behavioral finance perspective, this finding is consistent with the concept of herding behavior (Ritter, 2003). This theory posits that due to uncertainty in financial markets, investors frequently disregard personal information and opt to follow group actions. It also acknowledges that investors do not always act rationally and are influenced by psychological and social factors (Banerjee, 1992). One such factor is the tendency to mimic the actions of others, particularly during uncertain situations. When faced with investment market uncertainty, investors often look to the actions of other investors for guidance, assuming that the majority must be correct (Spyrou, 2013). Herding behavior can thus be seen as a psychological defense mechanism that helps reduce risk and uncertainty, particularly among less experienced investors. This finding applies to Generations X, Y, and Z in their investment decision-making processes.

In Generation X, herding behavior significantly influences investor decision-making, with a coefficient of β = 0.284 (p = 0.000). In general, Generation X investors are known to be emotionally stable and mature in their investment decision-making. However, precisely because of this stability and maturity, they tend to be more realistic in assessing risks. In the context of highly volatile markets, full of uncertainty, and influenced by economic sentiments and issues, especially in developing countries like Indonesia, Generation X investors prefer to follow the majority rather than make speculative decisions. This finding is further reinforced by the indicator of dependence on external advice or recommendations when making investment decisions, which serves as a key factor driving herding behavior among this generational group. This indicates that Generation X investors are particularly susceptible to herding behavior when making investment decisions. The findings align with the research by (Gurunathan & Lakshmi, 2023), which confirms that herding behavior impacts Generation X’s investment choices. Generally, Generation X investors demonstrate emotional stability in their investment decisions, often choosing not to follow the majority of other investors (Altaf & Jan, 2023). This study suggests that relatively experienced investors can still exhibit herding behavior. Current uncertain market conditions may lead Generation X investors to prioritize herding behavior, particularly by following the decisions of high-reputation institutions or individuals.

In Generation Y, the influence of herding behavior on investment decision-making is less pronounced than in Generation X but still significant, with a coefficient of β = 0.107 (p = 0.001). This finding shows that the characteristics of Generation Y, who are highly open to technology, make them more capable of accessing various relevant information before making investment decisions through multiple digital sources. Their openness to financial information tends to encourage this generation of investors to exhibit herding behavior in their investment decision-making. The abundance of available information can make individuals hesitant in making important decisions. Exposure to various advice, recommendations, and market trends simultaneously often creates confusion, leading Generation Y investors to follow the majority or group decisions as a safe strategy in investment decision-making. This study aligns with the theory of social influence, which states that individuals’ thoughts and behaviors are affected by others (Gong & Diao, 2023). The findings indicate that Generation Y is more influenced by informational influence, which is the tendency to imitate others’ decisions when facing uncertainty or new situations, thereby increasing their likelihood of engaging in herding behavior.

This indicates that Generation Y is affected by herding behavior, albeit to a lesser extent. These results are consistent with findings from Bharata et al. (2023), which reveal that many millennial investors in Indonesia possess low financial literacy but still invest in stocks by mimicking the majority and following stock influencers without conducting thorough analyses. Herding behavior is particularly reinforced by social media, where millennials seek validation and information from influencers and peers (Saputra & Maradona, 2023). This trend aligns with the characteristics of Generation Y, known for its preference for technology and digital information. While they demonstrate a smaller effect coefficient, indicating more independent research in their investment decisions, herding behavior remains significant due to psychological factors such as uncertainty and low self-confidence when facing market volatility (Ramnath et al., 2008). From the perspective of the behavioral finance theory, herding behavior reflects cognitive and emotional biases that affect investment decisions. This theory posits that investors often imitate the actions of others as a heuristic to simplify their decision-making process (Kahneman & Tversky, 1979). Although Generation Y demonstrates herding behavior at a lower level, this tendency persists due to social and emotional factors, like the desire to avoid regret (regret aversion) or the need to feel secure by following the majority (Korteling et al., 2023).

In Generation Z, herding behavior has a more decisive influence than in Generation Y but remains lower than in Generation X, with a coefficient of β = 0.318 (p = 0.000). The findings of this study indicate that Generation Z investors are still relatively young and new to the world of investing. With limited experience, this generation is considered more vulnerable to market uncertainty. To reduce risk, they tend to rely on herding behavior in their investment decision-making, particularly when they perceive the actions of the majority as a valid source of information. As digital natives, their openness to information through social media and online platforms further reinforces the tendency to follow the majority. Psychological factors, including lack of experience and the desire to achieve optimal outcomes, often lead Generation Z to make investment decisions without strong fundamental analysis. This behavior becomes particularly dominant in financial markets due to the high level of uncertainty, causing Generation Z investors to frequently view the majority as the main guide in their investment decision-making. This indicates that Generation Z investors are more inclined to follow the behavior of the majority in their investment decisions. The findings confirm the research by Angela et al. (2023); Kaban and Linata (2024), which highlight the substantial impact of herding bias on investment choices among Generation Z investors in Indonesia.

This generation is more likely to be swayed by group behavior, especially during market uncertainty. The study also supports Juwita et al. (2022), which found that Gen Z investors tend to display irrational behaviors in their investment decisions, particularly regarding cryptocurrencies, as others and the oversimplification of available information often influence their choices. Generation Z, born between 1997 and 2012, is considered a digital native generation that relies extensively on platforms such as Instagram, TikTok, and YouTube for information, favoring them over traditional sources like television or newspapers (Shan et al., 2023). Social media serves as an information source and influences investment decisions through viral trends. From a behavioral finance perspective, herding behavior in Generation Z can be understood through social proof and the availability heuristic. According to Pham et al. (2023), social proof suggests that individuals often imitate others’ actions in uncertain situations, especially when they perceive those individuals to have more accurate information. Generation Z closely aligns with this concept, frequently relying on recommendations from influencers or online communities for investment information. In addition, the availability heuristic of Pašiušienė et al. (2023) shows that Generation Z is more likely to rely on easily accessible and viral information rather than conducting in-depth analysis, thus increasing the tendency for herding behavior.

4.2. Herding Behavior Effect on Investment Decision-Making with Investment Experience Moderation

Overall, across all models, investment experience negatively modifies the relationship between herding behavior and investor decision-making (β = −0.025; p = 0.015). The findings of this study indicate that herding behavior does occur among the majority of investors; however, its influence can be mitigated by investment experience, especially in developing countries. From a behavioral finance perspective, investment experience plays a crucial role in building investors’ confidence to conduct personal analysis. More experienced investors tend to be capable of developing their own investment strategies and making independent decisions without being overly influenced by the actions of the majority. Amid highly volatile and uncertain market conditions, investment experience also functions as a mechanism to control cognitive biases. Experienced investors are able to evaluate risks more thoroughly, thereby reducing the likelihood of impulsive decisions driven by herding behavior. In addition, experience fosters a tendency for investors to act more independently in decision-making, which in turn strengthens their ability to navigate market uncertainty.

This indicates that as a person’s investment experience increases, the influence of herding behavior on their investment decisions weakens. This finding aligns with previous studies suggesting that investment experience can diminish the impact of behavioral biases in decision-making. For instance, research by Sabir et al. (2019) demonstrates that individuals with more excellent investment experience tend to rely more on rational analysis than following the majority’s decisions. Investors with more experience generally exhibit higher confidence in their decision-making and are less swayed by herding behavior, as they rely on their knowledge and analysis (Menkhoff et al., 2006; Zhao & Zhang, 2021). Investment experience helps mitigate cognitive biases, such as herding behavior, thereby enhancing the quality of decision-making (Ani & Özarı, 2020). The adverse moderating effect of investment experience can be attributed to more seasoned investors’ improved cognitive and emotional abilities and being better equipped to process information and manage their emotions concerning investments.

This broader experience allows them to understand market dynamics better, recognize potential information cascades or trends, and reduce the psychological biases that encourage copycat behavior (Gayathir & Sathya, 2024). Experienced investors tend to be more adept at conducting fundamental and technical analyses independently, making their investment decisions more on rational evaluations rather than transient market sentiment. From the perspective of the behavioral finance theory, the concepts of overconfidence bias and availability heuristics help explain this finding. Experienced investors typically exhibit a higher level of confidence in their market analysis capabilities (overconfidence bias), which reduces their dependence on the actions of others (Barber & Odean, 2001). Additionally, with more excellent investment experience, investors can rely on more relevant and reliable information, thus diminishing their susceptibility to herding behavior, which often stems from incomplete or biased information (Kahneman & Tversky, 1979). However, it is also recognized that in circumstances of extreme uncertainty, even experienced investors may succumb to herding behavior as a heuristic to simplify decision-making (Singh, 2024).

For Generation X, the moderating effect of investment experience is more significant (β = −0.059; p = 0.000), suggesting that investment experience is particularly effective at reducing the impact of herding behavior within this group. The findings indicate that the influence of herding on investment decisions among Generation X investors can be mitigated by investment experience. This occurs because Generation X investors generally have longer investment histories and a better understanding of market conditions, enabling them to reduce the tendency to follow the majority impulsively. As a more mature generation, Generation X investors also possess strong emotional control, allowing them to conduct rational analyses and make more independent investment decisions. This finding aligns with Sawitri et al. (2025) research, which indicates that Generation X, having generally longer investment experience and higher financial literacy than younger generations, tends to make more rational investment decisions, thereby mitigating the influence of behavioral biases like herding behavior.

The increased financial literacy that results from extensive investment experience can help reduce the adverse effects of herding by enabling investors to make more informed decisions (Aravind & Pullot, 2024; Sabir et al., 2019). Gong and Diao (2023) also find that older and more experienced investors, such as those from Generation X, typically make more rational investment choices by relying on the knowledge and experience they have accumulated over the years. Furthermore, Generation X tends to be more conservative and cautious in their investment strategies, having lived through various economic upheavals, such as the 2008 financial crisis (Tolani et al., 2020). Their experiences make them more inclined to trust their analysis rather than being swayed by market trends or public opinion. Their lengthy investment experience makes them more confident in their decision-making (Savithri & Rajakumari, 2025). Consequently, they are less affected by short-term market fluctuations and focus more on long-term financial goals, which reduces their tendency to engage in herding behavior when making investment decisions.

In Generation Y, the moderating effect of investment experience on herding behavior is insignificant (β = −0.004; p = 0.822). The findings indicate that investment experience in Generation Y does not reduce the influence of herding behavior on investment decisions. This is due to their openness to technology and broad access to information, which keeps herding behavior high. Although Generation Y generally has more investment experience compared to Generation Z, they tend to lack confidence in making investment decisions without first considering advice or recommendations from external sources. This condition underscores that social factors and external information continue to play a strong role in influencing the investment behavior of Generation Y. These findings align with the theory of social influence, which states that strong social pressure drives individuals to conform to their group. In the context of investing, this conformity leads Generation Y investors to follow majority decisions or external recommendations, especially when facing market uncertainty. This indicates that investment experience does not significantly diminish herding behavior’s impact on Generation Y’s investment decisions. The lack of a significant moderating effect suggests that the psychological bias driving herding behavior may be more substantial within this group and that their investment experience has not effectively mitigated it.

Generation Y, influenced by the digital era, tends to rely on online platforms and social media to obtain investment information (Barber & Odean, 2001). While they can access a broader range of information, the tendency to follow social and emotional trends can overpower their investment experience. With limited investment experience and a reliance on social media for information, Generation Z is often more willing to take risks (Prasarry et al., 2023). Growing up in the digital age, they are more active in sharing information and following trends on social media, which can enhance both informational and normative herding effects (Banerjee, 1992). As a result, despite having access to various sources of investment information, Generation Y may be more susceptible to market sentiment and recommendations from their social networks. Hence, limited investment experience provides inadequate cognitive protection against herding effects. Furthermore, some studies indicate that Generation Y may possess high levels of self-confidence, but their financial literacy remains underdeveloped. This can make them vulnerable to overconfidence bias and herding driven by unverified information (S. Gupta & Shrivastava, 2022).

Similarly, in Generation Z, the moderating effect of investment experience is also insignificant (β = −0.031; p = 0.146), suggesting that it does not significantly lessen herding behavior’s influence on their investment decisions. This study shows that herding behavior among Generation Z in investment decision-making cannot be mitigated by investment experience. This occurs because their experience is still relatively limited compared to previous generations, making them more reliant on advice and recommendations from external sources when making decisions. Psychological factors also play a role, as young Generation Z investors tend to be less emotionally stable in making investment decisions. Additionally, this generation has a tendency to seek quick or instant results, such as high investment returns, but with limited experience, herding becomes a dominant factor in their decisions. Massive exposure to social media further reinforces this behavior, making Generation Z highly vulnerable to market uncertainty and more likely to follow the majority or group decisions as a safe strategy in investment decision-making. This outcome contrasts with previous studies that suggested investment experience could reduce the tendency toward herding behavior, as more experienced investors typically exhibit greater confidence in their decision-making (Prasetyo et al., 2023).

However, this finding may reflect the unique characteristics of Generation Z, notably influenced by information from social media and online communities, potentially strengthening herding behavior, even among those with investment experience. Previous research has shown that Generation Z often relies on peers or influencers when making investment decisions, which can lead to diminished effects of investment experience on reducing herding behavior (P. Gupta & Goyal, 2024; Kaban & Linata, 2024). Despite their investment experience, millennials frequently display biases such as emotional reactions that can result in herding behavior, the tendency to follow the actions of others rather than making independent decisions. Emotional responses, usually called the fear of missing out (FoMO), may prompt Generation Z to imitate the investment choices of those around them, regardless of their investment experience (Manjusha & Bhooshetty, 2024).

5. Materials and Methodology

This study employs a quantitative explanatory research design to examine the relationships between psychological variables, herding behavior and investment experience, and how these differ across Generations X, Y, and Z. The explanatory approach is suitable for testing causal links and theoretical assumptions in behavioral finance. A Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used due to its ability to handle complex models, small or non-normal samples, and both reflective and formative constructs. This approach ensures accurate estimation of latent variables and robust assessment of measurement reliability and validity. To explore generational differences, Multi-Group Analysis (MGA) is applied within the PLS-SEM framework. MGA enables the comparison of path coefficients across generational groups, identifying potential moderating effects of age-related cohorts on psychological investment behaviors. The goal of MGA in this study is to evaluate differences in the influence of structural path relationships across Generations X, Y, and Z. Specifically, MGA is used to assess whether the moderating effect of investment experience on herding behavior significantly differs among these generational groups. The MGA technique employed in this study includes either the MGA-Permutation Test or MGA-Henseler method, both of which are appropriate for detecting group-specific differences in structural paths within the PLS-SEM framework. This choice is justified by the need to identify whether investment experience plays a significantly different moderating role in shaping herding behavior across generational cohorts. This design supports the study’s dual goals: assessing the impact of psychological constructs on investment decisions and identifying generational variation in these relationships. Using PLS-SEM alongside the MGA technique aims to identify any significant differences in the structural model among the groups being compared (Cheah et al., 2020). Overall, a quantitative explanatory design using PLS-SEM and MGA allows for rigorous testing of hypothesized relationships while accommodating measurement complexity and group-specific variation (Matthews, 2017).

5.1. Population and Sample

The population of this study comprises all individual stock investors registered with the Indonesia Stock Exchange (IDX), totalling 15,161,166 Single Investor Identification (SID) accounts. A purposive sampling technique was applied to ensure that respondents met the inclusion criteria: belonging to Generation X (born 1965–1980), Generation Y (born 1981–1996), or Generation Z (born 1997–2012), and having actively invested in the stock market within the past year. A total of 1338 valid responses were collected.

All participants received an informed consent statement prior to data collection, which outlined the study’s objectives, procedures, potential risks, and benefits. Participation was entirely voluntary, and only those who provided consent were able to proceed with the survey. The questionnaire was administered online via Google Forms, using a 7-point Likert scale to measure respondents’ perceptions and attitudes (Yusoff & Janor, 2014). Confidentiality and anonymity were strictly maintained, and data were securely stored. To provide an overview of respondents’ characteristics, descriptive statistics were employed, with results presented in

Table 6. Respondents Profile.

Variables	Frequencies	Percentages
Sex
Man	785	61%
Woman	508	39%
Generations
X Generations	420	32%
Y Generations	457	35%
Z Generations	461	36%
Education
Doctoral	89	7%
Master	120	9%
Bachelor	678	52%
High School	406	31%
Income (IDR)
IDR 1,000,000–10,000,000	550	43%
IDR 11,000,000–20,000,000	300	23%
IDR 21,000,000–30,000,000	278	22%
>IDR 31,000,000	165	13%
Invesment Experience
1–5 Year	423	33%
6–10 Year	480	37%
>11 Year	393	30%

.

5.2. Variable Operationalization

Variable operationalization in this study was guided by prior literature to ensure content validity. The measurement items for herding behavior were adapted from previous studies that conceptualize social media influence as a direct behavioral tendency in digital investment contexts (Q. Chen et al., 2010). The wording of the items was refined to capture actual behavior, such as the tendency to follow investment decisions of others on social media, to ensure construct validity.

Variable operationalization refers to the process of defining how each construct in a study is measured and quantified through observable and measurable indicators. In this study, the herding behavior variable is treated as an independent variable, measured using three indicator items (Kumari et al., 2019). The investment experience variable functions as a moderating variable, which affects the strength or direction of the relationship between the independent and dependent variables, and is measured using five indicator items (Vohra, 2023). Investment experience in this study is conceptualized not only as the duration or frequency of investment activities but also as the breadth of exposure to the investment ecosystem. Specifically, five indicators are employed: intermediaries, regulator, issuing company, use of technology, and investment instruments. This conceptualization is consistent with previous literature that highlights investor experience as a multifaceted construct. Interaction with intermediaries and regulators provides investors with knowledge about rights, obligations, and market integrity (OECD, 2017). Similarly, engagement with issuing companies through access to financial reports and disclosures enhances investors’ practical experience and decision-making ability (La Porta et al., 1999).

The adoption of technology, such as online trading platforms and robo-advisors, has also been shown to shape modern investment experiences by influencing learning processes and behavioral patterns (Statman, 2019). Finally, diversification across investment instruments reflects a higher level of exposure to risk–return trade-offs, aligning with the principles of modern portfolio theory (Markowitz, 1952). Taken together, these indicators provide a comprehensive measure of investment experience that captures both behavioral and structural dimensions of investor engagement in the capital market. In this study, investment experience is defined differently from traditional measurements, which generally emphasize outcome aspects such as investment duration, transaction frequency, or the level of returns. Referring to Vohra (2023) and Zhao and Zhang (2021), investment experience is understood as a multidimensional concept encompassing institutional, regulatory, and technological factors that shape investors’ interactions with the market.

Accordingly, the indicators used include the role of intermediaries, protection from regulators, the transparency and reputation of issuing companies, the utilization of technology, and the diversity of investment instruments. This approach provides a more comprehensive depiction of investment experience, thereby capturing the actual dynamics encountered by investors in the modern capital market era. Finally, the investor decision-making variable is set as the dependent variable, which assesses the impact of the independent variables on it, and is measured using five indicator items (Cao et al., 2021; Kourtidis et al., 2011; Raut, 2020). Operational definitions of the variables employed in this research are detailed in

Table 7. Variable Operationalization.

Variables	Definition	Indicator
Herding behavior	Herding behavior in the context of finance refers to the tendency of individuals to follow the actions of the majority or a larger group when making investment decisions, without conducting thorough independent analysis.	The propensity to imitate the investment choices of others. Dependence on external advice or recommendations when making investment decisions. The extent to which social media impacts investment behavior.
Investment experience	Investment Experience refers to the extent of an individual’s familiarity and involvement with financial markets, which is typically reflected by the duration, frequency, and diversity of their past investment activities.	Intermediaries. Regulator. Issuing company. Use of technology. Investment instruments.
Investor decision-making	Investor Decision-Making refers to the process through which individuals evaluate and select investment options based on available information, personal goals, and risk preferences	Macroeconomic information. Company performance reports. Stock price history. Availability and utilization of investment technology. Return expected.

.

5.3. Data Collection Technique and Dataset Source

The data in this study were collected through a structured online questionnaire distributed via the Google Forms platform. The questionnaire was disseminated through a Google Form link shared across various digital channels, including investor forums and social media platforms such as WhatsApp and Instagram. This method was chosen for its efficiency, accessibility, and ability to reach a wide population of individual stock investors across Indonesia. In the context of behavioral finance research, online surveys have proven effective in capturing self-reported psychological variables and investment behaviors from diverse demographic groups.

The questionnaire distribution strategy was designed to maximize response rates from active investors representing Generation X, Generation Y, and Generation Z. The dataset consists of primary data collected from voluntary respondents who met the inclusion criteria: (1) identifying themselves as members of Generation X, Y, or Z based on their year of birth; and (2) having actively participated in stock investment defined as having conducted at least one stock transaction within the past 12 months. Respondents who did not meet these criteria were excluded from the final dataset through a screening question embedded in the questionnaire.

To ensure the reliability and validity of responses, the questionnaire included attention-check questions and was pre-tested on a pilot sample of 30 respondents prior to full distribution. This pilot test also involved an assessment of face validity by asking respondents to evaluate how relevant, clear, and understandable each item was in relation to the constructs being measured. The pilot results were used to improve item clarity, logical flow, and completion time.

5.4. Data Pre-Processing and Dataset Validation

Before analysis, the data collected through the questionnaire underwent a series of preprocessing steps to ensure the validity and integrity of the dataset. The initial stage involved screening for incomplete responses, where entries with more than 10% missing answers were excluded from the analysis. Subsequently, data cleaning was performed to eliminate duplicates, verify logical consistency, and ensure that the measurement scales were correctly input. Out of the 1500 questionnaires distributed, a total of 1338 were deemed valid and included in the analysis, while 162 questionnaires (10.8%) were excluded due to not meeting data eligibility criteria. A summary of the number and percentage of respondents included and eliminated is presented in the Respondent Summary

Table 8. Data Pre-Processing.

Description	Number	Percentage
Questionnaires distributed	1500	100%
Questionnaires analyzed	1338	89.2%
Questionnaires not analyzed	162	10.8%

.

5.5. Common Method Bias Test

Common Method Bias (CMB) refers to potential distortion that may arise when data are collected using a uniform method, such as a single questionnaire with identical measurement scales. To assess CMB, Harman’s Single-Factor Test was conducted. The test results indicate that a single factor accounted for only 47.811% of the variance, or 44.966% after extraction using the Principal Axis Factoring method. Both values are below the commonly accepted threshold of 50%, suggesting that CMB is not a serious concern in this study. Furthermore, the analysis identified more than one factor with eigenvalues greater than 1, further supporting the conclusion that the dataset is not dominated by a single-method factor and has an adequate construct structure.

The detailed results of the Harman’s Single-Factor Test are presented in

Table 9. Total Variance Explained.

Factor	Initial Eigenvalues Total	Extraction Sums of Squared Loadings % of Variance
1	6.215	47.811
2	1.796	13.817
3	1.002	7.711
4	0.726	5.581
5	0.593	4.564
6	0.444	3.418
7	0.407	3.134
8	0.380	2.921
9	0.343	2.641
10	0.293	2.255
11	0.278	2.136
12	0.269	2.069
13	0.252	1.942

. As shown, the first factor accounted for less than 50% of the variance, both before and after extraction, while multiple factors with eigenvalues greater than 1 were identified. These findings confirm that the data structure is not dominated by a single factor, thereby indicating that common method bias is unlikely to pose a significant issue in this study.

5.6. Data Analysis Technique

The primary technique used for data analysis in this study was Partial Least Squares Structural Equation Modeling (PLS-SEM), conducted using SmartPLS 4 software. This method was chosen for its ability to model complex relationships between latent variables, even under conditions of non-normal data distribution and varying sample sizes. The analysis procedure was carried out in three stages. First, the measurement model (outer model) was evaluated to assess the reliability and validity of the constructs, distinguishing between reflective and formative indicators. This evaluation included convergent validity (assessed through indicator loadings), discriminant validity (using the Fornell-Larcker criterion), and composite reliability.

Second, the structural model (inner model) was analyzed to test the hypothesized relationships between constructs. The significance of the path coefficients was assessed using a bootstrapping procedure with 5000 subsamples at a 95% confidence level. Statistical significance was determined based on p-values, which were required to be less than 0.05. Additionally, the structural model’s quality was evaluated through the coefficient of determination (R²), which indicates the proportion of variance in the endogenous constructs explained by the exogenous constructs in the model. Higher R² values reflect stronger predictive power of the model. Third, to analyze generational differences, a Multi-Group Analysis (MGA) was performed using two approaches: the MGA-Permutation Test and MGA-Henseler Bootstrapping. These techniques enabled a comprehensive comparison of path coefficients across generational groups (Generation X, Y, and Z), allowing for the identification of potential moderating effects of age group on the tested structural relationships.

6. Conclusions

This study concludes that herding behavior has a positive and significant influence on investment decision-making across all generational cohorts, X, Y, and Z, within Indonesia’s capital market. The findings demonstrate that a stronger inclination toward herding behavior increases the likelihood that investors will base their decisions on the actions of the majority, particularly under conditions of market uncertainty. Notably, investment experience emerges as a significant moderating factor only for Generation X, effectively reducing the intensity of herding tendencies within this group. In contrast, the moderating effect of investment experience is not significant for Generations Y and Z, indicating that psychological and social influences, particularly those amplified by digital platforms, may override experiential learning in younger cohorts.

These results align with behavioral finance theory, which posits that investors often disregard personal information and follow the majority to avoid perceived mistakes or regrets. The generational differences identified underscore the importance of tailoring investor education and behavioral risk management strategies. For policymakers, financial educators, and industry stakeholders, these insights highlight the need to design targeted interventions that strengthen independent decision-making skills, particularly among younger generations who remain highly susceptible to socially driven herding behavior despite investment experience.

7. Implications and Limitations

The findings of this study provide actionable insights for policymakers, regulators, and financial industry stakeholders in developing targeted financial education programs that integrate behavioral finance principles, particularly the understanding and mitigation of herding behavior, tailored to the distinct characteristics of each generational cohort. The results highlight the need for regulatory frameworks that enhance information transparency and curb the dissemination of biased or misleading content on digital and social media platforms, which are major channels influencing Generations Y and Z. Such measures can help reduce the adverse effects of socially driven herding behavior, ultimately fostering more informed and rational investment decision-making in emerging markets like Indonesia. In terms of methodological considerations, although CMB was tested and found not to be a major threat in this study, future research may consider employing marker-variable techniques or collecting data from multiple sources to further mitigate the risk of method bias.

Despite these contributions, the study has certain limitations. It focuses exclusively on the context of a developing country, which may limit the generalizability of findings to developed markets where investor demographics, market efficiency, and behavioral dynamics differ significantly. Furthermore, the study relies on self-reported survey data, which may be subject to social desirability bias and does not capture real-time behavioral responses to market fluctuations. Moreover, as the data were collected through an online survey, the sample may overrepresent digitally active investors, particularly from younger generations, which could limit the representativeness of the findings across the broader investor population. Another limitation lies in the single-country scope of this study. By focusing only on Indonesia, the conclusions may not fully capture the diversity of generational investment behavior in different economic contexts. Factors such as market maturity, regulatory effectiveness, cultural attitudes toward risk, and technological adoption levels could shape herding tendencies differently in developed versus developing economies. Future studies should therefore incorporate multi-country analyses to examine whether the patterns observed here are consistent across regions with varying levels of economic development.

Future research could address these limitations by incorporating cross-country comparative analyses between developed and developing markets to examine whether generational patterns of herding behavior and the moderating role of investment experience remain consistent across different economic contexts. Additionally, longitudinal or experimental studies could be conducted to observe herding behavior in real-time market settings, providing stronger causal inferences. Further exploration into the role of emerging digital platforms, algorithm-driven trading recommendations, and financial literacy interventions could also yield deeper insights into mitigating herding behavior across generations.

In this study, the construct of investment experience (IE) was operationalized using five indicators: intermediaries, regulators, issuing company, use of technology, and investment instruments. This operationalization reflects the contextual characteristics of the Indonesian capital market, where investor interaction with market institutions, regulatory frameworks, and technological platforms plays a significant role in shaping investment behavior. While this approach captures relevant aspects of investor engagement in emerging markets, it differs from the more conventional measures of investment experience commonly adopted in the literature, such as years of investing, frequency of transactions, portfolio diversification, and monitoring intensity (Van Rooij et al., 2011).

This divergence may limit direct comparability of findings with prior studies conducted in developed market settings. Future studies are therefore encouraged to refine the measurement of investment experience by incorporating more direct and widely used indicators, including the duration of investing, transaction frequency, and portfolio diversity, as highlighted in behavioral finance and investor capability research (Lusardi & Mitchell, 2014). Employing such standardized measures would enhance construct validity and facilitate cross-market comparisons. At the same time, hybrid approaches that integrate contextual indicators, such as regulatory interaction and technology adoption, may offer richer insights into the multidimensional nature of investment experience, particularly in emerging markets.