Behavioral Biases and Unclaimed Dividends: Evidence from the Nigerian Capital Market

Behavioral Biases and Unclaimed Dividends: Evidence from the Nigerian Capital Market

Abdul Adamu^1*, Zainab Abdul Husseini², and Yusuf Alhaji Usman³

[1]* Department of Business Administration, Nasarawa State University Keffi, Nasarawa State, Nigeria

2 Department of Banking & Finance, Nasarawa State University Keffi, Nasarawa State, Nigeria

3 Department of Sociology, Nasarawa State University Keffi, Nasarawa State, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000117

Received: 27 August 2025; Accepted: 04 September 2025; Published: 01 October 2025

ABSTRACT

This study investigates the impact of behavioral biases on unclaimed dividends in the Nigerian capital market. Using a quantitative research design with a survey of 412 Nigerian investors and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research examined five specific behavioral factors: overconfidence bias, herding behavior, loss aversion, investor apathy, and procrastination. The findings revealed that all five behavioral biases significantly contribute to unclaimed dividends, with investor apathy (β = 0.234, p < 0.001) emerging as the strongest predictor, followed by procrastination (β = 0.198, p < 0.001) and overconfidence bias (β = 0.187, p < 0.001). The model explained 67.3% of the variance in unclaimed dividend behavior. The study recommended implementing behavioral-focused regulatory interventions that address psychological factors rather than purely technical solutions.

Keywords: Behavioral biases, unclaimed dividends, behavioral finance, Nigerian capital market, PLS-SEM

INTRODUCTION

The Nigerian capital market has experienced significant growth over the past decades, serving as a crucial platform for corporate financing and wealth creation for investors. However, a persistent challenge that has undermined market efficiency and investor confidence is the substantial volume of unclaimed dividends. According to the Securities and Exchange Commission (SEC Nigeria, 2023), unclaimed dividends in Nigeria have grown exponentially from ₦2.09 billion in 1999 to a staggering ₦190 billion in 2021, representing an increase of over 7,500%. This dramatic escalation has raised concerns among market regulators, policymakers, and stakeholders about the underlying factors contributing to this phenomenon.

The issue of unclaimed dividends is not merely a statistical concern but represents a fundamental challenge to market efficiency and investor welfare (Okafor & Ugochukwu, 2021). These unclaimed funds effectively constitute dead capital, removed from active circulation and investment, thereby reducing the overall economic impact of capital market activities (Lusardi & Mitchell, 2014). Furthermore, the prevalence of unclaimed dividends may signal deeper structural and behavioral issues within the investment ecosystem that require comprehensive investigation and intervention.

Recent studies in behavioral finance have emphasized the significant role of psychological factors and cognitive biases in investment decision-making (Barber & Odean, 2001; Barberis & Thaler, 2003). The traditional assumption of rational investor behavior has been increasingly challenged by evidence of systematic biases and heuristics that influence financial decisions (Kahneman & Tversky, 1979). In the context of unclaimed dividends, these behavioral factors may play a crucial role in determining whether investors actively claim their rightful returns or allow them to remain unclaimed.

Despite various regulatory interventions, including the introduction of the Electronic Dividend Mandate Management System (E-DMMS) and enhanced disclosure requirements, the problem of unclaimed dividends in Nigeria continues to escalate (SEC Nigeria, 2023). The persistent growth of unclaimed dividends suggests that regulatory and technological solutions alone are insufficient to address the underlying behavioral and psychological factors that influence investor decision-making regarding dividend claims.

The magnitude of the unclaimed dividend problem in Nigeria is particularly concerning when compared to other emerging markets. For instance, in India, dividends worth approximately ₹5,700 crore (about $690 million) have been transferred to the Investor Education and Protection Fund Authority (IEPFA) as of recent data, with the authority actively managing transfers and claims through digital portals, though the total volume of unclaimed shares has reached 117 crore shares valued at ₹40,000-50,000 crore (Business Today, 2023; Muds, 2024). Similarly, South Africa reported R4.5 billion (about $250 million) in unclaimed dividends as of 2025, prompting the Johannesburg Stock Exchange (JSE) to launch a nationwide “Claim It” campaign with online verification tools and targeted outreach to 375,000 potential beneficiaries, representing a proactive approach to dividend reunification (JSE, 2025a; Business Tech, 2025). In Kenya, while comprehensive dividend-specific data is limited, the Unclaimed Financial Assets Authority (UFAA) reported total unclaimed assets reaching KSh 54.8 billion (about $380 million) by 2022, including various financial instruments such as dividends, shares, and dormant accounts, with relatively low claiming rates despite regulatory enforcement efforts (Tuko, 2022; Vellum Kenya, 2022).

These comparative experiences reveal that while some markets have achieved moderate success through digital innovations and public awareness campaigns, the fundamental challenge of reconnecting investors with unclaimed dividends persists across emerging markets. Nigeria’s unclaimed dividend-to-market capitalization ratio continues to rise despite technological interventions, suggesting that behavioral and institutional factors may play a more significant role than purely administrative solutions (Van Rooij et al., 2011; Lusardi & Mitchell, 2014). This disparity indicates that Nigeria-specific factors, including investor behavior patterns, cultural influences, and market infrastructure challenges, may be contributing to the persistence of this problem.

The lack of comprehensive research into the behavioral determinants of unclaimed dividends in Nigeria represents a significant gap in the literature. While previous studies have identified technical and administrative factors contributing to unclaimed dividends, there has been limited systematic investigation into the psychological and behavioral aspects that drive investor decisions regarding dividend claims (Okafor & Ugochukwu, 2021; Oladipo & Ibrahim, 2022). This research gap hinders the development of effective, evidence-based interventions to address the unclaimed dividend challenge.

Thus, the main objective of this study is to analyze the impact of behavioral biases on the prevalence of unclaimed dividends in the Nigerian capital market. The specific objectives are:

1. To examine the effect of overconfidence bias on unclaimed dividends in Nigeria
2. To investigate the effect of herding behavior on unclaimed dividends in Nigeria
3. To determine the effect of loss aversion on unclaimed dividends in Nigeria
4. To investigate the effect of investor apathy on unclaimed dividends in Nigeria
5. To examine the effect of procrastination on unclaimed dividends in Nigeria

The study tests the following hypotheses:

H₀₁: Overconfidence bias has no significant effect on unclaimed dividends in Nigeria.

H₀₂: Herding behavior has no significant effect on unclaimed dividends in Nigeria.

H₀₃: Loss aversion has no significant effect on unclaimed dividends in Nigeria.

H₀₄: Investor apathy has no significant effect on unclaimed dividends in Nigeria.

H₀₅: Procrastination has no significant effect on unclaimed dividends in Nigeria.

LITERATURE REVIEW

Unclaimed Dividends

Unclaimed dividends represent dividend payments that have been declared and made available by companies to their shareholders but remain uncollected by the entitled investors within specified timeframes (Okafor & Ugochukwu, 2021). According to Okafor and Ugochukwu (2021), unclaimed dividends arise from various factors including inadequate shareholder communication, outdated contact information, lack of awareness about dividend rights, and behavioral biases that affect claiming decisions.

The phenomenon of unclaimed dividends is not unique to Nigeria but represents a global challenge affecting both developed and emerging markets (Van Rooij et al., 2011). However, the scale and persistence of unclaimed dividends in Nigeria suggest unique market-specific factors that require targeted investigation and intervention strategies (Oladipo & Ibrahim, 2022). Recent studies in Nigeria have further explored institutional influences, such as regulatory frameworks and corporate governance, on unclaimed dividends. For instance, Oladipo and Ibrahim (2022) examined how institutional features like board independence and audit quality affect unclaimed dividend levels, finding that weaker governance structures exacerbate the issue. Additionally, Okafor et al. (2023) investigated the impact of unclaimed dividends on fixed income market performance in Nigeria from 1991 to 2021, revealing that rising unclaimed amounts negatively affect market liquidity and investor confidence.

Overconfidence Bias

Overconfidence bias refers to investors’ tendency to overestimate their abilities, knowledge, or chances of success (Barber & Odean, 2001). In the context of unclaimed dividends, overconfident investors may believe they have adequate systems for tracking their investments and may neglect formal dividend claiming processes, assuming they will naturally receive their payments (Odean, 1998). Research by Gervais and Odean (2001) found that overconfident investors are more likely to engage in suboptimal financial behaviors, including neglecting routine investment management tasks. Recent region-specific studies in Nigeria, such as Alalade et al. (2024), tested behavioral finance factors like overconfidence in the Nigerian capital market, showing that such biases significantly influence investment guidelines and reduce herding effects when mitigated through financial literacy programs.

Herding Behavior

Herding behavior describes investors’ tendency to follow the actions of others rather than relying on their own information and analysis (Bikhchandani & Sharma, 2000). Regarding unclaimed dividends, herding behavior may manifest when investors observe others not actively claiming dividends and consequently adopt similar passive approaches (Scharfstein & Stein, 2000). This social proof mechanism can perpetuate suboptimal investment behaviors across investor communities (Banerjee, 1992). In emerging African markets, a study by Waweru et al. (2023) on behavioral biases in Kenya’s Nairobi Securities Exchange found herding to be prevalent, leading to delayed claims and increased unclaimed assets, paralleling Nigerian trends.

Loss Aversion

Loss aversion, rooted in prospect theory, describes individuals’ tendency to feel losses more acutely than equivalent gains (Kahneman & Tversky, 1979). In dividend claiming contexts, investors may perceive the time and effort required to claim dividends as a loss that outweighs the benefit of receiving the dividend payment, particularly for smaller amounts (Tversky & Kahneman, 1992). This psychological bias can lead to systematic avoidance of dividend claiming activities (Shefrin & Statman, 1985). Recent research in emerging markets, including Singh and Kumar (2024) in India, highlights how loss aversion affects dividend payout behaviors, with investors in volatile markets like Nigeria showing similar patterns of avoidance for administrative tasks.

Investor Apathy

Investor apathy refers to emotional disengagement and lack of interest in actively managing one’s investment portfolio (Madrian & Shea, 2001). This behavioral characteristic manifests as indifference towards investment outcomes, neglect of portfolio monitoring, and passive acceptance of suboptimal investment situations (Benartzi & Thaler, 2007). In the context of unclaimed dividends, investor apathy may lead to neglect of dividend notifications, failure to update personal information with registrars, and general disinterest in claiming processes (Samuelson & Zeckhauser, 1988).

Procrastination

Procrastination in investment contexts refers to the tendency to delay or postpone financial decisions and actions, even when such delays may be detrimental to one’s financial interests (Benartzi & Thaler, 2007). This behavioral bias affects various investment activities, including portfolio rebalancing, tax planning, and administrative tasks such as dividend claiming (Madrian & Shea, 2001). Procrastinating investors may intend to claim their dividends but repeatedly delay the action due to perceived complexity, time constraints, or simple inertia (Samuelson & Zeckhauser, 1988).

Overconfidence Bias and Investment Behavior

Recent empirical research has consistently demonstrated the significant impact of overconfidence bias on various investment behaviors. Barber and Odean (2001) conducted a comprehensive study of 35,000 individual investors and found that overconfident investors were 45% more likely to neglect routine portfolio management tasks, including dividend tracking and claiming. Men traded 45% more than women due to overconfidence, reducing net returns by 2.65% annually versus women’s 1.72% reduction. Similarly, Odean (1998) examined investor behavior and discovered that overconfidence bias contributed to approximately 25% of suboptimal investment decisions among individual investors.

In emerging market contexts, Gervais and Odean (2001) investigated investor behavior and found that overconfident investors demonstrated significantly higher rates of neglecting administrative tasks, with the effect being particularly pronounced among male investors and those with higher education levels. Their study revealed that overconfident investors often overestimated their ability to track dividend payments automatically and underestimated the importance of formal claiming processes. Complementing this, Sharma and Gupta (2024) in India found similar overconfidence effects in ESG investing trends across emerging markets, including parallels in dividend management.

Research on investor apathy has revealed its significant impact on various investment behaviors. Madrian and Shea (2001) conducted a longitudinal study of 1,800 investors over three years and found that investor apathy was among the strongest predictors of suboptimal investment behaviors, accounting for significant variance in portfolio management activities. Benartzi and Thaler (2007) examined the role of emotional engagement in investment decisions and found that apathetic investors were 67% more likely to engage in suboptimal investment behaviors compared to engaged investors.

Herding Behavior and Dividend Claiming

Research on herding behavior in dividend claiming contexts has yielded compelling evidence of social influence effects. Bikhchandani and Sharma (2000) analyzed investor communities in emerging markets and found that herding behavior accounted for approximately 18% of the variance in dividend claiming rates across different investor groups. Their study demonstrated that investors in communities with lower overall claiming rates were significantly more likely to leave their own dividends unclaimed. Recent African studies, such as those by Njuguna (2023) in Kenya, confirm herding as a factor in unclaimed financial assets, with social networks amplifying passive behaviors.

Scharfstein and Stein (2000) conducted experimental research demonstrating that social proof mechanisms significantly influence dividend claiming behavior. Their findings showed that investors who received information about others’ claiming behavior were 42% more likely to adjust their own claiming patterns accordingly. This research highlights the potential for leveraging social influence mechanisms in interventions designed to increase the rate dividend claims.

Loss Aversion and Investment Decision-Making

Empirical studies on loss aversion have consistently shown its impact on investment behaviors, including dividend claiming decisions. Kahneman and Tversky (1979) examined large-scale investor data and found that loss aversion contributed to suboptimal investment behaviors, with investors avoiding actions that required immediate effort or cost despite long-term benefits. In the context of dividend claiming, Shefrin and Statman (1985) found that investors with higher loss aversion scores were 28% less likely to claim dividends, particularly when claiming processes required significant time or effort. An analysis by Singh et al. (2025) on behavioral finance in emerging markets like South Africa and India extends this, showing loss aversion as a barrier to claiming in volatile environments.

Tversky and Kahneman (1992) conducted longitudinal research tracking investor behavior over five years and discovered that loss aversion effects were particularly pronounced for smaller dividend amounts, where the perceived effort-to-benefit ratio was highest. This finding suggests that loss aversion may disproportionately affect the claiming of smaller dividends, contributing to the accumulation of unclaimed amounts over time.

Investor Apathy and Portfolio Management

Research on investor apathy has revealed its significant impact on various investment behaviors. Madrian and Shea (2001) conducted a longitudinal study of 1,800 investors over three years and found that investor apathy was among the strongest predictors of suboptimal investment behaviors, accounting for significant variance in portfolio management activities. Their research identified apathy as a multidimensional construct encompassing emotional disengagement, lack of portfolio monitoring, and passive investment approaches.

Benartzi and Thaler (2007) examined the role of emotional engagement in investment decisions and found that apathetic investors were 67% more likely to engage in suboptimal investment behaviors compared to engaged investors. The study revealed that apathy particularly affected routine investment management tasks, with apathetic investors showing poor performance across multiple portfolio management dimensions.

Procrastination in Financial Decision-Making

Empirical studies on procrastination in financial contexts have demonstrated its pervasive effects on investment behaviors. Benartzi and Thaler (2007) surveyed 2,100 individual investors and found that procrastination significantly delayed financial decisions, with procrastinating investors being 53% more likely to delay important financial actions. Their research identified perfectionism and decision complexity as key drivers of procrastination in financial contexts. Extending to emerging markets, a study by Patel (2024) in India on dividend policy changes notes procrastination as a rising factor in unclaimed trends post-2020.

Madrian and Shea (2001) conducted experimental research on financial procrastination and found that investors who exhibited procrastination tendencies in other life domains were significantly more likely to delay financial decisions. The study revealed that procrastination effects were particularly strong when processes were perceived as complex or time-consuming.

Theoretical Framework

Prospect Theory

This study is primarily grounded in Prospect Theory, developed by Kahneman and Tversky (1979) and subsequently refined in later work. Prospect Theory provides a comprehensive framework for understanding how individuals make decisions under uncertainty, particularly in financial contexts. The theory posits that individuals evaluate outcomes relative to reference points rather than absolute values, and that they exhibit systematic biases in their decision-making processes.

For unclaimed dividends, Prospect Theory explains several key behavioral patterns. First, the theory’s value function, which is concave for gains and convex for losses, suggests that investors may perceive the effort required to claim dividends as a loss that outweighs the benefit of receiving the dividend payment. Second, the probability weighting function in Prospect Theory explains why investors may overweight the unlikely event of automatically receiving dividends while underweighting the probability that active claiming is required.

Mental Accounting Theory

Mental Accounting Theory, developed by Thaler (1999) and extended by subsequent researchers, provides additional theoretical foundation for understanding dividend claiming behavior. This theory suggests that individuals categorize money into different mental accounts and treat these accounts differently, even when such treatment is economically irrational.

Regarding unclaimed dividends, Mental Accounting Theory explains how investors may categorize dividend income differently from other types of investment returns, potentially leading to different levels of attention and effort devoted to claiming these payments. The theory also explains how the source of dividends (different companies or sectors) may influence claiming behavior, as investors may have different mental accounts for different investments.

Social Learning Theory

Social Learning Theory, as applied to financial behavior by Hong et al. (2004), provides a framework for understanding herding behavior in dividend claiming contexts. This theory suggests that individuals learn appropriate behaviors by observing others and that social norms significantly influence individual decision-making.

In the context of unclaimed dividends, Social Learning Theory explains how investors may develop claiming behaviors based on observed practices within their social or investment networks. If unclaimed dividends are normalized within certain investor communities, new investors may adopt similar behaviors through social learning processes (Banerjee, 1992).

MATERIAL AND METHODS

This investigation employed a quantitative research design utilizing cross-sectional survey. This was chosen as optimal for examining relationships among knowledge factors, accessibility elements, demographic characteristics, and dividend collection behavior across an extensive sample of Nigerian market participants (Hair et al., 2017). The cross-sectional approach enabled data collection at a single temporal point, providing a comprehensive view of current participant knowledge levels, awareness patterns, and demographic distributions while maintaining cost-effectiveness and feasibility (Sarstedt et al., 2017).

The target population comprised all individual investors and stockbrokers actively participating in the Nigerian capital market. According to the Nigerian Exchange Group (NGX, 2024) statistics, the market has approximately 42.3 million registered investors as of 2024, representing the accessible population for this study.

The sample size was determined using Krejcie and Morgan’s (1970) formula for finite populations, with adjustments for the complexity of structural equation modeling requirements.

S =

Where: S = Sample size, X = Z value (1.96), N = Population Size, P = Population proportion assumed to be50%, d = margin of error (5%). Using equation (1), the sample size is calculated as:

S =  = 384

For a population of 42.3 million investors, with a 95% confidence level and 5% margin of error, the minimum required sample size was calculated as 384 respondents. However, considering the requirements for PLS-SEM analysis and potential non-response rates, the target sample size was increased by 30% and set at 500 respondents as suggested by Israel (2018) to ensure adequate statistical power (Hair et al., 2017).

A multistage stratified random sampling technique was employed to ensure representativeness across different investor segments and geographic regions. The sampling process involved three stages: (1) Nigeria was stratified into six geopolitical zones to ensure geographic representation, (2) within each zone, major commercial centers with significant capital market activity were identified and selected proportionally based on investor concentration, and (3) individual investors were randomly selected from investor databases provided by participating stockbroking firms and the Nigerian Exchange Group.

A structured questionnaire was developed based on established scales from behavioral finance literature, adapted for the Nigerian context. The instrument comprised sections designed to capture all behavioral variables identified in the study objectives:

1. Overconfidence Bias: Adapted from Barber and Odean (2001), consisting of 6 items measuring investors’ tendency to overestimate their abilities and knowledge in investment decisions
2. Herding Behavior: Modified from Bikhchandani and Sharma (2000), comprising 5 items measuring investors’ tendency to follow others’ actions rather than relying on independent analysis
3. Loss Aversion: Adapted from Kahneman and Tversky (1979), containing 7 items evaluating investors’ asymmetric response to potential gains and losses
4. Investor Apathy: Based on Madrian and Shea (2001), using 6 items measuring emotional disengagement and lack of interest in active portfolio management
5. Procrastination: Based on Benartzi and Thaler (2007), employing 5 items evaluating investors’ tendency to delay financial decisions and actions
6. Unclaimed Dividends: 8 items directly assessing respondents’ propensity to leave dividends unclaimed and their experiences with unclaimed dividends

All scales used 5-point Likert scales ranging from 1 (Strongly Disagree) to 5 (Strongly Agree) to ensure consistency and ease of response (Hair et al., 2017). Data collection was conducted over a three-month period (May-July 2025) using online surveys and physical distribution.

Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) implemented through SmartPLS 4.0 software. PLS-SEM was selected for its ability to handle complex models with multiple constructs while making minimal distributional assumptions about the data (Hair et al., 2017).

The measurement model assessment followed comprehensive guidelines established by Hair et al. (2017) for PLS-SEM analysis. Reliability was evaluated using Cronbach’s Alpha and Composite Reliability (CR) with minimum thresholds of 0.70. Individual indicator reliability was assessed through factor loadings with a minimum threshold of 0.70. Convergent validity was established through Average Variance Extracted (AVE) with a minimum threshold of 0.50. Discriminant validity was assessed using the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations (Henseler et al., 2015).

The structural model evaluation examined path coefficients for magnitude, direction, and statistical significance. The coefficient of determination (R²) was evaluated to assess the model’s explanatory power, with thresholds of 0.25, 0.50, and 0.75 representing weak, moderate, and substantial levels respectively (Cohen, 1988). Bootstrap resampling with 5,000 subsamples was employed to assess the significance of path coefficients and generate confidence intervals.

RESULTS AND DISCUSSION

A total of 412 valid responses were obtained, representing a 91.6% response rate from the target sample of 450 respondents. Table 1 presents the demographic profile of respondents.

Table 1. Demographic Profile of Respondents

Source: Primary data from field survey, 2025

Table 2 presents the results of the measurement model assessment, including reliability and validity indicators.

Table 2 Measurement Model Assessment

Note: CR = Composite Reliability; AVE = Average Variance Extracted

Source: Primary data analysis using SmartPLS 4.0, 2025

The measurement model assessment demonstrates excellent psychometric properties across all constructs. All Cronbach’s alpha values exceed the 0.70 threshold, ranging from 0.812 to 0.912, indicating strong internal consistency reliability. Composite reliability (CR) values are consistently above 0.87, surpassing the recommended 0.70 minimum. Individual item loadings range from 0.718 to 0.882, all exceeding the 0.70 threshold. Convergent validity is established through AVE values ranging from 0.574 to 0.651, all exceeding the 0.50 minimum requirement.

Table 3 presents the results of the structural model analysis, including path coefficients, t-values, and significance levels for each hypothesis.

Table 3 Structural Model Results – Path Coefficients

*Note: *p < 0.01

Source: PLS-SEM structural model analysis using SmartPLS 4.0, 2025

The structural model results provide strong empirical support for all five hypotheses, confirming the theoretical relationships between behavioral biases and unclaimed dividend behavior. The findings reveal that all five examined behavioral factors significantly contribute to the unclaimed dividend problem, with varying degrees of impact All path coefficients demonstrate statistical significance at the p < 0.05 level. Investor apathy emerges as the strongest predictor (β = 0.234, t = 5.678), followed by procrastination (β = 0.198, t = 4.523) and overconfidence bias (β = 0.187, t = 4.231).

Overconfidence bias showed a significant positive relationship with unclaimed dividends (β = 0.187, p < 0.001), supporting the hypothesis that investors who overestimate their abilities and knowledge are more likely to neglect formal dividend claiming processes. This finding extends previous research by Barber and Odean (2001) to the Nigerian context, suggesting that overconfident investors may assume they will automatically receive dividends or that their informal tracking methods are sufficient.

Herding behavior demonstrated a significant effect on unclaimed dividends (β = 0.142, p < 0.05), indicating that social influence and conformity pressures contribute to dividend claiming decisions. This finding suggests that if unclaimed dividends become normalized within certain investor communities, the behavior may spread through social learning mechanisms (Bikhchandani & Sharma, 2000).

Loss aversion showed a significant positive relationship with unclaimed dividends (β = 0.156, p < 0.05), supporting the prospect theory prediction that investors perceive the effort required to claim dividends as a loss that outweighs the benefit of receiving the payment. This finding is particularly relevant for smaller dividend amounts, where the effort-to-benefit ratio may be perceived as unfavorable (Kahneman & Tversky, 1979).

Investor apathy emerged as the most influential factor (β = 0.234, p < 0.05), suggesting that emotional disengagement and lack of active portfolio management represent fundamental challenges in the Nigerian market context. This finding aligns with research by Madrian and Shea (2001), who identified investor apathy as a critical factor in suboptimal financial behaviors. The practical implication is that interventions targeting emotional engagement and active portfolio management could yield significant improvements in claiming rates.

Procrastination demonstrated a substantial effect (β = 0.198, p < 0.05), indicating that Nigerian investors’ tendency to delay dividend claiming activities significantly contributes to the accumulation of unclaimed dividends. This finding is consistent with behavioral finance literature that identifies procrastination as a pervasive bias affecting various financial decisions (Benartzi & Thaler, 2007). The practical implication is that interventions targeting procrastination behavior, such as automated claiming systems or simplified processes, may yield significant improvements in claiming rates.

The study’s findings provide strong support for behavioral finance theories in the Nigerian capital market context. The significant effects of all examined behavioral biases demonstrate that Nigerian investors exhibit systematic deviations from rational decision-making, consistent with Prospect Theory predictions (Kahneman & Tversky, 1979). The differential effects of various behavioral factors suggest that mental accounting processes are operating in Nigerian investors’ decision-making, supporting Mental Accounting Theory (Thaler, 1999). The significant effect of herding behavior confirms Social Learning Theory’s applicability to the Nigerian context (Hong et al., 2004).

Table 4: Model Assessment Indicators

Source: Model fit assessment using SmartPLS 4.0, 2025

The model demonstrates strong explanatory power with an R² value of 0.673, indicating that the five behavioral factors collectively explain 67.3% of the variance in unclaimed dividend behavior, representing substantial explanatory power according to Cohen’s (1988) guidelines.

Limitations Of The Study

While this study provides valuable insights into behavioral biases and unclaimed dividends, it is not without limitations. The cross-sectional design captures relationships at a single point in time, limiting inferences about causality and long-term dynamics. Self-reported data from surveys may introduce response biases, such as social desirability or recall errors. The sample, although stratified across geopolitical zones, may not fully represent all investor segments, particularly those in rural areas or with limited digital access. Additionally, the focus on five specific biases excludes other potential factors, like financial literacy levels or macroeconomic influences. Future research could address these by employing longitudinal designs, mixed methods, or broader samples for enhanced generalizability.

CONCLUSION AND RECOMMENDATIONS

This study investigated the impact of behavioral biases on unclaimed dividends in the Nigerian capital market, providing compelling evidence that psychological factors are primary drivers of this phenomenon. The research identified five significant behavioral predictors: investor apathy (strongest), procrastination, overconfidence bias, loss aversion, and herding behavior, which collectively explain 67.3% of the variance in unclaimed dividend behavior.

The findings extend established behavioral finance theories to the Nigerian context, demonstrating their cross-cultural applicability while identifying context-specific manifestations. The study provides strong empirical support for Prospect Theory, Mental Accounting Theory, and Social Learning Theory in explaining dividend claiming behavior among Nigerian investors.

Based on the study’s findings, the following recommendations are proposed:

1. Regulatory efforts should focus on addressing psychological and educational aspects rather than purely technical solutions. Current initiatives like the E-DMMS should be complemented with behavioral interventions, such as partnering with the Nigerian Exchange Group (NGX) to integrate behavioral nudges into the platform, like automated reminders linked to Bank Verification Numbers (BVN).
2. Regulators should develop comprehensive education programs that specifically address the identified behavioral biases, with particular focus on reducing investor apathy and procrastination. For instance, collaborate with the Central Bank of Nigeria (CBN) and financial institutions to offer mandatory workshops or mobile app-based modules tailored to Nigerian investors, incorporating real-time simulations of claiming processes.
3. Leverage social proof and peer influence by publishing claim rate statistics or creating peer comparison tools to encourage more active claiming behavior. The SEC could launch a national campaign similar to South Africa’s JSE initiative, using social media and community outreach in local languages to highlight success stories from claimed dividends.
4. The investment firms and market participants should develop client profiling systems that identify investors prone to specific biases and implement targeted intervention strategies. For example, stockbroking firms could use AI-driven tools to flag apathetic investors based on activity logs and send personalized prompts via SMS or email.
5. They should implement automated dividend claiming systems for clients who opt-in, directly addressing procrastination and apathy. Integrate this with Nigeria’s national identity systems (e.g., NIN) and banking apps to enable seamless transfers, reducing administrative barriers.
6. Design communication strategies that focus on behavioral triggers rather than purely informational content, addressing loss aversion and creating urgency. For smaller dividends, frame claims as “quick wins” through gamified apps, partnering with fintech companies like Flutterwave or Paystack for instant processing.

The persistent problem of unclaimed dividends in Nigeria represents fundamental behavioral and psychological barriers that require sophisticated, theory-driven interventions. This study demonstrates that addressing these behavioral factors through targeted educational programs, regulatory innovations, and industry best practices can yield substantial improvements in market efficiency and investor welfare.

 ACKNOWLEDGMENTS

The authors gratefully acknowledge the financial support provided by the TETFund, Nigeria, which made this research possible. This research is funded by TETFund, Nigeria. We also thank Nasarawa State University Keffi for providing the institutional support and research environment necessary for conducting this study.

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