From Short-Term Volatility to Long-Term Growth: Restricted Stock Units’ Impact on Earnings per Share and Profit Growth Across Sectors

Abstract

This research empirically investigates how the adoption of restricted stock units (RSUs) affects earnings per share (EPS) and operating profit, focusing on variations across industries. RSUs have emerged as a strategic compensation tool for promoting alignment between employee interests and long-term organizational objectives, while overcoming the short-sighted focus typically associated with conventional stock options. However, previous studies have mainly focused on analyzing the average effect of RSU or verifying only whether there is a short-term improvement in performance after its introduction, and there has been no sufficient review of the long-term effects. In addition, there is a lack of analyses on how the effects of RSU differ by industry. To fill this research gap, this study applied hierarchical regression analysis to S&P 500 company data from 1997 to 2023 to compare and analyze the differential effects of RSU by industry. The analysis showed that the EPS and operating income were only significantly affected by some industries in the early stages of RSU introduction, but the EPS and operating income were significantly improved in all industries in the long term. In addition, it was confirmed that the effects of RSU differ depending on the characteristics of the industry. This study empirically verifies the long-term effects of RSU and the differences by industry, offering practical insights for executives and shareholders when utilizing RSUs as a forward-looking compensation mechanism that fosters sustainable performance and enduring corporate value.

1. Introduction

1.1. Research Background and Need

Executive compensation systems have traditionally served not only as a payment mechanism but also as a strategic lever influencing management behavior, long-term sustainability, and shareholder returns. Traditionally, many companies have used stock options as the main method of executive compensation, but this method has several structural limitations. While stock options have the advantage of strengthening the motivation of management, they often generate unintended consequences such as greater price volatility, short-termism in decision-making, and a heightened tendency for risky or even manipulative behavior in pursuit of immediate gains (Hall & Murphy, 2003).

In particular, as the number of cases in which management artificially boosts the stock price in the short term to exercise stock options has increased, the issue of prioritizing short-term shareholder return strategies over long-term corporate growth has intensified. This not only hinders the sustainability of the company but also threatens to reduce long-term corporate value (Yoon, 2023).

To overcome these limitations and induce more long-term value creation, many companies are adopting restricted stock units (RSUs) as an alternative. RSUs are granted when specific conditions (e.g., completed tenure, achieving performance targets, etc.) are met, and since there is no exercise price, they are less directly influenced by a fall in the stock price. In addition, they are known to reduce the possibility of management making unreasonable decisions to increase the stock price in the short term and to induce continuous corporate growth (S. Lee, 2024).

Thanks to these advantages, RSUs were first introduced by Microsoft in 2003 and have since spread rapidly, especially among large US companies, and since the mid-to-late 2010s, major companies in Europe and Japan have also been actively introducing them (Yang, 2024). Furthermore, as we enter the 2020s, the pace of adoption is accelerating in Korea as well, with Hanwha becoming the first company to introduce RSUs, and they are becoming a key component of corporate compensation systems.

However, despite the fact that the RSU compensation system has been in operation for more than 20 years and has spread to various countries, empirical research on it is still limited. Existing studies have found that RSUs have a positive impact on attracting and retaining talent and motivating employees within the organization (Lu, 2024), but there is a lack of research on whether the introduction of RSUs actually contributes to long-term corporate value growth and increased shareholder value. The effects of corporate compensation systems accumulate over time, so it is difficult to accurately assess the long-term impact of RSUs based on short-term performance analysis alone (Murphy & Vance, 2019).

In addition, there is limited research on how RSUs have a differential impact on different industries. To date, research has focused on specific industries such as the IT industry or specific classes such as CEOs (Jia, 2023; Hwangbo & Yang, 2023; Abudy & Benninga, 2016), or has focused on analyzing the average impact of RSUs regardless of industry (Oxelheim et al., 2008; Kayani & Gan, 2022).

However, RSUs are likely to have different effects depending on the characteristics of the company and the industrial structure. In particular, the effects of RSUs may differ in capital-intensive industries, R&D-oriented industries, and regulated industries, and since RSUs are a system that expects long-term performance, research is needed to demonstrate long-term results.

1.2. Research Objectives

This research aims to empirically analyze the impact of the introduction of RSUs on the core financial performance indicators of companies, especially their earning per share (EPS) and operating income, by group for S&P 500 companies in the United States. Through this, we hope to provide empirical evidence that helps companies develop more effective compensation strategies. In addition, by extending the research period from 1997 to 2023, we focus on analyzing the long-term effects of RSUs, which have not been sufficiently examined in existing studies (Yoon, 2023; Jia, 2023; Yang, 2024; S. Lee, 2024). This research aims to empirically examine the impact of RSUs on the continuous improvement of corporate performance by comparing the short-term and long-term effects of RSUs. In particular, by classifying major companies considering the differential impact by industry, this study analyzes how the introduction of RSUs works differently depending on the characteristics of the industry.

This study aims to evaluate whether RSUs can contribute to corporate growth and long-term competitiveness, and thereby provide practical implications for corporate management, investors, and policymakers aiming to design more strategic compensation systems. Ultimately, this study aims to provide a strategic direction for the design of corporate compensation systems and empirically demonstrate that RSUs may serve as a useful mechanism for employee compensation to improve long-term performance and induce sustainable growth.

2. Literature Review

2.1. Scholarly Background and Purpose of RSU Implementation

Executive compensation today is no longer a straightforward payment system; rather, it plays a pivotal role in shaping corporate governance, influencing strategic choices, and fostering long-term value. The structure of incentive programs determines how executives interpret risks, balance short- and long-term trade-offs, and align with investor interests. One influential explanation for misalignment between executives and shareholders is offered by Jensen and Meckling (1976), who introduced the agency problem—a condition where delegated authority leads to potential conflicts due to information asymmetry and self-serving behavior by agents. Agency theory thus serves as a foundational framework for understanding why compensation strategies matter.

Traditional approaches to mitigating agency problems have relied heavily on equity-based tools, particularly stock options. These instruments link executive pay to company performance via share price appreciation. The logic is that personal financial interest in firm performance should compel managers to act in shareholders’ favor. McConnell and Servaes (1990) provide empirical evidence supporting this rationale, but with nuance: their study reveals that while moderate equity ownership fosters value creation, excessive holdings may have diminishing or adverse effects—a curvilinear relationship.

Despite their intended benefits, stock options have come under criticism for fostering unintended consequences. DeFusco et al. (1990) and Yermack (1995) observed that equity compensation can sometimes encourage short-termism or the manipulation of performance metrics. These concerns are echoed by Hall and Murphy (2003), who argued that options might shift managerial attention toward near-term stock performance at the expense of sustainable growth and innovation.

Such critiques gained further credibility from real-world corporate failures. Burns and Kedia (2006) found statistical links between generous stock option policies and higher incidences of accounting misconduct. Enron and WorldCom—two of the most notorious accounting scandals in U.S. corporate history—demonstrated how misaligned incentives can precipitate unethical behavior, ultimately resulting in firm collapse and shareholder loss.

In response, regulatory intervention followed. The U.S. Congress passed the Sarbanes–Oxley Act to enhance financial reporting standards and impose stricter accountability mechanisms. Nonetheless, academic work such as that by Coles et al. (2006) suggests that options may still distort executive risk preferences, leading to overly conservative business strategies. Further critiques from Hall and Murphy (2003) and Bebchuk and Fried (2006) questioned the fairness and effectiveness of options, particularly their sensitivity to market volatility and potential for diluting shareholder equity.

Given these challenges, attention has increasingly turned to alternative incentive mechanisms. Restricted Stock Units (RSUs), which invest only upon meeting specific performance or service conditions, have become an appealing solution. RSUs provide a clearer, more stable valuation at the time of grant and avoid some of the volatility and gaming associated with options. Although tax implications remain a consideration, their predictability has made them a preferred choice among both firms and executives.

What sets RSUs apart is not only their structure but also their theoretical compatibility with stewardship theory. Donaldson and Davis (1991) proposed that executives, when trusted, are more likely to act as stewards of the firm, motivated by a long-term vision rather than immediate rewards. This perspective reframes executive behavior as inherently responsible under the right conditions. Palmer (2024) offers empirical backing for this view, showing that RSUs can moderate opportunistic actions during the vesting period, reduce agency costs, and promote sustainable corporate outcomes.

A key inflection point in the adoption of RSUs came with the 2004 revision of U.S. accounting rules—FAS 123(R)—which required companies to treat stock options as accounting expenses. This shift raised the cost of traditional option-based programs (Yang, 2024), prompting a reevaluation of incentive structures. As a result, major corporations like Microsoft and Alphabet transitioned toward RSUs, viewing them as more transparent and aligned with shareholder value. Kim and Yang (2024) found that by 2006, RSU issuance in the U.S. had surpassed stock option grants, indicating a broader realignment in executive compensation philosophy. The differences between stock options and RSUs mentioned above can be found in the

Table 1. Comparison of stock options and RSUs (Y. Lee, 2024).

	Stock Option	RSUs
Management Perspective	- Attracts talent when stock prices rise, but may cause talent to leave when stock prices fall - Limited compensation effect when stock prices fall - High-risk, high-return incentives facilitate implementation of innovation strategies, but involve high risk	- Stable compensation facilitates long-term talent retention - Compensation can be provided regardless of stock price fluctuations - Can be linked to various performance targets in addition to stock prices
Financial and Accounting	- Complex accounting treatment and reporting requirements - Difficulty in determining exercise prices and making predictions	- Simpler accounting treatment compared to stock options - Compensation costs are determined in advance and are predictable compared to stock options
Compensation Structure	- Compensation value is determined based on exercise prices and stock prices - Compensation amounts are uncertain due to stock price volatility - No compensation value if stock price declines at exercise date - No cash outlay, taxes incurred at stock exercise date (for NQSO)	- Guaranteed number of shares after a certain period - Predictable compensation based on a fixed number of shares - Maintains number of shares even if stock price declines - Receive shares without cash outlay
Performance Incentives	- Ability to set long-term performance goals centered on stock price - Motivates employees through expected returns from stock price increases	- Can be linked to various performance goals - Provides stable compensation and enables team and performance management

below.

2.2. Previous Research Trends

Previous studies have analyzed the impact of RSUs on corporate financial performance from various perspectives. Bhagat et al. (2014) found that RSUs contribute to improving the quality of corporate financial reporting and the efficiency of earnings management, while Hou et al. (2020) empirically analyzed the effect of RSUs on reducing the motivation to avoid losses and encouraging management to consider maximizing long-term profits rather than short-term stock price management. These findings suggest that RSUs can play a role in enhancing accounting transparency and strengthening the long-term financial health of companies.

RSUs are evaluated as a strategic means of compensation that increases long-term performance and sustainability, rather than short-term performance rewards. A study by Murphy and Vance (2019) found that RSUs induce long-term performance through management behavior adjustment and psychological satisfaction. In addition, Tai (2018) empirically found that the introduction of RSUs is significantly related to corporate sales growth. In particular, Lovett et al. (2022) analyzed that RSUs are a more suitable compensation method for new CEOs and are likely to promote long-term performance creation. This suggests that RSUs can play an important role in supporting the sustainable growth of companies.

However, some studies have pointed out that the incentive effect of RSUs may be limited. Lambert and Larcker (2004) found that since RSUs are not directly linked to stock prices, they may not strongly motivate management to increase stock prices. In addition, Blouin and Carter (2010) pointed out that if a company over-issues RSUs to its employees, the opportunity to separately recognize revenue may be limited. Kanagaretnam et al. (2009) found that RSUs reduce management’s short-term stock price management motivation, but if the incentive effect is insufficient, the performance linkage may be weakened. In addition, Meulbroek (2001) pointed out that if stock-based compensation becomes too large, it may actually lead to inefficient decision-making.

There are differences between the studies on RSUs conducted in the early days of their introduction and recent studies, and while RSUs were viewed as an alternative to stock options from 2003 to before 2010, some empirical studies have produced negative results. On the other hand, since the mid-2010s, the number of companies adopting RSUs has increased, and studies that prove the positive effects of RSUs in the areas of finance and human resources have emerged. In particular, interest in RSUs has continued to grow as more studies have shown that RSUs are more effective than stock options in improving a company’s long-term performance.

Based on a review of existing studies, RSUs have been partially and qualitatively verified to have positive effects on improving the quality of management performance and inducing long-term performance. In particular, since 2010, studies have increasingly emphasized that RSUs have the effect of supporting the sustainable growth of companies and can be a more effective means of long-term compensation than stock options. Therefore, when companies introduce RSUs, it is important to strengthen performance-linkedness and establish a compensation strategy that takes into account the characteristics of the company and differences by industry. This study aims to verify the long-term effects of RSU introduction, which have been insufficiently examined in previous research, and to examine the differential impact of RSUs across industries, providing a clearer analysis of the effectiveness of RSUs.

2.3. The Importance of EPS and Operating Profit

This research focuses on earnings per share (EPS) and operating income as primary financial indicators to assess the influence of RSU implementation on corporate performance. These metrics are widely regarded as stable measures of long-term profitability and shareholder value enhancement, making them well-suited for studies aiming to minimize the effect of short-term stock volatility.

EPS, a widely used profitability measure from the investor’s viewpoint, is computed by dividing net income by the total number of outstanding shares (Bhatt & Sumangala, 2012). It provides valuable insights into how equity-based compensation like RSUs influences the net income structure and serves as a reliable variable for long-term performance evaluation. In addition, EPS is a foundational component of financial ratios such as the price-to-earnings ratio (PER), further reinforcing its relevance in corporate valuation (Young & Yang, 2011).

Beyond its role as a profit indicator, EPS captures the broader effects of capital structure, cost management, and financial leverage strategies. Chandren et al. (2015) noted that EPS is strongly linked to decisions involving dividends, debt financing, and capital raising, making it highly appropriate for assessing shifts in financial policy. Importantly, it remains relatively unaffected by transient external conditions, which enhances its value as a long-range performance gauge.

Operating income, in contrast, isolates the core operational performance by excluding irregular gains or losses and non-recurring items, offering a clearer picture of a firm’s ability to generate sustainable earnings (Jayathilaka, 2020). This makes it a relevant indicator for examining how RSUs impact internal efficiency and operational capabilities.

EPS also reveals the strategic alignment between a company’s financial decisions and its enduring outcomes (Bhatt & Sumangala, 2012). For example, a declining EPS in the face of rising revenue may indicate inefficiencies such as weak cost controls or share dilution. According to Chandren et al. (2015), EPS effectively captures financial policy changes involving dividends, buybacks, and capital structure modifications.

Another key strength of EPS is its utility in cross-company comparisons, particularly when firms differ in their capital structure. Even when net incomes are identical, EPS varies with the number of shares, offering a more precise reflection of profitability per shareholder. This feature makes EPS a superior benchmark compared to raw revenue or operating income.

Moreover, EPS is often correlated with the dividend policy. Improvements in EPS following RSU implementation may enhance a company’s capacity for dividend payouts, thereby increasing shareholder returns. Zhu and Ibrahim (2024) observed that RSUs help mitigate short-term performance pressures and promote sustainability, though their work was criticized for relying mainly on indirect evidence.

Crucially, EPS remains relatively stable against external economic fluctuations such as interest rate changes or inflation. Chandren et al. (2015) argued that this stability makes EPS well suited for long-term financial analysis, particularly when evaluating the role of stock-based incentives like RSUs in reinforcing fiscal discipline.

Operating profit is also used in this study as a complementary indicator. Since it reflects the profitability derived from core operations, it is useful for gauging the operational impact of RSUs (Jayathilaka, 2020). Prior studies, including those by Panggabean et al. (2021) and Senewe et al. (2021), also employed operating profit in examining the relationship between leverage and EPS, confirming its reliability as a financial variable.

3. Research Design

3.1. Information of Data-Set for Research

This research draws upon data from the Bloomberg Finance Database, focusing on companies listed in the S&P 500 between 1997 and 2023. These companies were chosen specifically because they operate under stringent U.S. accounting rules and comprehensive disclosure requirements. Such consistency and transparency in financial and managerial reporting enhance the validity of longitudinal analysis, particularly when evaluating the impact of RSU adoption over time (Baranyi et al., 2023).

As the S&P 500 is widely regarded as a barometer of the U.S. economy, its constituent companies are frequently used as benchmarks in academic and investment research. These firms reflect the practices of established, large-scale enterprises, making them especially suitable for examining how RSUs affect corporate performance and shareholder value. Their robust data infrastructure enables researchers to investigate long-term financial effects with greater accuracy and reliability (Baranyi et al., 2023).

To maintain analytical consistency, this study excludes financial sector companies from the sample. Financial firms differ fundamentally from other industries in terms of their accounting principles and performance metrics. Elements such as interest income, loan loss provisions, and derivative exposures introduce complexities that can distort profitability analyses. Notably, fluctuations in interest rates can directly impact asset valuations and loan reserves, significantly affecting EPS in ways that are not comparable to non-financial firms (Aras & Crowther, 2010; Abubakar et al., 2016).

Startups and small companies were also excluded from the analysis. Early-stage companies often face substantial financial volatility and frequently report negligible or negative earnings, making them unsuitable for analysis using profitability metrics like EPS. Since EPS—net income divided by the number of outstanding shares—serves as a measure of consistent profitability, its use is limited when applied to firms with erratic financial records (Silva et al., 2022).

Moreover, startups typically rely on stock options as a primary compensation tool. These instruments are better suited to high-risk, growth-oriented environments, whereas RSUs are designed to promote stability and predictability over the long term. This fundamental difference makes RSUs less relevant in startup contexts and complicates efforts to assess their effectiveness in such settings (Henrekson & Sanandaji, 2016). For this reason, the scope of this research is deliberately restricted to S&P 500 companies, where RSU practices can be more reliably and meaningfully analyzed.

3.2. Industrial Classification Methods and Criteria

The S&P 500 companies selected using the above criteria were first classified according to the Global Industry Classification Standard (GICS), and then subdivided into five groups, taking into account the inter-group relationships.

The GICS is a global industry classification system jointly developed by Morgan Stanley Capital International (MSCI) and S&P Dow Jones Indices, and is used as a standard for classifying companies according to their economic and industrial characteristics. This classification system provides investors with a unified standard and is designed to reflect a company’s core business model and main source of revenue. GICS consists of four hierarchical structures, namely Sector, Industry Group, Industry, and Sub-Industry, and follows a classification method that categorizes companies based on the sector in which their main source of revenue is generated.

The existing GICS industry classification system consists of a total of 11 sectors, but in this study, the 11 sectors were combined into five major groups for analysis, taking into account the similarities and economic characteristics of each industry.

The reason for this reorganization is the need to group them based on similarities and economic attributes across industries. Some GICS groups share similar characteristics in terms of their economic nature and how they generate profits, so the key objective is to increase the consistency of the research and to analyze the relationship between corporate performances more clearly. Therefore, it is believed that using larger industry groups will contribute to improving the practicality of the research.

The classification of industries grouped by this criterion is shown in

Table 2. GICS-based industry classification.

Category	Category Detail	Entry
Group 1	Consumer-Related Industries	Consumer Staples, Consumer Discretionary
Group 2	Resource and Energy-Related Industries	Energy, Materials, Utilities
Group 3	Industrial and Infrastructure- Related Industries	Industrials, Real Estate
Group 4	Technology and Communication-Related Industries	Information Technology, Communication Services
Group 5	Health Related Industries	Health Care

below.

The groups were divided based on the similarity of the nature and structure of the industries. Group 1 is a consumer-related industry, which combines consumer discretionary and consumer staples. The two industries are closely related to consumer spending and are similarly affected by changes in consumption patterns. According to a study by Guericke (2014), the consumer discretionary and consumer staples sectors are similarly affected by macroeconomic variables such as unemployment, inflation, and changes in income, and the level of consumers’ disposable income and economic fluctuations are important factors in the performance of both industries. Therefore, this study analyzed the two industries as a single group.

Group 2 is the resource and energy-related industries, which integrates the energy, materials, and utilities sectors. According to a study by Mendez-Alva et al. (2021), energy-intensive industries are highly linked in terms of raw material supply chains, energy consumption, waste recycling, and carbon emissions, and are characterized by pursuing resource optimization through inter-industry cooperation. Accordingly, this study analyzed these industries as a single group.

Group 3 comprises industry and infrastructure, which combines industrial companies and real estate. Both industries are based on physical assets, and long-term capital investment and infrastructure development are important factors. They are also heavily influenced by macroeconomic factors such as economic growth, interest rate fluctuations, and government policies, and share similar characteristics in terms of capital expenditure (CAPEX).

According to a study by Singh and Guleria (2012), real estate development acts as a key factor in economic growth and infrastructure construction, and large-scale real estate projects and industrial development share a common pattern of capital expenditure. In particular, the analysis of the impact of government policies and macroeconomic factors on industrial and real estate development highlights the similarity of the two industries in their long-term investment and growth strategies.

Group 4 is the technology and telecommunications industry, which combines the Information Technology and Communication Services sectors. Both industries operate on information and communication technology and are highly dependent on digital innovation and network infrastructure.

According to a study by Rahim and Ahmad (2018), information technology and communication services are the main driving force of the Fourth Industrial Revolution and are converging in various fields such as cloud computing, the Internet of Things (IoT), big data, and smart cities. In addition, the boundaries between the two industries are becoming increasingly blurred as IT infrastructure and network technologies play complementary roles.

Group 5 is the health-related industry, which has been separated into an independent group that includes only the healthcare industry.

3.3. Validity of Industrial Classification and Grouping Criteria

As previously explained, this study pre-classified companies based on the GICS framework and integrated them into five industry groups. Some may criticize this approach, arguing that clustering techniques (such as k-means clustering) should have been used to form clusters based on data. However, this study was conducted under the clear hypothesis that ‘the effect of RSUs introduction varies depending on industry characteristics’, so it was necessary to adopt a pre-classification approach.

The GICS system is a global standard industrial classification system that was jointly developed by Morgan Stanley and S&P, and is widely used as a reliable benchmark reflecting economic and industrial characteristics (Bhojraj et al., 2003). In particular, GICS has been empirically proven to capture market-based characteristics and financial homogeneity more accurately than the existing SIC classification. Hoberg et al. (2014) confirmed that the GICS framework remains a highly effective criterion for explaining economic similarity and competitive threats between companies.

Therefore, the use of GICS as the basis for grouping in this study is not an arbitrary approach but rather an analytical methodology grounded in a validated theoretical framework. This aligns with the findings of Hoberg and Phillips (2016), who found that traditional classification systems remain a reliable starting point for reflecting economic similarity.

On the other hand, cluster analysis is an exploratory technique aimed at identifying natural clusters within data. However, since this study aimed to verify the hypothesis that ‘the RSUs effect will differ by industry structure’, a systematic classification reflecting industry structure was more consistent with the research design than random clustering. This demonstrates that this study is not a simple exploratory study but an empirical study based on a clear theoretical foundation.

Furthermore, this study did not simply integrate the existing 11 GICS sectors but restructured them into five industry groups by considering the similarity of each industry’s economic characteristics and revenue generation patterns. This industry restructuring enhances the practicality and analytical consistency of the study while strengthening the homogeneity within industry groups and the comparability between groups.

Indeed, the significant influence of industry characteristics on corporate profitability and investment behavior has been empirically confirmed in the research of Heston and Rouwenhorst (1994). Additionally, Chan et al. (2007) demonstrated that industry classification systems can effectively explain return co-movement between companies, emphasizing the necessity of industry characteristic-based analysis. Bartram et al. (2012) also empirically demonstrated that industrial structure causes structural differences in companies’ stock price volatility and financial performance, thereby showing that industry characteristics can have a substantial impact on companies’ financial performance. These findings suggest that analyzing companies without considering the industry structure may lead to distorted results. This further supports the validity and reliability of the research design in this study, which distinguishes companies based on industry-specific characteristics and analyzes the effects of RSU implementation.

3.4. Theoretical Basis for Hierarchical Regression and Control Variable Selection

This research utilized hierarchical regression analysis to empirically analyze the effects of RSUs on EPS and operating income from both short-term and long-term perspectives. Hierarchical regression analysis is a suitable methodology for verifying the independent effects of key variables by sequentially incorporating independent variables based on their theoretical importance and evaluating changes in the explanatory power (Adjusted R²) at each stage (Cohen et al., 2003).

The objective of this study is not merely to observe the fragmented differences before and after RSU adoption, but to systematically verify the pure impact of RSU introduction on corporate financial performance by controlling for exogenous factors such as macroeconomic variables (inflation rate, interest rate, economic growth rate) and company characteristics (total assets, ROA, operating profit). To achieve this, in Stage 1 (Model 1), a basic model including exogenous variables was constructed, and in Stage 2 (Model 2), the introduction of RSUs was added to examine the increase in the model’s explanatory power and the significance of the independent variables. This stepwise approach aligns with the research design and analytical objectives, as it allows for a direct examination of the increase in explanatory power resulting from the addition of variables (Cohen et al., 2003).

In particular, to demonstrate that the introduction of RSU is an independent institutional change affecting corporate performance rather than a result of short-term market changes or overall economic recovery, a hierarchical regression approach is required, which first controls for exogenous variables and then includes the main explanatory variables. Petrocelli (2003) also emphasized hierarchical regression analysis as a method to verify the pure effects of key variables by adequately controlling external factors.

To gain a more accurate understanding of how RSU adoption affects company performance, this research incorporates a range of financial and macroeconomic variables—previously found to be associated with EPS—as independent variables in the analysis. The financial metrics considered include operating income, total assets, and return on assets (ROA), while the macroeconomic indicators include the inflation rate, interest rate, and economic growth rate. These variables have been widely acknowledged in prior empirical studies as key determinants of EPS. However, when operating profit is treated as the dependent variable, including it again among the independent variables can result in redundancy. To prevent such overlap, the operating profit variable was excluded from the model in those specific cases.

Sunartiyo (2018), for instance, emphasized that inflation has measurable effects on both EPS and stock prices, highlighting that changes in EPS often correlate positively with share price movements.

In relation to operating profit, Panggabean et al. (2021) investigated its influence on both operating leverage and EPS. Their findings suggest that an improved operating profit often leads to higher EPS, reinforcing the idea that EPS reflects a company’s core profitability and directly relates to its market valuation.

For ROA, Senewe et al. (2021) reported that companies exhibiting higher ROA tend to see increases in both their EPS and share price, implying that ROA is a strong indicator of how efficiently a company utilizes its assets.

Turning to the macroeconomic context, Sinurat et al. (2023) explored the relationship between EPS growth and national economic growth, and concluded that this linkage meaningfully shapes investor expectations for future stock returns. They further noted that robust economic growth does not always translate into capital market gains if EPS growth remains weak, cautioning against over-reliance on macroeconomic expansion alone.

Grounded in these previous findings, this study employed hierarchical regression analysis to assess the isolated and combined influence of each independent variable on EPS. Financial data such as revenue, operating income, and total assets were compiled from company filings with the U.S. Securities and Exchange Commission (SEC) and the Bloomberg database. Meanwhile, macroeconomic figures—including inflation, interest rates, and GDP growth—were sourced from authoritative databases such as the World Bank and the International Monetary Fund (IMF), covering the period from 1997 to 2023.

The empirical approach was structured in two stages. In the first stage, regression was performed using only financial and macroeconomic indicators, omitting RSU variables to establish a baseline. In the second stage, RSU adoption was introduced into the model as a dummy variable, and the resulting change in the adjusted R-squared (Adjusted R²) was evaluated. This two-step process allowed the study to quantify the unique impact of RSUs on EPS after controlling for other factors.

Table 3. H1.2. result for Group 1 (EPS effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	2.676	0.803	-	3.332	-	-	1.629	0.799	-	2.039	-	-
Inflation	−16.977	21.304	−0.122	−0.797	0.436	2.292	−19.925	19.821	−0.143	−1.005	0.436	2.296
Interest Rate	−11.060	10.802	−0.178	−1.128	0.408	2.452	−9.869	9.131	−0.143	−0.974	0.408	2.462
Economic Growth Rate	−11.358	15.572	−0.111	−0.729	0.444	2.254	−12.833	14.481	−0.125	−0.886	0.443	2.256
ROA	0.025	0.026	0.110	0.946	0.758	1.319	0.021	0.024	0.093	0.864	0.757	1.321
Total Assets	0.000	0.000	−0.131	−1.141	0.773	1.293	0.000	0.000	−0.121	−0.940	0.769	1.301
Operating Profits	3.0225	0.865	0.391	3.482	0.812	1.232	2.2535	0.835	0.291	2.704	0.761	1.315
RSUs Adoption	-	-	-	-	-	-	0.815	0.221	0.357	3.685	0.937	1.067
	R2 = 0.183, Adjusted R2 = 0.122 F = 2.996, p < 0.05						R2 = 0.303, Adjusted R2 = 0.241 ⊿R2 = 0.120, ⊿Adjusted R2 = 0.119 F = 4.912, p < 0.001
Durbin– Watson	-						1.947

outlines the financial and macroeconomic variables utilized in the analysis.

3.5. Hypotheses of Research and Analytical Framework

This study aims to analyze the impact of RSUs on EPS and operating profit. This study classifies the group based on the GICS and compares and analyzes the changes in the EPS and operating profit before and after the introduction of RSUs to empirically verify the long-term performance improvement effect of RSUs.

In particular, this research aims to increase the reliability of the study by analyzing both EPS and operating income. EPS is a financial performance indicator directly linked to shareholder value (Young & Yang, 2011), and operating profit is used as an indicator that reflects the actual profitability generated by the core business activities of a company (Jayathilaka, 2020). Therefore, a comprehensive analysis of the two indicators can more clearly identify the effect of RSUs on improving corporate performance.

In this study, the following research hypotheses were set to analyze the impact of the introduction of RSUs on corporate performance.

Hypothesis 1.

There is a statistically significant difference in EPS between the five-year period prior to the adoption of RSUs and the five years following the adoption.

Hypothesis 2.

There is a statistically significant difference in EPS between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

Hypothesis 3.

There is a statistically significant difference in operating profit between the five-year period prior to the adoption of RSUs and the five years following the adoption.

Hypothesis 4.

There is a statistically significant difference in operating profit between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

The specific hypotheses applied to each industry group are as follows.

• Hypotheses related to Group 1

H1.1. 

There is a statistically significant difference in EPS for Group 1 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H1.2. 

There is a statistically significant difference in EPS for Group 1 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

H1.3. 

There is a statistically significant difference in operating profit for Group 1 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H1.4. 

There is a statistically significant difference in operating profit for Group 1 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

• Hypotheses related to Group 2

H2.1. 

There is a statistically significant difference in EPS for Group 2 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H2.2. 

There is a statistically significant difference in EPS for Group 2 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

H2.3. 

There is a statistically significant difference in operating profit for Group 2 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H2.4. 

There is a statistically significant difference in operating profit for Group 2 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

• Hypotheses related to Group 3

H3.1. 

There is a statistically significant difference in EPS for Group 3 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H3.2. 

There is a statistically significant difference in EPS for Group 3 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

H3.3. 

There is a statistically significant difference in operating profit for Group 3 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H3.4. 

There is a statistically significant difference in operating profit for Group 3 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

• Hypotheses related to Group 4

H4.1. 

There is a statistically significant difference in EPS for Group 4 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H4.2. 

There is a statistically significant difference in EPS for Group 4 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

H4.3. 

There is a statistically significant difference in operating profit for Group 4 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H4.4. 

There is a statistically significant difference in operating profit for Group 4 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

• Hypotheses related to Group 5

H5.1. 

There is a statistically significant difference in EPS for Group 5 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H5.2. 

There is a statistically significant difference in EPS for Group 5 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

H5.3. 

There is a statistically significant difference in operating profit for Group 5 between the five-year period prior to the adoption of RSUs and the five years following the adoption.

H5.4. 

There is a statistically significant difference in operating profit for Group 5 between the five-year period prior to the adoption of RSUs and the sixth to tenth years following the adoption.

In this research, hierarchical regression analysis was used to empirically verify the impact of RSUs on company performance. This was done to evaluate the effects of RSUs in stages and to comprehensively analyze changes in the EPS and operating profit to increase the reliability of the study. In this research, EPS and operating income were set as dependent variables. The EPS independent variables included total assets, ROA, operating income, inflation, interest rates, and economic growth, while the operating income independent variables included total assets, ROA, inflation, interest rates, and economic growth. This was done to analyze the pure effect of introducing RSUs on company performance.

Hierarchical regression analysis was conducted in two stages. In the first stage (Model 1), we analyzed the volatility in EPS and operating income, including key macroeconomic and financial variables, excluding the introduction of RSUs. In the second stage (Model 2), we added a dummy variable for the introduction of RSUs to evaluate the impact of the introduction of RSUs on corporate performance. This allowed us to verify the explanatory power of the introduction of RSUs on company performance and analyze the long-term performance improvement effect.

4. Result

4.1. Results of Hypotheses H1.1.–H1.4. Regarding Group 1

To verify the hypotheses from H1.1. to H1.4., four regression analyses were conducted based on the same sample (N = 88), and the short-term and long-term effects of introducing RSUs on EPS and operating profit in Group 1 were analyzed. The results of the analysis are presented in Table 3,

Table 4. H1.1. result for Group 1 (EPS effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	Β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	2.291	0.728	-	4.013	-	-	2.290	0.764	-	2.998	-	-
Inflation	−27.314	19.261	−0.210	−1.418	0.434	2.305	−28.218	18.801	−0.217	−1.501	0.434	2.307
Interest Rate	−8.170	8.789	−0.142	−0.930	0.411	2.431	−7.516	8.582	−0.130	−0.876	0.411	2.434
Economic Growth Rate	−17.906	14.052	−0.187	−1.274	0.444	2.251	−18.427	13.715	−0.192	−1.344	0.444	2.252
ROA	0.020	0.024	0.095	0.847	0.750	1.333	0.019	0.023	0.089	0.876	0.750	1.334
Total Assets	0.000	0.000	−0.152	−1.392	0.804	1.244	0.000	0.000	−0.146	−1.372	0.803	1.245
Operating Profits	0.000	0.000	0.384	3.620	0.846	1.182	0.000	0.000	0.355	3.397	0.833	1.201
RSUs Adoption	-	-	-	-	-	-	0.457	0.203	0.216	2.247	0.984	1.016
	R2 = 0.227, Adjusted R2 = 0.170 F = 3.972, p < 0.01						R2 = 0.273, Adjusted R2 = 0.210 ⊿R2 = 0.046, ⊿Adjusted R2 = 0.040 F = 4.296, p < 0.001
Durbin– Watson	-						2.182

,

Table 5. H1.3. result for Group 1 (operating profit effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	9.904	0.706	-	14.034	-	-	9.347	0.752	-	12.434	-	-
Inflation	−41.029	18.994	−0.287	−2.160	0.451	2.217	−41.697	18.686	−0.292	−2.231	0.451	2.218
Interest Rate	27.067	8.453	0.431	3.202	0.440	2.274	27.232	8.316	0.435	3.286	0.440	2.274
Economic Growth Rate	−33.477	10.247	−0.314	−3.267	0.472	2.171	−33.743	13.621	−0.317	−2.477	0.472	2.171
ROA	−0.040	0.023	−0.172	−1.714	0.792	1.263	−0.040	0.023	−0.175	−1.772	0.792	1.263
Total Assets	0.000	0.500	0.429	4.469	0.867	1.153	0.000	0.500	0.427	4.366	0.861	1.161
RSUs Adoption	-	-	-	-	-	-	0.393	0.204	0.170	1.930	0.992	1.008
	R2 = 0.353, Adjusted R2 = 0.313 F = 8.194, p < 0.001						R2 = 0.382, Adjusted R2 = 0.336 ⊿R2 = 0.029, ⊿Adjusted R2 = 0.023 F = 8.246, p < 0.001
Durbin– Watson	-						1.977

and

Table 6. H1.4. result for Group 1 (operating profit effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	9.940	0.715	-	13.909	-	-	8.896	0.719	-	12.388	-	-
Inflation	−41.561	19.227	−0.285	−2.162	0.452	2.211	−42.656	17.837	−0.293	−2.391	0.452	2.212
Interest Rate	27.690	8.585	0.432	3.225	0.439	2.276	27.913	7.904	0.436	3.305	0.439	2.274
Economic Growth Rate	−31.724	14.036	−0.292	−2.260	0.471	2.123	−31.946	13.708	−0.294	−2.494	0.471	2.123
ROA	−0.032	0.024	−0.135	−1.353	0.790	1.266	−0.033	0.024	−0.138	−1.491	0.790	1.264
Total Assets	0.000	0.000	0.466	4.883	0.858	1.165	0.000	0.000	0.442	4.979	0.858	1.165
RSUs Adoption	-	-	-	-	-	-	0.730	0.195	0.309	3.738	0.994	1.006
	R2 = 0.370, Adjusted R2 = 0.331 F = 9.398, p < 0.001						R2 = 0.465, Adjusted R2 = 0.424 ⊿R2 = 0.095, ⊿Adjusted R2 = 0.093 F = 11.431, p < 0.001
Durbin– Watson	-						1.947

.

The results (H1.1. to H1.4.) of the preliminary evaluation to ensure the validity of the regression analysis showed that the tolerance coefficients of all independent variables exceeded 0.1 and that the variance inflation factors (VIF) were less than 10, confirming that there was no multicollinearity problem. Additionally, the Durbin–Watson statistic was generally distributed between 1.9 and 2.2, indicating no autocorrelation among the residuals.

To verify H1.1., an analysis was conducted to compare the changes in EPS before and after the introduction of RSUs over a five-year period. In Model 1, key financial indicators were set as independent variables, and the results showed that the adjusted R² was 0.170 (F = 3.972, p < 0.01), indicating that the model was statistically significant. In particular, operating profit was found to be a significant factor influencing EPS (t = 3.620, p < 0.01). In Model 2, which included the introduction of RSUs as a dummy variable, the adjusted R² increased to 0.210, improving the explanatory power by approximately 4.0 percentage points (F = 4.296, p < 0.001), and the RSU variable was analyzed as having a significant impact on EPS (t = 2.247, p < 0.05). This indicates that the introduction of RSUs contributed to an increase in EPS in the short term.

Next, in Hypothesis H1.2., the same sample was used to compare EPS in the five years before the introduction of RSUs and in the six to ten years after the introduction to analyze the long-term effects. The adjusted R² of Model 1 was 0.122 (F = 2.996, p < 0.05), and operating profit emerged as a variable with strong explanatory power for EPS (t = 3.482, p < 0.01). In Model 2, adding the RSUs variable increased the adjusted R² to 0.241 (F = 4.912, p < 0.001), and the variable was confirmed as a highly significant predictor (t = 3.685, p < 0.001). This indicates that the long-term impact of RSUs on EPS is more than three times higher than the short-term impact.

When comparing the adjusted R² of H1.1. and H1.2., the increase in the explanatory power of H1.2. (11.9%) with RSUs as an independent variable is much greater than the increase in H1.1. (4.0%). This is most likely due to the fact that the data used in H1.2. consist of data from 6 to 10 years after the introduction of RSUs. On the other hand, since H1.1. targeted the period of 1–5 years after the introduction of RSUs, the impact of RSUs may have appeared relatively small in the short term.

When comparing the significance of the RSU introduction variable in the two statistical results, H1.1. shows that the regression coefficient of the RSU introduction variable is t = 2.247, p < 0.05, but H1.2. shows that the regression coefficient of the RSU introduction variable is t = 3.685, p < 0.001, showing a much stronger significance than H1.1. Consequently, the introduction of RSUs in Group 1 has a significant impact on EPS in the short term, but the effect tends to become stronger in the long term.

Meanwhile, H1.3. analyzed the short-term impact of RSUs on operating profit. In Model 1, macroeconomic and financial variables such as interest rates, the economic growth rate, and total assets were set as independent variables, and the adjusted R² was 0.313 (F = 8.194, p < 0.001). Among these, interest rates (t = 3.202, p < 0.01), the economic growth rate (t = −3.267, p < 0.01), and total assets (t = 4.469, p < 0.001) were identified as significant explanatory variables. In Model 2, which reflects the adoption of RSUs, the adjusted R² increased by approximately 2.3 percentage points to 0.336 (F = 8.246, p < 0.001), and the RSU variable showed an influence that was close to statistically significant (t = 1.930, p < 0.1).

Finally, in H1.4., we compared the changes in operating profit over the five years prior to the introduction of RSUs and the six to ten years following their introduction from a long-term perspective. The adjusted R² in Model 1 was 0.331 (F = 9.398, p < 0.001), and the interest rate (t = 3.225, p < 0.01), economic growth rate (t = −2.260, p < 0.05), and total assets (t = 4.883, p < 0.001) were all significant variables. When the RSU variable was added to Model 2, the adjusted R² increased to 0.424 (F = 11.431, p < 0.001), and the RSU variable was found to have a highly significant effect on operating profit (t = 3.738, p < 0.001). This indicates that RSUs are an important variable in explaining a company’s operating performance in the long term. When comparing the adjusted R² of H1.3. and H1.4., the increase in the explanatory power of H1.4. (9.3%) with RSUs as an independent variable is much greater than that of H1.3. (2.3%). This is most likely because the data used in H1.4. consist of data from 6 to 10 years after the adoption of RSUs. On the other hand, since H1.3. targeted the 5-year period after RSU adoption, the impact of RSUs may have appeared relatively small in the short term.

When comparing the significance of the RSU adoption variable in the two statistical results, it was found that the regression coefficient of the RSU adoption variable in H1.3. was t = 1.930, p < 0.1, showing weak significance at a statistically significant level. On the other hand, in H1.4, the regression coefficient of the RSU adoption variable was t = 3.738, p < 0.001, showing a stronger significance than H1.3. In conclusion, the introduction of RSUs in Group 1 may have a positive impact on operating profit in the short term, but the effect tends to be more pronounced in the long term.

When the analysis of Group 1 is combined, the impact of RSU adoption on EPS and operating profit shows a similar pattern, and the effect tends to be more pronounced in the long run. These results suggest that RSU adoption is an important explanatory variable in both EPS and operating profit changes; in particular, it is found to have a stronger explanatory power in the long run.

Moreover, when comparing the significance of the variables of RSUs, the impact of RSUs on EPS and operating profit was found to be weak in the short term, but the effect tended to be stronger in the long term. In particular, the EPS analysis of Group 1 showed that RSU adoption had a significant impact in the short term, but the operating profit showed weak statistical significance in the short term. This suggests that RSUs in Group 1 may be reflected more quickly in EPS than in operating profit, and that the effect of operating profit may be interpreted as gradually appearing over time.

4.2. Results of Hypotheses H2.1.–H2.4. Regarding Group 2

To examine the hypotheses from H2.1. to H2.4., four separate regression analyses were carried out using the same sample (N = 96), aiming to assess both the short-term and long-term impacts of RSU introduction on the EPS and operating profit in Group 2. Detailed results are presented in

Table 7. H2.1. result for Group 2 (EPS effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	1.803	0.470	-	3.833	-	-	1.380	0.513	-	2.692	-	-
Inflation	11.845	13.238	0.111	0.891	0.543	1.841	11.669	13.091	0.109	0.892	0.542	1.844
Interest Rate	−12.424	6.222	−0.306	−1.997	0.398	2.794	−12.404	6.129	−0.305	−2.024	0.398	2.794
Economic Growth Rate	18.400	11.238	0.216	1.637	0.484	2.067	18.312	11.026	0.215	1.661	0.484	2.066
ROA	−0.011	0.015	−0.073	−0.787	0.793	1.263	−0.011	0.015	−0.073	−0.787	0.793	1.263
Total Assets	0.000	0.000	0.291	2.391	0.566	1.768	0.000	0.000	0.294	2.492	0.566	1.768
Operating Profits	0.000	0.000	0.258	2.154	0.583	1.715	0.000	0.000	0.246	2.075	0.581	1.720
RSUs Adoption	-	-	-	-	-	-	0.286	0.148	0.174	1.926	0.997	1.003
	R2 = 0.253, Adjusted R2 = 0.203 F = 5.027, p < 0.001						R2 = 0.283, Adjusted R2 = 0.226 ⊿R2 = 0.030, ⊿Adjusted R2 = 0.023 F = 4.970, p < 0.001
Durbin– Watson	-						2.401

,

Table 8. H2.2. result for Group 2 (EPS effect of Long-term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	2.326	0.496	-	4.685	-	-	1.634	0.531	-	3.084	-	-
Inflation	3.773	14.110	0.035	0.267	0.545	1.834	4.313	13.099	0.039	0.329	0.545	1.834
Interest Rate	−8.516	6.674	−0.205	−1.276	0.353	2.833	−8.721	6.399	−0.210	−1.363	0.353	2.835
Economic Growth Rate	6.072	12.080	0.077	0.503	0.485	2.067	6.702	12.067	0.077	0.555	0.485	2.067
ROA	−0.011	0.016	−0.070	−0.708	0.932	1.073	−0.011	0.016	−0.070	−0.703	0.932	1.073
Total Assets	0.000	0.000	0.298	2.487	0.658	1.521	0.000	0.000	0.298	2.569	0.658	1.521
Operating Profits	0.000	0.000	0.172	1.458	0.658	1.521	0.000	0.000	0.178	1.574	0.658	1.521
RSUs Adoption	-	-	-	-	-	-	0.455	0.154	0.271	2.953	0.999
	R2 = 0.196, Adjusted R2 = 0.141 F = 3.576, p < 0.01						R2 = 0.269, Adjusted R2 = 0.210 ⊿R2 = 0.073, ⊿Adjusted R2 = 0.069 F = 4.580, p < 0.001
Durbin– Watson	-						2.357

,

Table 9. H2.3. result for Group 2 (operating profit effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	Β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	9.015	0.688	-	13.102	-	-	8.895	0.698	-	12.743	-	-
Inflation	−26.827	19.437	−0.162	−1.380	0.552	1.813	−26.827	19.531	−0.162	−1.374	0.552	1.813
Interest Rate	16.948	9.167	0.269	1.849	0.358	2.791	16.948	9.216	0.269	1.840	0.358	2.791
Economic Growth Rate	−1.722	16.545	−0.013	−0.104	0.485	2.106	−1.722	16.602	−0.013	−0.104	0.485	2.106
ROA	0.025	0.022	0.102	1.135	0.940	1.064	0.025	0.023	0.102	1.132	0.940	1.064
Total Assets	0.000	0.000	0.471	5.124	0.898	1.114	0.000	0.000	0.471	5.099	0.898	1.114
RSUs Adoption	-	-	-	-	-	-	0.080	0.223	0.031	0.358	1.000	1.000
	R2 = 0.318, Adjusted R2 = 0.280 F = 8.394, p < 0.001						R2 = 0.319, Adjusted R2 = 0.273 ⊿R2 = 0.001, ⊿Adjusted R2 = −0.007 F = 6.949, p < 0.001
Durbin– Watson	-						2.029

and

Table 10. H2.4. result for Group 2 (operating profit effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	1.054	0.168	-	6.293	-	-	0.819	0.168	-	4.883	-	-
Inflation	−4.614	4.743	−0.124	−0.973	0.543	1.840	−4.207	4.589	−0.113	−0.923	0.543	1.842
Interest Rate	−2.817	2.208	−0.201	−1.276	0.357	2.803	−2.937	2.132	−0.201	−1.384	0.357	2.807
Economic Growth Rate	1.378	2.084	0.128	0.661	0.486	2.058	1.516	2.036	0.138	0.745	0.486	2.058
ROA	−0.007	0.005	−0.123	−1.273	0.940	1.064	−0.007	0.005	−0.123	−1.319	0.940	1.064
Total Assets	0.000	0.000	0.364	3.658	0.894	1.119	0.000	0.000	0.367	3.835	0.894	1.119
RSUs Adoption	-	-	-	-	-	-	0.148	0.051	0.261	2.888	0.999	1.001
	R2 = 0.221, Adjusted R2 = 0.177 F = 5.004, p < 0.001						R2 = 0.290, Adjusted R2 = 0.241 ⊿R2 = 0.069, ⊿Adjusted R2 = 0.064 F = 5.909, p < 0.001
Durbin– Watson	-						2.362

.

To examine the validity of the regression analysis for H2.1. to H2.4., we diagnosed multicollinearity and residual autocorrelation. The results showed that all independent variables had a multicollinearity coefficient exceeding 0.1 and a VIF below 10, indicating no multicollinearity issues. The Durbin–Watson statistic was also found to be between 2.3 and 2.5, confirming a low possibility of residual autocorrelation.

To test H2.1., we compared the changes in EPS before and after the introduction of RSUs over a five-year period using hierarchical regression analysis. In Model 1, the adjusted R² was 0. 203 (F = 5.027, p < 0.001), and total assets (t = 2.391, p < 0.05) and operating profit (t = 2.154, p < 0.05) were identified as variables significantly affecting EPS. In Model 2, after adding the introduction of RSUs as an independent variable, the adjusted R² increased to 0.226 (F = 4.970, p < 0.001), and the RSU variable showed a positive effect at the significance level (t = 1.926, p < 0.1).

H2.2. analyzed the long-term effects by comparing EPS in the five years before the introduction of RSUs and the six to ten years after the introduction, using the same sample. The adjusted R² of Model 1 was 0.141 (F = 3.576, p < 0.01), and total assets (t = 2.487, p < 0.05) emerged as a significant explanatory variable for EPS. In Model 2, which included the RSU variable, the adjusted R² increased to 0.210 (F = 4.580, p < 0.001), and the RSU variable was found to significantly influence EPS at a statistically significant level (t = 2.953, p < 0.01).

When comparing the adjusted R² of H2.1. and H2.2., the increase in the explanatory power of H2.2. (6.9%) with RSUs as an independent variable was much greater than the increase in H2.1. (2.3%). In other words, this suggests that the adoption of RSUs may explain EPS fluctuations more strongly in the long run, and that the effect of RSUs is likely to become more pronounced over time. When comparing the significance of the RSU adoption variable in the two statistical results, the regression coefficient of the RSU adoption variable in H2.1. is t = 1.926, p < 0.1, indicating that it has a somewhat weak effect at a statistically significant level. In contrast, in H2.2, the regression coefficient of the RSU adoption variable was t = 2.953, p < 0.01, showing a much stronger significance than H2.1. Therefore, the adoption of RSUs in Group 2 may have a positive impact on EPS in the short term, but the effect tends to be more pronounced in the long term.

H2.3. analyzes the short-term impact of RSUs on operating profit. In Model 1, inflation, interest rates, the economic growth rate, ROA, total assets, etc., were set as independent variables, and the adjusted R² was 0.280 (F = 8.394, p < 0.001), with total assets identified as a significant variable (t = 5.124, p < 0.001). In Model 2, when the RSU variable was added, the adjusted R² slightly increased to 0.287 (F = 6.949, p < 0.001), but the variable was not statistically significant (t = 0.358, p > 0.1). This suggests that the introduction of RSUs did not have a direct impact on operating profit in the short term.

On the other hand, H2.4. analyzed the long-term changes in operating profit over the 6–10 years following the introduction of RSUs. In Model 1, the adjusted R² was 0.177 (F = 5.004, p < 0.001), with total assets (t = 3.658, p < 0.001) identified as a significant variable. In Model 2, after adding the RSU implementation variable, the adjusted R² increased to 0.241 (F = 5.909, p < 0.001), and the RSU variable was found to have a statistically significant effect on operating profit (t = 2.888, p < 0.01). These results suggest that the introduction of RSUs contributes substantially to operating performance in the long term.

When comparing the adjusted R² of H2.3. and H2.4., the increase in the explanatory power of H2.4. (6.4%) with RSUs as an independent variable is much greater than the increase in H2.3. (0.7%). However, when comparing the significance of the RSU adoption variable in the two statistical results, the regression coefficient of the RSU adoption variable in H2.3. was found to be t = 0.358, p > 0.1, indicating that it does not have a statistically significant effect. On the other hand, in H2.4., the regression coefficient of the adoption of RSUs was t = 2.888, p < 0.01, indicating a significant result. This suggests that the adoption of RSUs does not affect the increase in operating profit in the short term but has a significant impact in the long term. This suggests that the adoption of RSUs is likely to become a more important factor in increasing corporate operating profit over time.

When the analysis of Group 2 is combined, it is found that the impact of RSU adoption on EPS and operating profit shows a similar pattern, and that the effect increases over the long term. These results suggest that the adoption of RSU is a significant variable in both EPS and operating profit changes, and that it has a stronger explanatory power in the long term.

In addition, when comparing the significance of the variables of RSUs, EPS showed that RSUs have a weak significance in the short term, but the effect tended to be stronger in the long term. However, in terms of operating profit, the introduction of RSUs was not significant in the short term, but the effect turned into significant results in the long term.

This suggests that RSUs, like Group 2, may be reflected in EPS before operating profit, and that the effect of operating profit may be interpreted as gradually appearing over time.

4.3. Results of Hypotheses H3.1.–H3.4. Regarding Group 3

To assess the hypotheses from H3.1. to H3.4., four hierarchical regression analyses were performed using a consistent sample of firms (N = 104), with the aim of evaluating both the short-term and long-term effects of Group 3 RSU adoption on EPS and operating income. The complete results are provided in

Table 11. H3.1. result for Group 3 (EPS effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	2.266	0.588	-	3.858	-	-	2.030	0.659	-	3.083	-	-
Inflation	7.969	16.524	0.058	0.482	0.563	1.776	8.992	16.606	0.069	0.541	0.590	1.760
Interest Rate	3.024	6.179	0.072	0.489	0.384	2.606	2.784	6.130	0.070	0.454	0.362	2.647
Economic Growth Rate	−23.123	12.672	−0.269	−1.826	0.346	2.747	−22.736	12.673	−0.269	−1.789	0.364	2.717
ROA	−0.003	0.009	−0.023	−0.332	0.850	1.176	−0.003	0.009	−0.023	−0.317	0.850	1.177
Total Assets	0.000	0.000	−0.306	−1.860	0.303	3.302	0.000	0.000	−0.277	−1.674	0.364	3.264
Operating Profits	0.000	0.000	0.642	3.403	0.331	3.319	0.000	0.000	0.642	3.547	0.281	3.558
RSUs Adoption	-	-	-	-	-	-	0.140	0.175	0.075	0.800	0.930	1.076
	R2 = 0.245, Adjusted R2 = 0.196 F = 4.987, p < 0.001						R2 = 0.251, Adjusted R2 = 0.193 ⊿R2 = 0.006, ⊿Adjusted R2 = −0.003 F = 4.349, p < 0.001
Durbin– Watson	-						1.695

,

Table 12. H3.2. result for Group 3 (EPS effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	3.449	0.621	-	5.553	-	-	2.737	0.713	-	3.838	-	-
Inflation	−19.747	17.500	−0.131	−1.128	0.549	1.820	−14.599	17.540	−0.097	−0.837	0.560	1.785
Interest Rate	5.013	6.360	0.108	0.788	0.391	2.561	4.005	6.299	0.087	0.636	0.383	2.609
Economic Growth Rate	−32.936	13.251	−0.258	−2.485	0.355	2.814	−31.243	13.203	−0.248	−2.366	0.354	2.827
ROA	0.006	0.009	0.062	0.661	0.841	1.189	0.006	0.009	0.060	0.651	0.843	1.186
Total Assets	0.000	0.000	−0.636	−3.604	0.237	4.217	0.000	0.000	−0.277	−2.609	0.248	4.034
Operating Profits	0.000	0.000	0.747	3.888	0.194	5.511	0.000	0.000	0.747	3.888	0.194	5.511
RSUs Adoption	-	-	-	-	-	-	0.374	0.192	0.184	1.945	0.802	1.246
	R2 = 0.299, Adjusted R2 = 0.255 F = 6.766, p < 0.001						R2 = 0.327, Adjusted R2 = 0.276 ⊿R2 = 0.028, ⊿Adjusted R2 = 0.021 F = 6.510, p < 0.001
Durbin– Watson	-						1.741

,

Table 13. H3.3. result for Group 3 (operating profit effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	6.113	0.462	-	13.219	-	-	5.567	0.485	-	11.679	-	-
Inflation	40.337	12.861	0.264	3.136	0.584	1.713	39.967	12.406	0.262	3.221	0.584	1.713
Interest Rate	−9.515	4.891	−0.199	−1.945	0.398	2.513	−9.312	4.862	−0.194	−1.917	0.398	2.516
Economic Growth Rate	20.280	10.153	0.213	1.997	0.365	2.743	19.968	10.123	0.210	2.038	0.364	2.747
ROA	0.002	0.007	0.023	0.328	0.853	1.173	0.002	0.007	0.023	0.342	0.854	1.172
Total Assets	0.000	0.000	0.767	11.563	0.944	1.058	0.000	0.000	0.766	11.979	0.944	1.054
RSUs Adoption	-	-	-	-	-	-	0.373	0.131	0.178	2.861	0.999	1.001
	R2 = 0.602, Adjusted R2 = 0.581 F = 28.992, p < 0.001						R2 = 0.633, Adjusted R2 = 0.610 ⊿R2 = 0.031, ⊿Adjusted R2 = 0.029 F = 27.332, p < 0.001
Durbin– Watson	-						1.955

and

Table 14. H3.4. result for Group 3 (operating profit effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	6.433	0.454	-	14.161	-	-	5.452	0.432	-	13.335	-	-
Inflation	37.895	12.657	0.252	2.994	0.590	1.695	37.895	11.033	0.252	2.435	0.590	1.695
Interest Rate	−6.519	4.771	−0.140	−1.366	0.401	2.494	−6.519	4.622	−0.140	−1.362	0.401	2.494
Economic Growth Rate	12.980	9.981	−0.139	1.300	0.368	2.720	12.980	9.936	−0.139	1.291	0.368	2.720
ROA	−0.002	0.007	−0.019	−0.275	0.852	1.174	−0.002	0.007	−0.019	−0.273	0.852	1.174
Total Assets	0.000	0.000	0.774	11.624	0.944	1.060	0.000	0.000	0.774	13.335	0.944	1.060
RSUs Adoption	-	-	-	-	-	-	0.654	0.116	0.319	5.655	1.000	1.000
	R2 = 0.590, Adjusted R2 = 0.569 F = 28.235, p < 0.001						R2 = 0.692, Adjusted R2 = 0.673 ⊿R2 = 0.102, ⊿Adjusted R2 = 0.104 F = 36.296, p < 0.001
Durbin– Watson	-						2.221

.

To ensure the robustness of the regression models (H3.1. to H3.4.), standard diagnostic checks were applied to test for multicollinearity and residual autocorrelation. All independent variables exhibited tolerance values above 0.1 and VIFs below the threshold of 10, indicating no significant multicollinearity issues. Additionally, the Durbin–Watson statistic ranged between 1.6 and 2.3, supporting the assumption that residuals were independently distributed without serial correlation.

In testing H3.1., which explores the short-term influence of RSU implementation on EPS, hierarchical regression was conducted using data from the five years preceding and following RSUs adoption. Model 1, which included primary financial indicators, yielded an adjusted R² of 0.196 (F = 4.987, p < 0.001), confirming the model’s statistical significance. Within this model, interest rate (t = 2.489, p < 0.05) and operating profit (t = 3.403, p < 0.001) demonstrated significant relationships with EPS. However, upon introducing RSUs in Model 2, the adjusted R² declined slightly to 0.193 (F = 4.349, p < 0.001), and the RSU variable did not show statistical significance (t = 0.800, p > 0.1), suggesting a limited short-term impact on EPS.

Hypothesis H3.2. assessed the long-term relationship between RSU adoption and EPS, comparing data from five years before with that from six to ten years after implementation. Model 1 yielded an adjusted R² of 0.255 (F = 6.766, p < 0.001), with the economic growth rate (t = −2.485, p < 0.05), total assets (t = -3.604, p < 0.001), and operating income (t = 3.888, p < 0.001) used as significant explanatory variables. In Model 2, adding RSUs increased the adjusted R² to 0.276 (F = 6.510, p < 0.001), and the RSU coefficient reached marginal significance (t = 1.945, p < 0.1), indicating that their effect may gradually emerge over time. When comparing the adjusted R² of H3.1. and H3.2., the increase in the explanatory power (2.1%) of H3.2., which includes RSUs as independent variables, is greater than the change in H3.1. (−0.003%). In particular, in H3.1., the R² decreased after the introduction of RSUs, resulting in a decrease in the explanatory power of the model. On the other hand, in H3.2., the explanatory power increased after RSU adoption.

When comparing the significance of the RSU adoption variable in the two statistical results, it was found that the regression coefficient of the RSU adoption variable in H3.1. was t = 0.800, p > 0.1, indicating that it does not have a statistically significant effect. On the other hand, in H3.2., the regression coefficient of the RSU adoption variable was t = 1.945, p < 0.1, showing weak significance.

To test H3.3., the hypothesis that the adoption of RSUs in Group 3 would have a short-term impact on operating profit, we conducted a hierarchical regression analysis of the impact of five years of operating profit without the introduction of RSUs and five years of operating profit during the first five years after the introduction.

For H3.3., the short-term effect of RSUs on operating profit was analyzed. Model 1, which included macroeconomic and financial indicators—such as inflation, interest rate, GDP growth, ROA, and total assets—produced an adjusted R² of 0.581 (F = 28.992, p < 0.001). Inflation (t = 13.219, p < 0.001), economic growth (t = 1.997, p < 0.05), and total assets (t = 11.563, p < 0.001) all had strong explanatory power. Upon the inclusion of RSUs in Model 2, the adjusted R² increased to 0.610 (F = 27.332, p < 0.001), and the RSU coefficient was statistically significant (t = 2.861, p < 0.01), suggesting a meaningful contribution to the short-term operating profit.

H3.4 examined the long-term implications of RSUs on operating income. Comparing the operating performance before adoption and between six and ten years post-adoption, Model 1 produced an adjusted R² of 0.569 (F = 28.235, p < 0.001), with inflation (t = 2.994, p < 0.05), interest rate (t = −1.945, p < 0.1), and total assets (t = 11.624, p < 0.001) emerging as significant predictors. The addition of RSUs in Model 2 increased the adjusted R² to 0.673 (F = 36.296, p < 0.001), and RSUs exhibited a highly significant effect (t = 5.655, p < 0.001), supporting their relevance in improving long-term profitability.

When comparing the adjusted R² of H3.3. and H3.4., the increase in the explanatory power of H3.4. (10.4%) with RSUs as an independent variable is much greater than that of H3.3. (2.1%). In other words, RSU adoption can explain the changes in operating profit more strongly in the long run, suggesting that the effects of RSUs are likely to become more pronounced over time.

When comparing the significance of the RSU adoption variable in the two statistical results, H3.3. shows that the regression coefficient of the RSUs adoption variable is t = 1.945, p < 0.05, confirming that it is a significant influencing variable, but the effect is relatively weak. On the other hand, in H.3.4., the regression coefficient of the RSU introduction variable was t = 5.655, p < 0.001, showing a much stronger significance than H3.3. This suggests that the introduction of RSUs may have a limited impact on operating profit in the short term, but that it has a very significant impact on the increase in the operating profit of the company in the long term.

In the EPS analysis of Group 3, the adoption of RSUs did not have a significant impact in the short term, but there was a tendency for it to have an impact in the long term. However, in the operating profit analysis, the adoption of RSUs had a significant impact in the short term, but the effect was much stronger in the long term. This suggests that in Group 3, RSUs may be reflected more quickly in operating profit than in EPS, and in the case of EPS, the effect may be interpreted as gradually appearing over time.

4.4. Results of Hypotheses H4.1.–H4.4. Regarding Group 4

To assess the hypotheses from H4.1 to H4.4, four hierarchical regression analyses were conducted using a consistent sample of firms (N = 102); this was in order to evaluate the short-term and long-term effects of Group 4 RSU adoption on EPS and operating profit. The detailed results are presented in

Table 15. H4.1. result for Group 4 (EPS effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	Β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	1.295	0.946	-	1.368	-	-	0.869	1.059	-	0.821	-	-
Inflation	2.998	27.168	0.016	0.110	0.463	2.162	4.960	27.136	0.025	0.183	0.461	2.170
Interest Rate	−0.214	11.242	−0.009	−0.019	0.309	2.843	−0.247	11.200	−0.008	−0.022	0.308	2.847
Economic Growth Rate	−4.745	20.279	−0.040	−0.234	0.362	2.781	−3.883	20.132	0.032	−0.193	0.361	2.787
ROA	0.023	0.012	0.215	1.991	0.854	1.131	0.023	0.013	0.213	1.965	0.853	1.134
Total Assets	0.000	0.000	0.153	1.091	0.409	2.246	0.000	0.000	0.237	0.874	0.410	2.250
Operating Profits	0.000	0.000	0.233	1.469	0.409	2.246	0.000	0.000	0.204	1.254	0.393	2.254
RSUs Adoption	-	-	-	-	-	-	0.249	0.277	0.094	0.900	0.957	1.045
	R2 = 0.102, Adjusted R2 = 0.040 F = 1.645, p = 0.144						R2 = 0.110, Adjusted R2 = 0.038 ⊿R2 = 0.008, ⊿Adjusted R2 = −0.002 F = 1.523, p = 0.170
Durbin– Watson	-						1.962

,

Table 16. H4.2. result for Group 4 (EPS effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	1.142	0.700	-	1.631	-	-	0.169	0.735	-	0.230	-	-
Inflation	−1.716	19.899	−0.009	−0.086	0.462	2.165	0.222	18.966	0.001	0.012	0.462	2.167
Interest Rate	2.356	8.305	0.037	0.283	0.358	3.287	1.222	7.919	0.031	0.154	0.358	3.287
Economic Growth Rate	−8.476	14.842	−0.078	−0.571	0.369	2.871	−5.991	14.161	−0.050	−0.423	0.367	2.880
ROA	0.021	0.008	0.194	2.533	0.899	1.113	0.021	0.008	0.189	2.515	0.899	1.113
Total Assets	0.000	0.000	0.696	9.535	0.986	1.014	0.000	0.000	0.696	9.581	0.947	1.056
Operating Profits	0.061	0.006	0.696	9.535	0.986	1.014	0.057	0.006	0.651	9.181	0.947	1.056
RSUs Adoption	-	-	-	-	-	-	0.612	0.194	0.223	3.158	0.958	1.043
	R2 = 0.538, Adjusted R2 = 0.506 F = 17.058, p < 0.001						R2 = 0.585, Adjusted R2 = 0.552 ⊿R2 = 0.047, ⊿Adjusted R2 = 0.046 F = 17.536, p < 0.001
Durbin– Watson	-						1.977

,

Table 17. H4.3. result for Group 4 (operating profit effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	6.835	1.030	-	6.636	-	-	6.148	1.117	-	5.503	-	-
Inflation	0.613	29.389	0.003	0.021	0.447	2.236	1.504	29.176	0.006	0.052	0.447	2.237
Interest Rate	−2.489	12.240	−0.031	−0.203	0.360	3.265	−2.469	12.124	−0.031	−0.199	0.360	3.266
Economic Growth Rate	25.920	21.860	0.170	1.186	0.239	3.850	25.910	21.692	0.172	1.192	0.237	3.850
ROA	−0.022	0.012	−0.158	−1.779	0.907	1.102	−0.022	0.012	−0.159	−1.797	0.907	1.102
Total Assets	0.000	0.000	0.587	6.647	0.920	1.088	0.000	0.000	0.591	6.726	0.919	1.087
RSUs Adoption	-	-	-	-	-	-	0.445	0.292	0.129	1.526	0.996	1.004
	R2 = 0.369, Adjusted R2 = 0.333 F = 10.290, p < 0.001						R2 = 0.385, Adjusted R2 = 0.343 ⊿R2 = 0.016, ⊿Adjusted R2 = 0.010 F = 9.093, p < 0.001
Durbin– Watson	-						2.382

and

Table 18. H4.4. result for Group 4 (operating profit effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	7.247	1.044	-	6.939	-	-	5.417	1.061	-	5.106	-	-
Inflation	−0.908	29.840	−0.004	−0.030	0.446	2.241	3.325	27.517	0.015	0.121	0.446	2.244
Interest Rate	−6.303	12.609	−0.079	−0.500	0.304	3.291	−7.031	12.700	−0.097	−0.553	0.304	3.292
Economic Growth Rate	31.525	22.464	0.206	1.403	0.164	3.852	32.071	20.701	0.204	1.376	0.162	3.857
ROA	−0.024	0.013	−0.177	−1.927	0.906	1.103	−0.024	0.013	−0.176	−1.924	0.906	1.103
Total Assets	0.000	0.000	0.548	6.016	0.918	1.089	0.000	0.000	0.564	6.004	0.918	1.087
RSUs Adoption	-	-	-	-	-	-	1.139	0.278	0.330	4.100	0.996	1.004
	R2 = 0.323, Adjusted R2 = 0.285 F = 8.506, p < 0.001						R2 = 0.432, Adjusted R2 = 0.393 ⊿R2 = 0.109, ⊿Adjusted R2 = 0.108 F = 11.150, p < 0.001
Durbin– Watson	-						2.302

.

To assess the reliability of the regression analysis (H4.1.–H4.4.), standard diagnostic procedures were conducted to identify any multicollinearity or residual autocorrelation. All independent variables had tolerance values exceeding 0.1 and VIFs well under the conventional limit of 10, indicating no multicollinearity issues. Additionally, the Durbin–Watson statistic was calculated to be between 1.9 and 2.4, suggesting that residuals were independently distributed.

For H4.1., Model 1 included fundamental financial variables believed to affect EPS. The adjusted R² was 0.040 (F = 1.645, p = 0.144), indicating that these predictors collectively explained 4.0% of the variance in EPS. However, the model was not statistically significant, and none of the included variables showed a meaningful relationship with EPS.

In Model 2, the RSU adoption variable was added to evaluate its additional explanatory contribution. The adjusted R² slightly declined to 0.038 (F = 1.523, p = 0.170), reflecting a decrease of 0.2%. The RSU variable itself was not significant (t = 0.900, p > 0.1), suggesting that it did not influence EPS in the short term.

H4.2. explored the long-term relationship between RSUs and EPS by comparing data from five years before to between six and ten years after adoption. As shown in Table 18, Model 1 reported an adjusted R² of 0.506 (F = 17.058, p < 0.001). Economic growth (t = −2.485, p < 0.05), total assets (t = −3.604, p < 0.001), and operating profit (t = 3.888, p < 0.001) were significant contributors.

Model 2 added the RSU variable, which increased the adjusted R² to 0.552—a 4.6% gain. The model retained statistical significance (F = 17.536, p < 0.001), and RSUs showed a significant positive association with EPS (t = 3.158, p < 0.01), indicating that RSUs contribute to higher EPS in the long run.

Looking at the adjusted R² of H4.1. and H4.2., the increase in the explanatory power of H4.2. (4.6%) with RSUs as an independent variable is greater than the change in H4.1. (−0.2%). In particular, in H4.1., the adjusted R² decreased after RSU adoption, resulting in the model having a lower explanatory power. In addition, when comparing the significance of RSU adoption in H4.1., the regression coefficient was t = 0.900, p > 0.1, indicating that it did not have a statistically significant effect.

On the other hand, in H4.2., the explanatory power increased after the introduction of RSUs, which can better explain EPS fluctuations than H4.1. In addition, the variable for the introduction of RSUs in H4.2. showed a significant regression coefficient of t = 3.158, p < 0.01. This suggests that the introduction of RSUs may have limitations in explaining EPS fluctuations in the short term, but the effect may gradually increase over time.

To examine the short-term effect of RSUs on operating profit (H4.3.), we performed hierarchical regression using data from five years before and after RSU adoption. Model 1 had an adjusted R² of 0.333 (F = 10.290, p < 0.001), suggesting that financial variables explained about 33.3% of the operating profit variability. After adding RSUs in Model 2, the adjusted R² slightly rose to 0.343 (F = 9.093, p < 0.001), but the RSU variable was not statistically significant (t = 1.526, p > 0.1).

For H4.4., we investigated the long-term operating profit effects by comparing pre-adoption data with those from six to ten years post-adoption. Model 1 showed an adjusted R² of 0.285 (F = 8.506, p < 0.001), and economic growth (t = 2.063, p < 0.05) and total assets (t = 6.616, p < 0.001) were significant. Model 2, with RSUs included, improved the adjusted R² to 0.393—an increase of 10.8%. The RSU variable had a highly significant coefficient (t = 4.100, p < 0.001), pointing to a strong long-term link with operating performance.

When comparing the adjusted R² of H4.3. and H4.4., the increase in the explanatory power of H4.4. (10.8%) with RSUs as an independent variable was much greater than that of H4.3. (1.0%). The significance of RSU adoption was analyzed as having no statistically significant impact on H4.3., as the regression coefficient of RSU adoption was t = 1.526, p > 0.1. In contrast, in H4.4. the regression coefficient of the adoption of RSUs was t = 4.100, p < 0.001, showing strong significance. This suggests that the introduction of RSUs may have a limited impact on operating profit in the short term, but that it has a very significant impact on the company’s operating profit in the long term.

The EPS analysis of Group 4 showed that the adoption of RSUs had no significant impact in the short term, and that the introduction of RSUs had little impact on the variable. Operating profit also showed that the explanatory power of the introduction of RSUs was very small at 1% in the short term, and that the introduction of RSUs was found to be insignificant. However, the impact of RSUs on EPS and operating income has been confirmed to have a long-term effect. In particular, the results show that operating income increases by more than 10% in the long run.

4.5. Results of Hypotheses H5.1.–H5.4. Regarding Group 5

To examine the short-term and long-term effects of the adoption of RSUs in Group 5 on EPS and operating profit, four statistical analyses were conducted on the same sample of companies (N = 72) from H5.1. to H5.4. The complete results are provided in

Table 19. H5.1. result for Group 5 (EPS effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	5.359	0.976	-	5.490	-	-	4.503	1.041	-	4.327	-	-
Inflation	−84.600	26.503	−0.578	−3.192	0.373	2.678	−81.979	25.870	−0.560	−3.169	0.373	2.684
Interest Rate	24.290	10.964	0.484	2.215	0.257	3.894	23.720	10.701	0.472	2.218	0.257	3.904
Economic Growth Rate	−64.916	20.524	−0.693	−3.163	0.329	3.920	−62.788	20.032	−0.678	−3.137	0.326	3.923
ROA	0.007	0.004	0.174	1.478	0.879	1.138	0.008	0.004	0.179	1.563	0.879	1.137
Total Assets	0.000	0.000	−0.120	−1.237	0.388	2.580	0.000	0.000	−0.056	−0.317	0.375	2.666
Operating Profits	0.000	0.000	0.444	2.520	0.308	2.678	0.000	0.000	0.361	2.043	0.343	2.678
RSUs Adoption	-	-	-	-	-	-	0.496	0.244	0.227	2.031	0.933	1.072
	R2 = 0.266, Adjusted R2 = 0.192 F = 3.622, p < 0.01						R2 = 0.314, Adjusted R2 = 0.232 ⊿R2 = 0.048, ⊿Adjusted R2 = 0.040 F = 3.856, p < 0.01
Durbin– Watson	-						2.206

,

Table 20. H5.2. result for Group 5 (EPS effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	5.699	1.000	-	5.696	-	-	4.052	1.058	-	3.830	-	-
Inflation	−84.289	27.428	−0.534	−3.073	0.381	2.622	−75.382	25.654	−0.478	−2.938	0.377	2.652
Interest Rate	25.096	11.893	0.449	2.110	0.255	3.924	22.633	11.087	0.405	2.041	0.254	3.942
Economic Growth Rate	−70.411	22.014	−0.669	−3.198	0.263	3.797	−63.105	20.595	−0.600	−3.064	0.260	3.842
ROA	0.004	0.005	0.082	0.720	0.883	1.132	0.005	0.005	0.106	0.969	0.879	1.138
Total Assets	0.000	0.000	−0.230	−1.471	0.437	2.124	0.000	0.000	−0.095	−0.593	0.436	2.294
Operating Profits	0.000	0.000	0.552	3.602	0.490	2.041	0.000	0.000	0.376	2.465	0.313	2.333
RSUs Adoption	-	-	-	-	-	-	0.855	0.262	0.352	3.268	0.861	1.162
	R2 = 0.285, Adjusted R2 = 0.216 F = 4.128, p < 0.001						R2 = 0.392, Adjusted R2 = 0.322 ⊿R2 = 0.107, ⊿Adjusted R2 = 0.106 F = 5.617, p < 0.001
Durbin– Watson	-						2.064

,

Table 21. H5.3. result for Group 5 (operating effect of short term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	7.063	0.927	-	7.619	-	-	6.225	0.948	-	6.569	-	-
Inflation	9.652	25.203	0.053	0.383	0.390	2.564	8.625	24.133	0.047	0.357	0.390	2.565
Interest Rate	−7.524	10.676	−0.121	−0.705	0.254	3.930	−6.113	10.652	−0.107	−0.575	0.254	3.932
Economic Growth Rate	15.957	19.763	0.136	0.807	0.423	3.822	14.830	18.937	0.124	0.783	0.420	3.820
ROA	0.003	0.004	0.056	0.606	0.880	1.137	0.004	0.004	0.082	0.924	0.879	1.153
Total Assets	0.000	0.000	0.749	8.448	0.948	1.057	0.000	0.000	0.752	8.528	0.947	1.060
RSUs Adoption	-	-	-	-	-	-	0.577	0.229	0.210	2.523	0.984	1.016
	R2 = 0.561, Adjusted R2 = 0.524 F = 15.067, p < 0.001						R2 = 0.604, Adjusted R2 = 0.563 ⊿R2 = 0.043, ⊿Adjusted R2 = 0.039 F = 14.757, p < 0.001
Durbin– Watson	-						2.376

and

Table 22. H5.4. result for Group 5 (operating effect of long term).

Independent Variable	Model 1						Model 2
	B	S.E	β	t	Collinearity Statistics		B	S.E	β	t	Collinearity Statistics
					TOL	VIF					TOL	VIF
Intercept	7.064	1.023	-	6.908	-	-	5.728	0.998	-	5.743	-	-
Inflation	15.509	27.829	0.085	0.557	0.379	2.639	15.219	25.300	0.084	0.602	0.379	2.639
Interest Rate	−11.794	11.879	−0.186	−0.993	0.251	3.978	−10.597	10.901	−0.170	−0.972	0.250	3.976
Economic Growth Rate	23.657	21.991	0.178	1.076	0.261	3.827	21.258	20.158	0.174	1.063	0.259	3.827
ROA	−0.002	0.005	−0.036	−0.374	0.880	1.136	0.003	0.005	0.056	0.628	0.879	1.151
Total Assets	0.000	0.000	0.698	7.208	0.944	1.059	0.000	0.000	0.707	7.082	0.943	1.060
RSUs Adoption	-	-	-	-	-	-	0.885	0.240	0.318	3.687	0.983	1.017
	R2 = 0.468, Adjusted R2 = 0.424 F = 10.559, p < 0.001						R2 = 0.568, Adjusted R2 = 0.524 ⊿R2 = 0.100, ⊿Adjusted R2 = 0.100 F = 12.911, p < 0.001
Durbin– Watson	-						2.418

.

To verify the reliability of the regression analysis results (H5.1.–H.5.4.), diagnostic tests were carried out to detect potential multicollinearity and autocorrelation among the independent variables. All predictors had tolerance values above 0.1 and VIF scores below 10, confirming the absence of multicollinearity. Furthermore, the Durbin–Watson statistic was between 2.0 and 2.5, indicating that residuals were not serially correlated.

For H5.1., Model 1 evaluated the impact of key financial and macroeconomic indicators on EPS. The adjusted R² was 0.192 (F = 3.622, p < 0.01), suggesting that approximately 19.2% of the variance in EPS was explained by the model. Statistically significant variables included inflation (t = −3.192, p < 0.01), the interest rate (t = 2.215, p < 0.05), economic growth (t = −3.163, p < 0.01), and operating profit (t = 2.520, p < 0.05). Notably, inflation and economic growth had negative associations with EPS, while interest rate and operating profit were positively correlated.

In Model 2, RSU adoption was introduced as an additional predictor. The inclusion of this variable improved the adjusted R² to 0.232 (F = 3.856, p < 0.01), representing a 4.0% increase in explanatory power. The RSU variable was statistically significant (t = 2.031, p < 0.05), indicating that RSUs contributed positively to EPS in the short term.

To assess H5.2., which explores the long-term effects of RSUs on EPS, the regression compared five years of EPS prior to adoption with EPS between six to ten years post-adoption.

Model 1 yielded an adjusted R² of 0.216 (F = 4.128, p < 0.001), with inflation (t = −3.073, p < 0.01), interest rate (t = 2.110, p < 0.05), economic growth (t = −3.198, p < 0.01), and operating profit (t = 3.602, p < 0.01) emerging as significant predictors. The addition of the RSU variable in Model 2 increased the adjusted R² to 0.322 (F = 5.617, p < 0.001), indicating a 10.6% improvement. The RSU variable also showed a strong positive effect (t = 3.268, p < 0.01), suggesting a more prominent long-term impact on EPS.

When comparing the adjusted R² of H5.1. and H5.2., it can be seen that the increase in the explanatory power of H5.2. (10.6%) with RSUs as an independent variable is much greater than that of H5.1. (4.0%). These results suggest that RSU adoption can explain EPS fluctuations more strongly in the long run and that the effect is likely to become more pronounced over time.

Moreover, comparing the significance of the RSU adoption variable in the two analyses, the regression coefficient of the RSU adoption variable was t = 2.031, p < 0.05 in H5.1., but the regression coefficient was t = 3.268, p < 0.01 in H5.2. This means that the impact of the adoption of RSUs in H5.2. on EPS is statistically more significant. Consequently, the adoption of RSUs in Group 5 has a significant impact on EPS in the short term, but the effect tends to be stronger in the long term.

In order to test H5.3., the hypothesis that the introduction of RSUs in Group 5 would have a short-term impact on operating profit, we conducted a hierarchical regression analysis of the impact of five years of operating profit without the introduction of RSUs and five years of operating profit for the first five years after introduction.

For H5.3., which examined the short-term effects of RSUs on operating profit, a similar regression approach was adopted. Model 1, which included financial and macroeconomic factors, produced an adjusted R² of 0.524 (F = 15.067, p < 0.001). Among the variables, total assets had a significant and strong positive effect (t = 8.448, p < 0.001). Upon introducing RSUs in Model 2, the adjusted R² rose to 0.563 (F = 14.757, p < 0.001), reflecting a 3.9% increase in explanatory power. The RSU variable was statistically significant (t = 2.523, p < 0.05), implying a positive contribution to the short-term operating performance.

For H5.4, Model 1 achieved an adjusted R² of 0.424 (F = 10.559, p < 0.001), with total assets again showing a significant effect (t = 7.208, p < 0.001). When RSUs were added in Model 2, the adjusted R² increased to 0.524 (F = 12.911, p < 0.001), reflecting a 10.0% gain in explanatory power. The RSU coefficient was also highly significant (t = 3.687, p < 0.001), reinforcing their long-term impact on operating profit.

Additionally, when comparing the significance of the RSU adoption variable in the two statistical results, the regression coefficient of the RSU adoption variable was t = 2.523, p < 0.05 in H5.3., but the regression coefficient was t = 3.687, p < 0.001 in H5.4. This means that the impact of the introduction of RSUs on operating profit is stronger and statistically significant in H5.4. RSU introduction in Group 5 also had a significant impact on operating profit in the short term, but the effect tended to be stronger in the long term.

When the analysis of Group 5 is combined, the impact of RSU adoption on EPS and operating income shows a similar pattern, and the effect tends to be more pronounced in the long term. This suggests that the adoption of RSUs acts as an important explanatory variable for corporate EPS and operating profit, and that the impact is likely to be strengthened in the long term.

Also, when comparing the significance of the variables of RSUs, both analyses showed that the impact of RSUs was relatively weak in the short term, but the effect tended to be stronger in the long term. This suggests that RSU adoption gradually has a positive impact on corporate performance over time, and shows that companies need to approach the effects of introducing RSUs from a long-term perspective rather than a short-term perspective.

5. Discussion

This study analyzes the short-term and long-term effects of introducing RSUs on corporate performance (EPS and operating profit) by industry. The results show that the introduction of RSUs has a stronger long-term effect, and that the magnitude and significance of the impact vary by industry. In addition to EPS, operating income was used as an additional dependent variable to more comprehensively analyze the impact of RSUs on corporate profitability. This is different from previous studies that mainly evaluated the effects of RSUs based on qualitative research (Yoon, 2023; S. Lee, 2024).

The results of the study are more clearly shown in

Table 23. Summary of results regarding the impact of RSU adoption on EPS and operating profit.

Category	Indicator	Period	⊿Adjusted R2	p-Value (RSU Introduction)
Group 1	EPS	Short-Term	0.040	p < 0.05
		Long-Term	0.119	p < 0.001
	Operating Profit	Short-Term	0.023	p < 0.1
		Long-Term	0.093	p < 0.001
Group 2	EPS	Short-Term	0.023	p < 0.1
		Long-Term	0.069	p < 0.01
	Operating Profit	Short-Term	0.070	p > 0.1
		Long-Term	0.064	p < 0.01
Group 3	EPS	Short-Term	−0.003	p > 0.1
		Long-Term	0.021	p < 0.1
	Operating Profit	Short-Term	0.021	p < 0.05
		Long-Term	0.104	p < 0.001
Group 4	EPS	Short-Term	−0.020	p > 0.1
		Long-Term	0.046	p < 0.01
	Operating Profit	Short-Term	−0.010	p > 0.1
		Long-Term	0.108	p < 0.001
Group 5	EPS	Short-Term	0.040	p < 0.05
		Long-Term	0.106	p < 0.01
	Operating Profit	Short-Term	0.039	p < 0.05
		Long-Term	0.100	p < 0.001

, which summarizes the explanatory power by industry and the significance of the variables introduced by RSUs.

When we analyzed the effect of RSUs on EPS and operating profit by industry, we found that the long-term EPS growth was relatively higher in the consumer-related industry (Group 1) and the technology and telecommunications-related industry (Group 4). In contrast, we found that RSUs had a stronger impact on operating income in the health-related industry (Group 5).

However, the impact of RSUs was relatively limited or showed no significant results in the resources and energy industry (Group 2) and the industrial and infrastructure industry (Group 3). This is in line with existing research suggesting that the business model and labor market characteristics of each industry are important factors in determining the effectiveness of RSUs (Ahn & Kapinos, 2014).

Most importantly, unlike stock options, which cause short-term volatility in share prices, RSUs can induce long-term value creation, and the effect of improving corporate performance is likely to gradually appear over time (Yang, 2024). In each industry, the introduction of RSUs showed mixed results in the short term, with some cases being statistically significant and others not, but in the long term, all industries showed statistically significant results.

In industries such as consumer goods and technology, where the value of human capital is high, the introduction of RSUs is likely to have an impact on short-term performance. On the other hand, in industries such as energy and infrastructure, the introduction of RSUs is unlikely to have a significant impact in the short term, but can be expected to gradually improve performance in the long term.

These results can complement previous studies that have not sufficiently verified the long-term performance improvement effects of RSUs (Murphy & Vance, 2019). Moreover it is consistent with the research results (Lu, 2024) that the introduction of RSUs has a positive impact on companies’ sustainable growth and talent retention, and can emphasize the need for compensation design that takes long-term performance into account.

In the technology and consumer industries, the impact on EPS is relatively large and appears quickly, while in the resources and infrastructure industries, the effect of increasing the operating income is more pronounced. Therefore, companies need to design RSUs by considering industry characteristics, and in certain industries, a long-term RSU operation strategy aimed at increasing operating income rather than improving EPS may be required.

In addition, there may be a short-term cost burden immediately after the introduction of RSUs, which is more pronounced in the technology and infrastructure industries. On the other hand, over time, the long-term compensation effects of RSUs are reflected in corporate performance, and the effects of an increased operating income in particular are more pronounced in the long term in most industries. When companies consider the introduction of RSUs, they should focus on the long-term effects of human capital rather than short-term company performance.

In particular, this research found that financial performance becomes more pronounced six to ten years after RSU implementation. This further reinforces the view that RSUs contribute to long-term financial performance. This suggests that RSUs are an important meaning of compensation that strengthens a company’s sustainability in the long term, even though they may cause short-term cost burdens.

Moreover, this study more accurately evaluated the effects of RSUs by considering macroeconomic factors. Previous studies often applied the macroeconomic environment to the effects of RSUs in a limited way, which could have underestimated or overestimated the effects of RSUs (Jia, 2023; S. Lee, 2024).

This study conducted a more sophisticated analysis by controlling external variables. This reflects the need to consider macroeconomic variables when analyzing corporate performance.

In addition to financial performance, this study emphasizes the strategic role of RSUs in long-term corporate strategy. RSUs act as a tool to strengthen corporate strategy by linking the compensation of executive and employees to a long-term perspective (Aras & Crowther, 2010).

This suggests that RSUs play a role in simultaneously increasing corporate performance and shareholder value. In addition, RSUs can have a positive impact on the retention and motivation of human resources, which is an important factor in helping companies gain a competitive edge.

The results highlight that RSUs are not just a tool for improving financial performance, but also play an important role in corporate sustainability.

6. Conclusions and Implications for Future Research

This research empirically confirms that RSUs can act as a strategic tool to improve corporate performance and shareholder value. While previous studies have focused on assessing the overall impact of RSUs, this study has identified that RSUs can act differently depending on industry characteristics by analyzing industry-specific differential effects. This provides empirical evidence that enables companies to use RSUs more precisely.

This research’s major insights are as follows. First, the long-term effect of RSUs was empirically verified. This study analyzed the long-term effects of RSUs over a period of 10 years or more using data from 1997 to 2023. This study confirmed that the positive impact of RSUs on corporate performance strengthens over time. This means that RSUs may cause costs in the short term, but in the long term, they can increase corporate financial stability and promote sustainable growth.

Second, we identified the differential effects of RSUs by industry. Despite the fact that the effects of RSUs are likely to differ depending on the industry characteristics of the company, previous studies have not sufficiently taken this into account. This study classified companies into five industry groups based on the GICS to analyze the differential effects of RSUs, thereby providing empirical evidence to help companies design customized RSUs that reflect their industry characteristics. In particular, the effects of RSUs being stronger in the technology and telecommunications industries and in capital-intensive industries is consistent with previous studies (Yoon, 2023; Lu, 2024).

Third, we compared and analyzed the change patterns of EPS and operating profit. After the introduction of RSUs, EPS and operating income showed a negligible impact in the short term, but both indicators showed a significant increase in the long term. This suggests that RSUs can contribute not only to a company’s short-term financial performance but also to its long-term profitability and sustainability. Previous studies have also pointed out that RSUs have a high potential to reduce short-term stock price volatility and improve financial stability and corporate value in the long run (Bhagat et al., 2014).

The limitations of this study and future research directions are as follows. First, this study analyzed the effects of RSUs on the S&P 500 companies in the United States, so the effects of RSUs in other countries or small and medium-sized enterprises may differ. Previous studies have also pointed out that the effects of stock based compensation may vary depending on the economic environment and regulatory differences by country (Yang, 2024). Therefore, future studies need to compare and analyze the effects of RSUs on companies in Europe, Asia, and emerging markets to derive optimal ways to utilize RSUs on a global scale.

Second, the impact of RSUs on company culture and human resource management within organizations has not been sufficiently addressed. RSUs can play an important role in the long-term growth of companies and employee retention and motivation (Palmer, 2024), and future research should empirically analyze the impact of RSUs on employee motivation and retention rates to more specifically examine the non-financial effects of RSUs.

Third, this study used reliable financial data for S&P 500 companies, but considering Green’s (1991) sampling theory, the parameter size was relatively small for some industries (especially healthcare, Group 5). Future studies should expand the sample size to analyze the industry-specific RSU effect more comprehensively.

Ultimately, this study has empirically demonstrated that RSUs can be used not only as a simple stock compensation tool, but also as a strategic tool to ensure long-term corporate value creation and sustainability for company executives, investors, and policymakers.

Future studies should explore the optimal design of RSUs in various industries and regulatory environments to maximize the effects of RSUs in changing economic conditions. In particular, further research is needed to analyze the impact of RSUs in terms of the differences in the effects of RSUs by company size and non-financial performance (e.g., organizational culture and employee motivation).

An area that requires further study is the fact that while RSUs can lead to positive results from the company’s perspective, the economic benefits to employees must also be considered. Generally, employees can expect a higher income through job changes, so careful consideration is needed to determine whether the income increase generated by RSUs exceeds the income increase that can be obtained through job changes. If the compensation level provided by RSUs does not exceed the potential income increase that could be obtained through job switching, RSUs may instead impose economic costs on employees in the form of income reduction and constraints on career growth opportunities. Therefore, future research should conduct a more in-depth analysis of the impact of RSUs on employees’ long-term economic benefits and career development, evaluate the actual incentive effects on a post-tax basis, and comprehensively review the effectiveness of the RSU system from the perspectives of both the company and employees.

Finally, we would like to supplement this study by addressing a potential counterargument suggesting that the introduction of RSUs may affect corporate performance and dividend policies from the perspective of existing financial theory.

This study aims to provide a complementary discussion on the counterarguments that may arise from existing financial theory regarding the impact of RSUs on corporate performance and dividend policy.

First of all, according to Modigliani and Miller (1958)’s capital structure irrelevance theory and Fama (1970)’s Efficient Market Hypothesis (EMH), any changes in a company’s capital structure or compensation system should already be reflected in market prices, and such changes are not expected to have a substantial impact on company value. From this perspective, the introduction of RSUs should also be immediately reflected in stock prices, and the positive changes in EPS or operating profit observed in this study after the introduction of RSUs may require additional theoretical explanation.

However, in reality, RSUs have different conditional payment characteristics from traditional stock options and exhibit a relatively non-standard structure in both institutional design and accounting treatment (S. Lee, 2024; Hwangbo & Yang, 2023). In particular, the process of introducing RSUs involves complex procedures such as board resolutions, disclosures, and changes in accounting standards, which may make it difficult for the market to immediately and fully reflect this information. It is also necessary to consider that the timing and intensity of RSU introduction vary across industries and companies.

Such delays in market reactions can be partially explained from the perspective of behavioral finance theory (Hirshleifer, 2015). Market participants are influenced by cognitive biases and information asymmetries, and may tend to interpret and reflect information about complex or unfamiliar systems such as RSUs in a gradual manner. In this context, RSUs may act as quasi-surprises or delayed signals, which can be interpreted within the framework of the weak-form or semi-strong-form efficient market hypothesis.

Therefore, the empirical results of this study, which show that the effects of RSU introduction are not immediately reflected in the market, can be understood as a realistic phenomenon when considering the uncertainty surrounding institutional changes and the market’s gradual learning effects. Accordingly, there may be certain limitations in applying the ideal assumptions of MM theory and EMH directly to the real business environment, and it would be desirable to consider interpretations that take into account the time lag effects and market inefficiencies inherent in structural changes such as RSUs.

Furthermore, the introduction of RSUs may also provide meaningful implications in relation to dividend policy. The traditional Dividend Irrelevance Proposition (Modigliani & Miller, 1961) assumes that under perfect capital markets, a company’s dividend policy does not affect company value. That is, investors are indifferent between dividends and capital gains, and the dividend policy does not affect the cost of capital.

However, real markets do not meet the perfect market assumption, and dividend policies can be influenced by factors such as managerial signaling, agency costs, and liquidity constraints (Lintner, 1956; Easterbrook, 1984). In particular, the spread of long-term performance-based compensation systems such as RSUs has the potential to bring about structural changes in dividend policies USA Financial Accounting Standards Board stated in 2004 that RSUs are characterized by the fact that compensation is granted upon meeting certain tenure and performance requirements and is recognized as an accounting expense at the grant date. This structure may incentivize management to prefer long-term corporate value enhancement and increased retained earnings over short-term dividend expansion.

According to Brav et al. (2005), the higher the proportion of long-term incentive compensation, the more cautious and conservative management tends to be in dividend policy; in particular, the higher the holding of RSUs, the lower the dividend payout ratio tends to be. This suggests that RSUs can have a substantial influence on management’s financial decision-making.