Board Structure and Executive Compensation for R&D Spending in Innovative Companies Amid COVID-19

Abstract

Innovation has played a vital role in continuing business operations worldwide amid the challenges of the COVID-19 pandemic. Innovation is critical for the success and survival of global organizations. Due to the risky long-term nature of innovation, executives with decision-making power may act cynically. Such pessimistic actions become normal when executive compensation is based on the firm’s short-term outcomes. Therefore, the current research examines the effect of executive compensation on research and development (R&D) investment using data from the world’s top 48 innovative companies in Australia. The proposed model was tested using Smart-PLS (v.3.2.8). The findings indicate that board composition significantly and positively affects R&D investment. Likewise, the long-term composition of executives has a positive effect, whereas short-term executive compensation has a negative effect on R&D. Hence, this research suggests that to increase innovation, firms should control the myopic actions of top management by orientating their compensation toward long-term innovation.

1. Introduction

Innovation is crucial for the success and survival of organizations. Several organizations were once leading their sectors; however, they have since been acquired or outperformed by competitors such as Nokia, Yahoo, Motorola, and Kodak. These organizations were unable to meet the requirements of the digital economy, and their top management was reluctant to invest in risky innovative projects. Although these organizations did not do anything wrong, they did not show the flexibility to adopt an innovation. These organizations in business history highlight the importance of research and development (R&D) and innovation. During the pandemic, innovation became increasingly crucial for survival. When the government imposed a lockdown because of COVID-19, businesses began to search for other ways to conduct business. A new era of innovation began at that point. The debatable point, though, is whether the business was prepared for all of this or not.

Researchers estimate that the failure rate of innovative projects completely or partially is between 40% and 90% (Rhaiem & Amara, 2021). Because of the financial restrictions imposed by COVID-19, firms with limited financial flexibility have become more reluctant to invest in risky projects (Bedford et al., 2023). Moreover, R&D has been significantly influenced by the global financial crises. Bankruptcies in the business world have increased, and the formation of new businesses has almost stopped. Furthermore, uncertainty regarding the declining future trend of most developed countries regarding innovation has increased. More specifically, when persistent unskilled employment increases, it causes dwindling public support for innovation. This phenomenon has caused long-term damage to R&D activities.

Apart from financial constraints, innovation is directly controlled by the board of directors and any change in their compensation contracts will have a significant effect on R&D investments. R&D investments are inherently exploratory and fraught with uncertainties. Therefore, managers’ ability to take risks is essential if company owners want to encourage executives to invest in R&D. Researchers have suggested that effective incentive systems are the most effective means of promoting R&D (Xu et al., 2024). Agency theorists recommend that executive compensation can be utilized as an important tool to adjust administration concerns to those of shareholders and that executive pay is an answer to the agency expenses emerging from the partition of proprietorship and administration (Jensen & Meckling, 1976). They further add that the separation of possession and control creates potential clashes between the concern of expert administrators and stockholders, which may prompt firms’ top management to artfully put resources into R&D by firms’ top management (Shang et al., 2023; Semadeni & Krause, 2019; Carberry & Zajac, 2017). As discussed, innovation is a risky investment; therefore, executives tend to hesitate while investing in risky projects (Blibech & Berraies, 2018). Additionally, the occurrence of notable agency issues, in addition to financial failures, brings up key issues about the authenticity of existing governance game plans to encourage good corporate governance concerning executive compensation (Sheikh et al., 2018).

However, whether the propositions of agency theory remain the same during the current pandemic or create more severe effects as organizations change the compensation arrangements aimed at COVID-19. In 2020, nearly 50% of S&P 500 companies implemented various COVID-19-related measures, such as reducing base salaries, and adjusting special awards and incentive plan targets. Therefore, the current study investigates the effect of corporate board and executive compensation on R&D spending during COVID-19. The current research is unique in that it deals with short- and long-term compensation separately. These two elements are distinct in nature and affect R&D investment decisions differently. Hence, because of their distinct nature, both short-term and long-term compensation need to be handled separately. It is important to treat both types of compensation separately to obtain profound results on how short- and long-term compensation affects R&D. Additionally, corporate board characteristics also influence firms’ R&D spending, but the effect of the composite board construct is not widely tested in the existing literature. Thus, this study fills this gap by examining the relationships among corporate boards, executive compensation, and R&D spending.

2. Literature Review

According to agency theory, managers are self-interested and focus on actions that increase their benefits (Jensen & Meckling, 1976). Therefore, executives and managers generally adopt myopic behaviors towards long-term projects with uncertain payoffs. The epidemic increased this short-termism as managers concentrated on urgent survival strategies to survive the catastrophe (Eklund & Stern, 2021). On the other hand, owners always try to make organizations profitable in the long run; hence, they have a greater tendency to engage in risky value creative projects that will make firms more competitive even in uncertainties (Nguyen et al., 2018; Wuebker & Klein, 2017; Shen & Zhang, 2013). During the pandemic, CEOs were more concerned with their employment and short-term results than long-term investments, resulting in increased conflicts of interest. Meanwhile, numerous companies have proposed salary cuts as part of the “share burden” phenomenon (Bedford et al., 2023). Therefore, differences in the interests of both parties may result in potential damage to firms over a long period. Accordingly, agency theory proposes that owners must rely on certain incentives in compensation contracts to align managers’ interests with firm value. Moreover, long-term incentives play a critical role in binding managers’ interests with firms’ value-creation activities. Examining agency theory in the context of COVID-19 demonstrates the need for adaptable incentive structures to navigate new obstacles and to ensure congruence between management actions and long-term business value.

The fundamentals of innovation are value creation activities, through which firms can transform new ideas, knowledge, and opportunities into their strength to compete with their rivals (Akram et al., 2017). Scholars argue that in a digital business environment, R&D activities prepare a firm for environmental changes and boost its competitive advantage, which ultimately influences its performance (Akram et al., 2017; Shen & Zhang, 2013). Shen and Zhang (2013) and S. S. Chen et al. (2014) artfully describe the uniqueness of R&D as they say that, although the cost of R&D is tangible, the benefits of it are not directly observable. The COVID-19 pandemic highlighted the crucial need for innovation, as businesses were forced to respond quickly to unprecedented problems. During the pandemic, companies with strong R&D skills were better positioned to pivot and adapt to changing market needs and operational restrictions (Xu et al., 2024).

Additionally, R&D investment is expensed immediately; therefore, its impact on short-term profitability is unconstructive (Shang et al., 2023). However, R&D projects generate profits after a few years; therefore, they have a high failure rate (Shujuan et al., 2018; Dalziel et al., 2011). Hence, due to a longer time horizon and uncertainty about results, executives may hesitate to put resources into strategically oriented R&D projects (Xu et al., 2024). By contrast, shareholders may find R&D more attractive as they produce a higher return for a long period (Semadeni & Krause, 2019; Shaikh et al., 2018; Cheng, 2004). Furthermore, due to the risky and uncertain nature of R&D investment, future benefits are also uncertain (Hall & Lerner, 2010) as well, and forthcoming benefits from R&D are not completely mirrored by current stock prices (Lotfi & Rost, 2014). Therefore, executives who hold short-term stocks may ease R&D to augment investments with settlements to be fully pointed toward contemporary stock outlays (Shujuan et al., 2018; Banker et al., 2013). Despite its importance in R&D investment decisions, the effects of executive compensation have not been thoroughly investigated.

In addition, CEOs who are rewarded based on performance might concentrate on improving a company’s short-term performance by reducing investment in R&D (Shang et al., 2023; Cheng, 2004; Bushee, 1998). This phenomenon may direct firm managers to invest opportunistically in R&D activities (Shujuan et al., 2018). Other studies analyzed R&D through the perceptual lens of the CEO, as a person responsible for making central decisions and having the power to make decisions regarding the allocation of resources and crucial investments for an organization have a significant impact on corporate innovation and R&D (J. Kim & X. Wang, 2018; Barker & Mueller, 2002). Diversification is likely to be a form of innovation, and there is evidence that executives’ incentives affect firms’ diversification (H. C. Wang et al., 2019), as diversification executives lose full or partial control over firms’ finances/departments, and they also need to struggle to manage a diversified business (Nguyen et al., 2018). Hence, diversified businesses are another reason for conflicts of interest among shareholders and managers. However, if executives’ interests are attached to firms’ long-term growth, then the myopic behavior of executives towards R&D spending can be curtailed.

Similarly, researchers found that executive compensation is raised in high-growth firms because high-R&D-intensive firms demand unique and high human capital and pay according to their ability to understand new knowledge (Banker et al., 2013). They further added that the proportion of equity-based compensation is greater in R&D-intensive firms; M. C. Anderson et al. (2000) provide the same evidence for information technology firms. Furthermore, research conducted by Cheng (2004) analyzed whether pay contracts help mitigate managers’ opportunistic reductions in R&D investment and found that executive compensation with long-term incentives and less emphasis on performance-based pay plays a critical role in preventing myopic cuts in R&D by shielding CEO compensation from R&D.

R. C. Anderson and Bizjak (2003) found a negative relationship between total compensation, CEO cash, and R&D investment and diversification, while Cheng (2004) stated that there is no association between the value of employee stock option grants and R&D investment. Additionally, performance-based compensation and short-term incentives are likely to decrease R&D intensity (Biggerstaff et al., 2019; Banker et al., 2013); therefore, managers whose compensation is more closely associated with financial performance become risk-averse and hesitant when investing in long-term investments such as R&D (Shujuan et al., 2018; Sanyal & Bulan, 2010). Some researchers have stated that compensation aligned with performance raises executive performance (Raza et al., 2018; Manso, 2017; Bebchuk & Fried, 2005). In contrast, other scholars argue that incentives lead to high executive compensation and low performance (Banker et al., 2013).

Regardless of the constructive impact of performance pay, incentive schemes are still used by many organizations to motivate their executives (Lotfi & Rost, 2014). Furthermore, stock performance or turnover reflects a firm’s performance in the short run, and such measures are used to determine executives’ variable pay (Biggerstaff et al., 2019). Executives’ compensation emphasis on short-term incentives indicates that they do not have sufficient long-term incentives to invest in projects, such as R&D, with a longer time horizon (Manso, 2017; Cheng, 2004). As R&D investment is linked to long-term commitment and uncertainty regarding a firm’s resources, most prior studies believe that long-term payments, such as stock options, can foster R&D activities. Hence, these incentives foster top management’s long-term commitment to R&D (Bereskin & Hsu, 2014). Baranchuk et al. (2014) stated that to motivate executives and to reduce the myopic behavior of managers, there should be compensation with a longer time horizon, like early termination protection, option, and stock compensation.

Despite being widely studied, executive compensation’s influence on R&D spending remains unclear. The primary reason behind this is that existing studies have examined the effect of total compensation on R&D/innovation (Lu et al., 2020) or only considered one type of compensation. Therefore, the current study categorizes executive compensation into two major categories: short-term (performance-based compensation) and long-term incentives (equity-based compensation with a longer time horizon). This will help us understand the exact effects of long-term incentives and short-term pay on R&D.

Furthermore, some studies have examined the effect of corporate boards on R&D (AlHares, 2020; Manso, 2017); predominantly, board size and board independence have often been analyzed because these factors are considered important in aligning the interests of shareholders and managers to eliminate agency problems (Mezghanni, 2010). Independent outside directors are more willing to monitor the board’s activities and contribute their professional expertise in the strategic decision-making process; hence, they effectively protect shareholders’ wealth (Shaikh et al., 2018). However, independent directors potentially lack the specific knowledge and experience related to a firm that is necessary for the firm’s competencies (Banker et al., 2013).

Contrary to this, internal directors have high-quality superior information, and they participate actively in the ongoing operations of the company because they have a better understanding of the strategic decisions and their potential outcomes concerning the short or long run (Patel et al., 2018). Moreover, a corporate board is the central point for processing and managing information with diverse knowledge and experience. During the COVID-19 pandemic, the position of internal directors became even more important as businesses faced unprecedented obstacles and had to make quick, educated choices. The pandemic underscored the necessity for directors to have profound, practical insights into urgent operational changes and long-term strategy alterations. Evidence from the crisis demonstrates that organizations with internally knowledgeable directors are better placed to navigate changes and modify their strategies.

Additionally, according to resource dependence theory, board size is widely known as a bank of resources that may foster R&D (Patel et al., 2018). However, researchers have contradictions about board size and frequency of board meetings; some prefer a larger board size (Patel et al., 2018), while others support a smaller board size (AlHares, 2020). However, these researchers agree on one point, that board size and board meetings affect R&D. Proponents of larger board sizes have postulated that a larger board could bring more diverse resources to evaluate risky projects. Those that support smaller boards argue that a larger board may face problems with communication and delays in the decision-making process. Despite being widely studied, there is still no ideal size of the corporate board suggested by researchers; hence, it is an open question to further investigate.

The frequency of board meetings has been continuously discussed among academic and non-academic researchers. However, unfortunately, like board size, researchers are unable to agree on the maximum and minimum frequencies of board meetings (AlHares, 2020). On one hand, a high frequency of board meetings can be a solution to asymmetrical information and agency problems, which will result in more trust in directors among shareholders (AlHares et al., 2020). Moreover, directors are able to evaluate strategic opportunities more frequently and make timely decisions. On the other hand, as the number of meetings increases, the cost of organizing them also increases, as corporations need to arrange their travel, stay, refreshments, and other necessary arrangements for directors (AlHares et al., 2020). Hence, the costs of board meetings may surpass these benefits. Meanwhile, during the COVID-19 pandemic, all meetings were held online, saving corporations the cost of CEOs’ travel and associated expenditures.

In conclusion, the association between board structure and R&D spending is well documented in the traditional literature on corporate governance, although the results are still inconclusive. The emergence of COVID-19 has created new dynamics, prompting further investigation into how pandemic-induced changes in governance practices may affect this relationship. Understanding these changes is critical for adding to and broadening the current corpus.

3. Hypothesis Development

In this section, hypotheses are developed based on previous related arguments and theories.

3.1. Board Composition and R&D Spending

Boards act as the processing unit or mind of the organizations because their artificial identity organizations are unable to make decisions by themselves. Therefore, the attributes of top management and processing units have a strong influence on organizational R&D investments (Balsmeier et al., 2017). Additionally, the corporate board serves as a hub for intellectual knowledge needed for strategic decisions, such as R&D. The board of directors is one of the main components of the board. They are responsible for monitoring top management and aligning wealth with performance. According to resource-based theory, the corporate board possesses diverse knowledge, advice, and experience to help managers make strategic decisions (Epstein et al., 2017; H. Kim et al., 2008). This theory also states that a corporate board is a bank of resources that a firm needs in day-to-day operations, specifically for making crucial decisions related to innovative projects (J. Kim & X. Wang, 2018). A corporate board based on a greater proportion of independent directors may increase R&D spending through better monitoring of the top management. Moreover, independent directors are appointed to reduce the myopic behavior of management, as they are more efficient in observing board activities. Furthermore, improving a board’s independence directly enhances firm performance and increases the number of successful R&D investments (Vafeas & Vlittis, 2019). Independent directors are often busier than internal directors and may struggle to maintain their effectiveness (Moursli, 2020). In addition, board size and meetings are crucial for obtaining and processing information required for R&D decisions (H. L. Chen, 2013). A larger board is a great source of diverse intellectual capital (Balsmeier et al., 2017); however, board size is also inversely related to risk-taking, as measured by R&D intensity (AlHares et al., 2020). Therefore, the current study proposes the following hypothesis:

H1: 

The composition of a corporate board has a positive effect on firms’ R&D spending.

3.2. Executive Compensation and R&D Spending

Shareholders are more conscious of executives’ compensation schemes and plans because compensation is a motivational force behind directors’ actions and leads them towards the shareholders’ objective of value maximization (Shujuan et al., 2018; Liu, 2014; Lotfi & Rost, 2014). According to the equity theory of motivation, employees try to manage a situation by reducing their efforts if they feel that they are not fairly rewarded (Adams, 1965). Additionally, as innovation is risky and has more chances of failure, directors tend to be safer and prefer to emphasize alternative short-term performance-oriented practices (Hon & Lu, 2015). Hence, board members’ compensation determines firms’ investment decisions, more specifically when it comes to R&D spending. Numerous studies have investigated the compensation effect on R&D to build common interests between shareholders and managers.

However, these studies failed to provide any conclusion; some reflected a positive relationship between executive compensation and R&D investment (Shujuan et al., 2018; Barker & Mueller, 2002), whereas others reported a negative relationship (Souder & Shaver, 2010). Compensation is tightly linked to short-term financial performance, which hurts a firm’s commitment to R&D (J. Kim & X. Wang, 2018; Cheng, 2004). Similarly, stock-option-based incentives may also negatively influence R&D investment if there is a significant difference in the time horizon between equity compensation and R&D outcomes (Honoré et al., 2011).

By contrast, studies (Sanyal & Bulan, 2010; Ryan & Wiggins, 2002) report a positive association between executive compensation and R&D investment. Accordingly, the current study proposes the following hypotheses:

H2: 

Executives’ cash compensation negatively influences R&D spending of firms.

H3: 

Executives’ long-term incentives positively influence the R&D spending of firms.

4. Research Methodology

4.1. Data and Sample

This study used the Australian economy as a sample because it is one of the developed countries that has performed the best in controlling the pandemic compared to other developed countries. However, its market remains damaged, as in other wealthy countries. The current study takes a dataset consisting of the 52 most innovative manufacturing firms operating in Australia. These were drawn from the listing of Australia’s most innovative manufacturers during 2023, and I ensure that the final sample does not contain any firms in the health, pharmaceutical, wellness, cleaning, or medical device industries, which may directly or indirectly have improved due to pandemic-induced demand. The selection involved an industry report and an innovation ranking exercise to ascertain that the sampled firms were fully engaged in innovation during the financial years 2019–2023. This period covers three years during which the pandemic caused considerable disruption, offering a rich environment for exploring the interplay between aspects of board composition, executive compensation, and R&D. Hence, the final sample of this study comprises 52 manufacturing companies out of a total of 74 most innovative manufacturers, excluding the industries mentioned above. Australia’s “two-strike” regulation on compensation reports proves that the executive pay cut is not simply a pretense; it is being applied and affects executives. This study used data from the annual published reports of 52 companies for the financial years of 2019–2020, 2020–2021, 2021–2022, and 2022–2023, with 208 total observations.

4.2. Variable Measurement

The composition of the corporate board was measured using three indicators: board size, board independence, and board meeting frequency. Executive cash compensation is a combination of two indicators: short-term incentives and fixed salary. Short-term incentives are measured using the sum of all one-year bonuses divided by total compensation, and fixed salary is the ratio of the fixed cash amount of compensation to total compensation (Liu, 2014). Similarly, long-term compensation is measured using two indicators, namely, equity compensation and long-term incentives (LTIs), where equity compensation is the ratio of the dollar value of shares rewarded as part of compensation and LTIs include the ratio of long-term incentives and other long-term benefits to total compensation (Liu, 2014; Lotfi & Rost, 2014). The dependent variable of this study is R&D spending, measured using three proxies to cover a wider range of firms’ spending on R&D, as firms sometimes invest in R&D projects and sometimes use diversification processes for R&D purposes as shown in

Table 1. Measurement of variables.

Variables	Items	Measurement	Source
R&D	R&D Expense	Log of R&D expense	Lu et al. (2020)
	Diversification	Measured as a dummy, 1 for yes and 0 for no
	R&D Intensity	R&D/sales
Corporate Board	Board Size	Number of total directors	Liu (2014)
	Board Independence	% of independent directors
	Board Meeting	Number of board meetings held in a year
Cash Compensation	Short-Term Incentives	Sum of all one-year bonuses divided by total compensation	Liu (2014)
	Fixed Salary	Ratio of fixed cash amount of compensation to total compensation
Long-Term Compensation	Equity Compensation	Ratio of the dollar value of shares rewarded as part of compensation	Lotfi and Rost (2014)
	Long-Term Incentives (LTIs)	Ratio of long-term incentives and other long-term benefits to total compensation

. Hence, this study followed the existing literature (Lu et al., 2020) and applied three proxies of R&D spending: log of R&D expense, diversification (measured as a dummy, 1 for yes and 0 for no), and R&D intensity (R&D/sales), which are also used by Lotfi and Rost (2014).

This study purposefully uses a combination of formative (such as cash compensation and long-term compensation) and reflective (such as R&D and corporate board) measuring scales, in line with the theoretical nature of the constructs and the goals of this study. Indicators like board size, board independence, and meeting frequency are used in reflective constructs like corporate board since they represent a single latent variable and are highly correlated and interchangeable. On the other hand, formative constructs like cash compensation and long-term compensation are made up of indicators (like fixed salaries, short-term incentives, and equity-based or other long-term benefits) that each contribute in a different way to the measurement of the construct but are not interchangeable. When working with secondary data, which frequently contains pre-defined indications that differ in magnitude and character, this method is very pertinent. This was addressed by normalizing variables with different scales (e.g., dummy variables for diversification and continuous variables for R&D spending) and using log transformation to variables like R&D expenditure to standardize scales and reduce skewness. Consistent contributions across indicators are thus guaranteed.

4.3. Study Model (Smart-PLS)

While preparing the data for the final analysis, I applied the Cook’s distance (CD) test to detect potential outliers. According to Cook’s distance, a CD value greater than 1 indicates an outlier (Pallant, 2007); fortunately, there were no outliers in the data. The current research applied partial least squares structural equation modeling (PLS-SEM), as it is more appropriate to deal with complex models with small sample sizes, and this technique is most commonly applied in various research disciplines. Unlike covariance-based SEM (CB-SEM), it does not assume a normal distribution of data and being most applicable in exploratory studies. It helps justify the very purpose of this study, that is, to explore the nuanced relationships around board composition, executive compensation, and R&D spending during the COVID-19 pandemic.

As per PLS-SEM requirements, the sample size should be ten times the number of arrows pointed toward a specific construct in the model (J. F. Hair et al., 2016). In the current research, board composition, executives, and long- and short-term compensation are used as independent variables, and the number of total arrows pointed to a construct is three, which indicates that the sample size for the current analysis should be 30 or more (3 × 10 = 30). Hence, the sample of the current study fulfilled the basic requirement of sample size, as I had 208 total observations. Moreover, Smart-PLS is also known for being able to handle reflective and formative constructs simultaneously, which is one of the reasons why it is very much needed in this study because composite measures are under use in constructs like corporate board characteristics and executive compensation. Besides having these advantages, Smart PLS performs much better than CB-SEM for smaller sample sizes, such as in this study, where the sample size consisted of 52 firms, and it also aids in maximizing explained variance (R²) and predictive relevance (Q²). Moreover, it generates path coefficients and effect sizes, helping to evaluate the relationships between the variables by testing the hypothesis.

Moreover, composite reliability (CR) and average variance extracted (AVE) were used to test reflective components in order to verify convergent validity and internal consistency. In the meantime, formative conceptions were evaluated for indicator relevance using outer weights and for multicollinearity using variance inflation factors (VIF). By addressing the empirical realities of secondary data and ensuring that the measurement model is in line with the theoretical foundations of the constructs, the mixed approach offers a strong framework for analysis. Lastly, using Smart-PLS, the structural equation model was constructed and examined. The model’s structural relationships and predictive power were evaluated to make sure they supported this study’s hypotheses. This methodological method confirms that this study’s analytical process is transparent, reproducible, and rigorous, providing a robust framework for examining the impact of executive compensation and board composition on R&D spending.

5. Results and Discussion

The descriptive statistics for the variables are presented in

Table 2. Descriptive statistics.

Variables	Mean	Minimum	Maximum	Standard Deviation	Skewness	Kurtosis
Board size	12.5	10	15.0	3.5355	0.110	1.200
Independent directors	7.00	0	14.0	9.8995	0.192	1.211
Board meetings	23.5	3	44.0	28.9914	0.175	1.207
Fixed salary	1.00	0.11	1.856	1.2346	0.244	1.109
STIs	0.50	0	0.916	0.6477	0.356	1.029
Equity	0.50	0	0.954	0.6746	0.812	0.636
Other long-term incentives	0.50	0	0.931	0.6583	0.407	1.159
R&D	80.3	0	160.648	113.5953	0.295	1.143
Diversification	26.0	29	23	4.2426	0.494	1.308

. The results indicate that the largest board had a maximum of 15 board members, with an average board size of 12.8. The maximum number of independent directors on the board is 14, with an average of 7 independent directors on the board. The maximum number of board meetings held during the year was 44, with an average frequency of meetings of 23.5 times. Many companies during the height of the pandemic in 2020 significantly increased the frequency of their board meetings, with some boards reportedly meeting weekly or even multiple times a week.

Moreover, Table 2 also indicates that the mean of fixed salary was AUD 100,000, with the maximum and minimum fixed salaries being 185,600 and 11,000 Australian dollars (AUD), respectively. The maximum number of STIs offered by the sample firms is 91,600 Australian dollars, with an average of 50,000. Equity compensation and other long-term incentives are almost similar to the STIs, as the maximum equity and other long-term incentives offered by the companies are AUD 95,400 and 93,100, respectively, with an average of AUD 50,000 in equity value and long-term incentives. The sample has a huge investment in R&D, with an average of AUD 80,300 and a maximum of AUD 16,064,800. Descriptive statistics also show that a total of 23 (44.3%) of the sample firms were engaged in diversification, while 29 (55.7%) were not engaged in diversification. Furthermore, the normality of the data was established through skewness and kurtosis values, which fell within the acceptable criteria. Absolute skewness or kurtosis values greater than one suggest that data. A kurtosis value of ±1 is usually ideal; however, values as high as ±2 are also frequently acceptable (J. Hair et al., 2022).

5.1. Reflective Measurement Scale

The effectiveness of the reflective construct can be assessed by examining three main criteria, i.e., convergent validity, average variance extracted (AVE), and Cronbach’s alpha, which measures internal consistency. However, a basic limitation of Cronbach’s alpha is that it assumes that all indicators are equally reliable and generally underestimates reliability based on internal consistency (J. F. Hair et al., 2016). Thus, considering the limitations of Cronbach’s alpha, this study used Cronbach’s alpha along with composite reliability as a measure of internal consistency reliability, as prescribed by J. F. Hair et al. (2016). According to J. F. Hair et al. (2016), the value of Cronbach’s alpha should be 0.70 or higher, and the value of composite reliability falls between 0.70 and 0.90 for most advanced research. However, the reliability and validity of formative constructs are generally assessed through multicollinearity and the significance of outer weights.

Moreover, to check convergent validity, scholars used average variance extracted (AVE) and the outer loadings of indicators. According to J. F. Hair et al. (2016), the value of AVE should be 0.50 or greater which indicates that a specific construct explains half or more than half of its indicator’s variance. They further added that if the value of AVE is less than 0.50, the variance explained is less than the error that remains in the indicators. The evaluation of multicollinearity and the significance of indicators in formative constructs depends heavily on VIF and item weights. Item weights and VIF do not apply to board composition, which was treated as a reflecting construct. As required for reflective constructs, internal consistency and convergent validity were instead evaluated using outer loadings, composite reliability (CR), and average variance extracted (AVE). The reliability and validity of the reflective and formative constructs are depicted in

Table 3. Reliability and validity of measurement model.

Reflective Constructs	Items	Outer Loadings	Items Weights	t-Statistics	VIF	CR	AVE
Corporate Board	Board Size	0.932 ***	--	29.372	--	0.868	0.695
	Board Independence	0.925 ***	--	32.318
	Board Meeting	0.701 ***	--	7.130
R&D	R&D Expense	0.894 ***	--	3.561	--	0.883	0.716
	Diversification	0.836 ***	--	2.985
	R&D Intensity	0.837 ***	--	9.389
Cash Compensation	Fixed Salary	--	0.893 ***	6.926	1.229	Formative Constructs
	STIs	--	0.729 ***	3.864	1.538
Long-Term Compensation	Equity	--	0.782 ***	4.230	1.034
	Other LTIs	--	0.819 **	4.650	1.020

Note: ** p < 0.05; *** p < 0.01.

.

The composite reliability (CR) value shows that the items are equally reliable, as the current value meets the threshold level, as shown in Table 3. In addition, the AVE of the reflective constructs also meets the threshold level, and outer loadings are statistically significant at the 1 percent level of significance. Moreover, there was no multicollinearity in the formative construct; thus, the validity and reliability of the formative construct were satisfied.

5.2. Discriminant Validity

Fornell and Larcker’s (1981) technique was applied to assess the discriminant validity of reflective constructs.

Table 4. Discriminant validity.

	Corporate Board	R&D
Corporate Board	0.833
R&D	0.723	0.760

shows that the diagonal values are higher than the other values in the rows and columns, which confirms discriminant validity (J. Hair et al., 2022; Akram et al., 2019).

5.3. Results of the Structural Model

The inner model was evaluated based on the coefficients of determination of the variables, predictive power, and effect size. In

Table 5. Path coefficient and hypothesis test.

Hypotheses	Path Coefficients	t-Statistics	Decision	f2	R2	Q2
Corporate Board → R&D	0.673 ***	3.273	Accepted	0.757	0.707	0.234
Cash Comp → R&D	−0.149 *	1.791	Accepted	0.081
LTIs → R&D	0.169 *	1.682	Accepted	0.162

Note: * p < 0.1; *** p < 0.01; (R&D = research and development; Comp = Compensation).

, the R² was used to examine the explained variance. The explained variance (R²) in this study was 0.707, indicating that the model accounted for 70.7% of the variation as shown in Table 5.

To check the model’s predictive relevance, researchers (Stone, 1974; Geisser, 1975; Fornell & Cha, 1994; Chin, 1998) suggested using the Stone–Geisser non-parametric test. As J. F. Hair et al. (2016) note, the predictive relevance, indicated by Q², should be greater than zero to confirm the validity of the model. Q² values of 0.02, 0.15, and 0.35 indicate low, medium, and high predictive power, respectively. A Q² value of 0.234 indicated a medium level of predictive power. J. F. Hair et al. (2016) defined the effect size (f²) as a construct’s contribution to the model’s total variance. The parameters for effect magnitude were similar to those for Q² (J. F. Hair et al., 2016). Figure 1 illustrates the smart partial least squares (PLS) path model.

5.4. Hypotheses Testing and Discussions

The results of the structural model for the hypotheses test reflect some interesting facts. As Table 5 shows, board composition has a significant and positive effect on R&D spending, which means that the board plays a critical role in R&D spending because it is a major source of diverse resources and information (Shadab, 2008). A board’s structure, including criteria such as board size, independence, and meeting frequency, has a large and favorable impact on R&D investment since it acts as a vital source of various resources and information. A well-structured board with an appropriate size brings together a diverse range of knowledge, talents, and viewpoints, allowing for more extensive appraisals of complicated R&D activities. Independent directors give an impartial perspective, challenging groupthink and ensuring strategic alignment with long-term innovation goals (Chiang et al., 2023). Furthermore, the frequency and efficacy of board meetings increase the board’s exposure to new trends, technical breakthroughs, and competitive challenges, all of which are critical drivers of R&D spending. Collectively, these components enable boards to play an active and important role in fostering strategic decisions that promote innovation and maintain the organization’s competitive edge. Thus, its composition refers to the diverse available information required to make important investment decisions. Hence, H1 is accepted at (β= 0.673, t = 3.273, p < 0.01), which is similar to the findings of Shadab (2008). A corporate board plays a central role in an organization and makes all the necessary decisions related to investment and other issues, which has become even more crucial during the COVID-19 pandemic. These findings are also in line with resource dependence theory, which posits that the corporate board brings the resources required for evaluating risky investment projects (Balsmeier et al., 2017). As this study demonstrates, board directors have access to various tools and information that are critical for executing R&D. These results verify that throughout the COVID-19 pandemic, the corporate board’s influence on R&D investment remains unchanged.

The results reported in Table 5 indicate that the second hypothesis is accepted at (β = −0.149, t = 1.791, p < 0.1), which shows that executive cash compensation has a negative and significant effect on R&D spending, similar to those of Sanyal and Bulan (2010). This shows that executive compensation based on cash incentives reduces investment in R&D because R&D investment decreases current-year performance, leading to a decrease in cash incentives. In other words, R&D spending negatively influences firms’ short-term financial performance; therefore, CEO pay-for-performance or cash compensation decreases if they fail to meet their short-term performance goals. Thus, CEOs adopt myopic behavior, which leads to low R&D spending. These findings strengthen the agency’s theory that managers are self-interested and need to be monitored effectively. Another piece of evidence from the literature phrases this concept as follows: higher cash incentives would result in higher R&D (L. Wang et al., 2023). When directors’ cash compensation is not linked with uncertain investments like R&D and innovation, they are keener to pursue long-term investments. We can conclude that the effect of cash compensation on R&D development did not change during COVID-19, as the companies ran out of cash and focused on surviving during that period. Companies were running out of cash, which caused a significant decrease in R&D during the COVID-19 pandemic.

Furthermore, the regression analysis confirms hypothesis three; however, the coefficient from LTIs to R&D has a lower significance level than the other two coefficients. Hypothesis 3 is supported (β = 0.169, t = 1.682, p < 0.1). Although the path coefficient of LTIs to R&D is smaller, it has a positive effect on R&D spending. This finding shows that long-term incentives as part of executive compensation positively influence R&D spending, as documented by Hall and Lerner (2010). Directors are intrinsically focused on long-term incentives since their compensation is linked to the organization’s continued success.

According to Flammer (2017), including long-term incentives in remuneration is critical for increasing company innovation. Similarly, equity-based incentives have been found to boost business innovation development (L. Chen, 2019). Because R&D is a long-term investment that normally yields benefits in 3 to 5 years, equity-based pay aligns board members’ interests with the company’s long-term objectives. This synergy encourages directors to prioritize investments in innovation because successful R&D activities have the potential to boost both the company’s worth and its personal wealth over time (B. Wang et al., 2024). Furthermore, equity incentives instill a sense of ownership in directors, pushing them to be more aggressive in driving the firm toward innovation. Such incentives also limit the danger of making short-term decisions, ensuring that the emphasis stays on initiatives that create long-term development and competitive advantage.

5.5. Robustness Check of Results

This research applied two robustness tests to verify the empirical results obtained in the previous section. These robustness tests include the linearity test (using RAMSEY’s reset test) and the endogeneity test, which was performed using Park and Gupta’s (2012) Gaussian copula approach. The robustness tests were proposed by Sarstedt et al. (2020). The results of RAMSEY’s reset test indicate that the specified model is linear in its relationship because the F-value is 0.372 and the p-value is 0.759, which means that we cannot reject H0 (linearity) of RAMSEY’s reset test.

Table 6. RAMSEY’S reset test.

Nonlinear Relationship	Path Coefficients	t-Statistics	RAMSEY’s Test
(Corporate Board)2 → R&D	0.140	1.049	F-value = 0.372, p-value = 0.759
(Cash Compensation)2 → R&D	0.031	0.864
(LTIs)2 → R&D	0.201	1.280

presents the results of the linearity tests.

The endogeneity of the model was confirmed using the Gaussian copula approach following the method proposed by Sarstedt et al. (2020). The results reported in

Table 7. Endogeneity test, Gaussian copula approach.

	Variables	β	p-Values
Gaussian copula of model 1 (endogenous variable; CB)	CB	0.220	<0.01
	CC	0.203	0.619
	LTIs	0.110	0.822
	c CB	0.031	0.975
Gaussian copula of model 2 (endogenous variable; CC)	CB	0.140	<0.01
	CC	0.227	<0.01
	LTIs	0.119	0.788
	c CC	0.101	0.869
Gaussian copula of model 3 (endogenous variable; LTIs)	CB	0.151	<0.01
	CC	0.129	<0.01
	LTIs	0.114	0.079
	c LTIs	0.019	0.907
Gaussian copula of model 4 (endogenous variable; CB, CC)	CB	0.145	<0.01
	CC	0.179	<0.01
	LTIs	0.105	0.666
	c CB	0.100	0.805
	c CC	0.009	0.847
Gaussian copula of model 5 (endogenous variable; CC, LTIs)	CB	0.205	<0.01
	CC	0.165	<0.01
	LTIs	0.100	0.701
	c CC	0.002	0.988
	c LTIs	0.041	0.859
Gaussian copula of model 6 (endogenous variable; CB, LTIs)	CB	0.198	<0.01
	CC	0.158	<0.01
	LTIs	0.095	0.563
	c CB	0.018	0.606
	c LTIs	0.042	0.809
Gaussian copula of model 7 (endogenous variable; CB, CC, LTIs)	CB	0.188	<0.01
	CC	0.147	<0.01
	LTIs	0.109	0.833
	c CB	0.009	0.709
	c CC	0.049	0.407
	c LTIs	0.007	0.582

Note: CB stands for corporate boards, CC stands for cash compensation, LTIs stands for long-term incentives, and ^c stands for copulas.

reveal that none of the Gaussian copula (indicated by ^c) from Models 1 to 7 is significant, which confirms the non-existence of endogeneity in this study. Hence, the robustness of the results of the structural model was established.

6. Conclusions and Recommendations

Innovation becomes crucial for the survival of any organization, as a lack of innovation is the reason for the failure of several companies. Investment in innovation and R&D activities is usually decided by top management, and they decide to invest in risky projects according to their interests. This situation became crucial with the pandemic outbreak in 2020, and companies began to close their operations, which included physical human involvement. When companies’ businesses shrank, they curtailed the compensation of their employees; therefore, this research aimed to investigate the influence of board composition and CEO’s short-term and long-term compensation on R&D spending aimed at COVID-19. For this, I used a sample from the world’s top 48 most innovative companies working in Australia for 2020 and 2021. This research is novel and different from previous studies as it used composite measures of compensation, such as short-term compensation, long-term compensation, and corporate board composition, to scrutinize its effect on R&D.

The findings of this study indicate that board composition has a positive effect on R&D spending. These findings indicate that corporate boards provide the essential resources needed by firms in critical decisions. In addition, an independent board increases the monitoring effectiveness of the board, which ultimately ensures that the board’s resources are appropriately used for long-term value-creation projects. The board’s meeting frequency also affects the time required to process information at the top level of management. Hence, the findings of the current study indicate that the board structure of the sample firms remains the opposite for R&D spending during COVID-19.

Moreover, based on the theoretical groundings, we expected a negative impact of cash compensation on R&D spending, and the findings of the current study support this proposed statement. There are certain reasons for this, such as the time-based difference between cash compensation and R&D sending, as cash compensation generally consists of short-term incentives, short-term performance-based rewards, and fixed salary, while the nature of R&D is long-term. Such time-based differences between R&D spending and executives’ cash compensation cause myopic behavior, with executives emphasizing short-term performance enhancers. The findings of this study are also consistent with the hypothesized statements about the positive effect of executives’ long-term compensation on R&D spending. Executives’ long-term compensation and R&D have a long-term time horizon (Lotfi & Rost, 2014); therefore, a positive relationship exists between the two. When directors have long-term incentives, they feel extrinsically and intrinsically motivated toward investments in long-term value creation projects because they will only be able to earn a profit from their long-term incentives if they remain profitable in the long run. From these findings, we conclude that the effect of executive compensation on R&D spending remains consistent, as expected. When companies face difficulties in meeting their short-term goals, they reduce their risky investments such as R&D. However, with long-term planning, firms can sustain a pandemic in the future; hence, they plan their innovation activities accordingly.

This study has several implications based on the empirical findings, as it suggests that agency issues and executives’ myopic behavior can be reduced by using appropriate incentives for executives. This will help align the interests of owners and agents towards the same long-term value creation objectives of corporations. Further, the results of this study also indicate that managers can be motivated if their compensation is appropriately aligned with short- and long-term firm value. This study goes beyond the conventional boundaries of agency theory to expand the current theoretical framework of corporate governance. The results demonstrate the importance of the board of directors as an organizational resource and validate the conclusion that board structure has a major impact on R&D expenditure. In addition to keeping an eye on management, boards also provide a variety of networks, information, and experience that are critical for making good decisions. According to this resource-based viewpoint, directors contribute important expertise and insights that improve the board’s capacity to assess intricate R&D projects and match them with long-term corporate objectives. This study opens the door for future research to examine governance structures that foster innovation and sustainable growth by combining the monitoring and resource-provision roles. This helps to create a more thorough understanding of how corporate governance mechanisms affect strategic investments like R&D.

This paper, based on data from the most innovative manufacturing companies in Australia, could benefit from the application of a more extensive dataset featuring companies and industries from different countries. One exciting direction for future study would be analyzing the effects of asymmetric information, together with the human and social capital of board members on research and development spending. Also, whereas this study focuses only on R&D investment and does not consider the outcome or output of such investments, future studies need to fill this gap in knowledge so that there will be a full comprehension of how board characteristics affect R&D expenditure and, ultimately, innovation performance.