Abstract
Purpose: The purpose of this study is to discuss the mediating effects of research and development (R&D) and advertising expenditures on the relationship between free cash flow (FCF) and sales from a dynamic perspective.
Design/methodology/approach: Drawing upon the resource-based and knowledge-based views, we propose a dynamic model, in which FCF change positively affects R&D expenditure change and advertising expenditure change, which in turn positively affect sales change, and the effect of FCF change on R&D expenditure change is moderated by industry type (high-tech vs. non-high-tech). To test for the dynamic mediating effects, we use a two-step approach that incorporates growth modelling and structural equation modelling, utilising longitudinal financial data.
Findings/results: Research and development and advertising expenditure changes mediate the relationship between prior FCF change and subsequent sales change, with R&D expenditure change having a stronger dynamic mediating effect for high-tech firms.
Practical implications: As FCF increases, managers should allocate a proportionate share of the incremental resources to R&D and advertising. High-tech firms should be particularly attentive to the need for allocation to R&D. Short-term gains should not come at the expense of long-term value creation. Businesses should establish mechanisms for continuous monitoring and adaptation.
Originality/value: The results spotlight the mechanism of dynamic mediation of R&D and advertising expenditures on the relationship between FCF and sales and the moderating role of industry type on the dynamic mediation of R&D expenditures. This study enhances our understanding of dynamic mediation in the context of strategic investments.
Keywords: advertising; change; dynamic mediation; free cash flow; R&D; sales.
Introduction
The allocation of financial resources is fundamental to a firm’s strategic investment decisions, shaping innovation and performance outcomes across industries (Tvaronaviciene & Burinskas, 2021). Free cash flow (FCF) has emerged as a crucial driver of research and development (R&D) and marketing investments, with recent evidence showing positive internal cash flows increasingly support R&D expenditures among mature firms in developed economies (Chinloy et al., 2020; Perez-Alaniz et al., 2023; Tvaronaviciene & Burinskas, 2021). These effects are most pronounced when internal funding becomes the main determinant of innovation investment, especially in large organisations (Perez-Alaniz et al., 2023).
Recent scholarship also documents how emerging market firms channel FCF towards sustainability and social-environmental objectives, often influenced by ownership structure (Junias & Suharto, 2024). At the same time, the FCF hypothesis cautions that excess cash can lead to agency problems – highlighting the need for balanced resource allocation (Jensen, 1986).
Despite robust empirical links between FCF, R&D and advertising expenditures, the underlying dynamic mechanisms remain insufficiently explored. Leading research calls for more longitudinal and dynamic modelling to capture how innovation, resources and firm advantage interact over time (Peteraf & Barney, 2003; Sirmon et al., 2007). Internal funds are consistently shown to drive both R&D and advertising, fostering output growth and mediating firm-specific sustainability of competitive advantage (Sveikauskas et al., 2024; Wu et al., 2022). Contemporary innovation research further emphasises the importance of resource-based and knowledge-based frameworks, organisational capabilities and strategic resource deployment (Ali et al., 2023; Martin & Eisenhardt, 2010; Schilke et al., 2017).
According to Pitariu and Ployhart (2010), a dynamic relationship refers to a longitudinal relationship between two variables, both measured repeatedly over time. Dynamic mediation can be conceptualised as three growth models linked together: one for change in the predictor over time, one for change in the mediator over time, and one for change in the outcome variable over time. Change in the predictor is linked to change in the mediator, which in turn is linked to change in the outcome variable. Therefore, only time-varying variables can be included when modelling dynamic mediation. Moreover, the predictor should precede the mediator, which should also precede the outcome variable, even though all three are repeatedly measured over time.
Change over time is a crucial consideration when analysing dynamic relationships and can be effectively captured through growth modelling (Chen et al., 2011; Ployhart et al., 2011). A linear growth trajectory model includes a random intercept (representing the initial status) and a random slope (representing the rate of change), both of which are unobservable. Although the mean of yearly growth rates (or log differences) of a variable can be used to approximate its rate of change, this approach may introduce bias. A better way is to regress the variable on time and use the regression coefficient associated with time to measure the change rate (e.g. Barton & Gordon, 1988). Growth modelling, however, provides even more precise estimates of unobserved change rates using empirical Bayes estimation (Chen et al., 2011). This study employs a two-step approach to analyse dynamic mediation. In the first step, growth modelling is used to capture change over time and to obtain estimates of the unobserved random slopes of the linear growth trajectories for FCF, R&D expenditures, advertising expenditures, and sales across firms, taking time lags into account. In the second step, structural equation modelling (SEM) is applied, using the slope estimates from the first step as input, to examine the structural dynamic relationships. Despite their potential value, longitudinal studies employing these methods to test for the resource-based view are not frequently found in the literature. This study responds to these gaps by employing dynamic mediation methodology to examine the mediating roles of R&D and advertising expenditures on the relationship between FCF and sales. The aim is to provide new empirical insights into the dynamic relationships across financial resources, innovation investments and firm performance, extending both the resource-based and knowledge-based views (Dubey et al., 2019). The following sections review current literature, outline methodological design, discuss findings, and evaluate theoretical and practical implications.
Theory and hypotheses
The dynamic relationships between FCF and expenditures on R&D and advertising
Recent studies demonstrate a robust, positive relationship between FCF and strategic investments in both advertising and R&D, with resource-rich firms most able to allocate substantial funds towards marketing and innovation activities (Ali et al., 2023; Sveikauskas et al., 2024). Empirical analyses confirm that the ‘affordability’ principle remains highly relevant: when internal financial resources are ample, firms reliably increase their advertising expenditures (Sveikauskas et al., 2024; Wu et al., 2022). Dynamic models further reveal that both R&D and advertising play mediating roles between FCF and sales growth, highlighting the strategic importance of resource allocation for firm performance (Ali et al., 2023).
Free cash flow is a key determinant of the firm’s R&D and advertising spending. Longitudinal evidence shows that rising FCF boosts both R&D and advertising expenditure, while declining FCF suppresses these investments. Thus, we hypothesise:
H1: Free cash flow change positively affects R&D expenditure change and advertising expenditure change.
Generally, FCF might be more prevalent in technology companies. Industries in the high-tech sector – characterised by rapid technological change and heavy investment in R&D – rely extensively on FCF to fund large-scale innovation and sustain competitiveness (Liu & Zhang, 2023; Xin et al., 2019). Investing in R&D is critical for firms in uncertain and technologically complex industries, as these firms pursue new R&D initiatives to manage uncertainty, enhance strategic resilience, and leverage information about resource or market quality (Lin et al., 2021; Nguyen & Nguyen, 2024). Recent research confirms that basic and innovative R&D investments yield greater productivity gains in high-tech industries, with Chief Executive Officers (CEOs) in these sectors more inclined to allocate rising FCF towards high-risk R&D initiatives when resources are available (Nguyen & Nguyen, 2024; Xin et al., 2019). We hypothesise:
H2: Industry type moderates the positive relationship between free cash flow change and R&D expenditure change, with a stronger effect for high-tech firms than for non-high-tech firms.
The dynamic mediating roles of R&D and advertising expenditures
The resource-based view posits that a firm achieves and sustains competitive advantage by possessing resources and capabilities that are valuable, rare, inimitable and non-substitutable (Barney, 1991). Within this theoretical framework, intangible resources such as R&D and advertising are recognised as essential for long-term performance, with their value stemming from their tacit and immobile nature. Recent empirical research further establishes that R&D and advertising function as strategic intangible assets that can drive various dimensions of firm performance, with R&D exerting a stronger influence on firm value and advertising enhancing profitability, even under conditions of environmental volatility (Piao & Choi, 2022).
Innovation remains fundamental to achieving and sustaining competitive advantage in both advanced and emerging economies (Giudici et al., 2017). Contemporary studies find that increased R&D expenditure is consistently linked to enhanced innovation outputs at both the firm and national levels (Ivanová & Žárská, 2023; Kahn et al., 2024). Furthermore, empirical analyses demonstrate that technological innovation, together with R&D investments, plays a crucial role in driving economic growth and strengthening scientific production across diverse contexts (Arana-Barbier, 2023).
Firms create and accumulate knowledge through their internal R&D activities, which not only spur firm-level innovation but also enhance productivity and knowledge spillovers to the broader economy (Audretsch & Belitski, 2023). Corporate R&D expenditure and patent accumulation represent vital measures of knowledge flows and stock within firms, both serving as key determinants of innovation capacity and competitive performance (Audretsch & Belitski, 2023; Mallinguh et al., 2022). The interplay between technological knowledge creation – spurred by sustained R&D – and complementary skills, including technology transfer and patent portfolio management, has significant implications for firm growth and competitive heterogeneity (Arana-Barbier, 2023; Mallinguh et al., 2022). Empirical evidence further demonstrates that firms with advanced innovation outputs and higher R&D intensity consistently report notable improvements in productivity, employment and sales, reflecting the enduring value of innovative investment for long-term performance (Kahn et al., 2024).
Advertising serves as a key channel for firms to communicate value, enhance sales, and strengthen firm-level profitability and competitive positioning (Piao & Choi, 2022). The signalling theory underscores how advertising signals product quality to consumers, thereby influencing purchasing decisions (Kihlstrom & Riordan, 1984). Recent evidence shows that advertising investments, particularly when complemented by digital marketing capabilities, provide significant profitability gains beyond those achieved by traditional marketing alone (Homburg & Wielgos, 2022). Resource-based logic positions advertising and R&D as complementary intangible assets, both of which enable differentiation and sustained firm performance (Piao & Choi, 2022). Innovation capability, when integrated with intellectual capital and dynamic marketing resources, forms a multidimensional basis for ongoing innovative performance (Ali et al., 2023). Contemporary perspectives suggest that brand advertising aims to deliver a meaningful, compelling and achievable promise to consumers, with innovation playing a dominant role in shaping this message (Martin et al., 2024). Altogether, R&D and advertising are strategic resources that enhance firm performance and support lasting competitive advantage (Piao & Choi, 2022). Grounded in resource-based and knowledge-based perspectives, the analysis posits the following temporal patterns: increases (or decreases) in FCF are expected to result in corresponding rises (or reductions) in R&D and advertising expenditures, subsequently influencing sales in the same direction. The mediating role of R&D expenditure change is predicted to be more substantial in high-tech industries than in other sectors. Since FCF change is positively correlated with changes in both R&D and advertising outlays, which in turn are associated with sales change, and industry type moderates the link between FCF change and R&D expenditure change, the hypotheses are as follows:
H3: R&D expenditure change mediates the positive relationship between free cash flow change and sales change, with a stronger mediating effect for high-tech firms than for non-high-tech firms.
H4: Advertising expenditure change mediates the positive relationship between free cash flow change and sales change.
The hypothesised relationships among FCF change, R&D and advertising expenditure changes, industry type, and sales change are summarised in the conceptual framework diagram presented in Figure 1. The diagram reflects the dynamic mediation structure, moderating effect and relevant time periods for all focal variables included in the empirical model.
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FIGURE 1: Research framework illustrating the hypothesised relationships (H1–H4) among the variables. |
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Methodology
Sample
The sample firms used in this study encompass both high-tech and non-high-tech firms in North America. Longitudinal financial and organisational data were obtained from established sources, including Compustat, U.S. Securities and Exchange Commission (SEC) filings, the Thomson Reuters Institutional Holdings (13F), and the Institutional Brokers’ Estimate System (IBES), covering the fiscal years 2012–2019. This timeframe was deliberately chosen to focus on a period of relative macroeconomic stability, thereby excluding the effects of the global financial crisis, the European debt crisis before 2012 and the unprecedented structural disruptions posed by the COVID-19 pandemic after 2019.
Restricting the sample to this pre-pandemic interval ensures that observed changes in FCF, R&D and advertising expenditures reflect typical firm behaviour rather than extraordinary responses to exogenous shocks. This design strengthens the interpretability of dynamic mediation and longitudinal analyses, as confounding effects from crisis-driven policy interventions and demand volatility are systematically avoided. Furthermore, 5 consecutive years of data per firm are required to enable robust estimation of random slopes and the dynamic mediating and moderating effects.
Consistent with prior longitudinal management research, our choice of these years supports comparability with existing benchmarks and establishes a necessary baseline for future studies analysing post-pandemic change. Financial and utilities firms were excluded because of fundamentally different capital structures and regulatory regimes (Klasa et al., 2018). Data for focal and control variables cover 5 consecutive years for each firm. Out of an initial pool of 5388 firms, 4673 were excluded because of incomplete data on the primary variables of interest, leaving 715 firms with full longitudinal coverage. After removing an additional 15 firms with missing control variables, the final sample comprised 700 firms (193 high-tech and 507 non-high-tech, classified using Standard Industrial Classification (SIC) codes per U.S. SEC Office of Technology guidance), yielding 3500 firm-year observations (5 × 700) for hypothesis testing.
Sample selection bias can occur when a considerable amount of data is missing. Heckman’s two-step procedure (Heckman, 1976) can be employed to examine and correct for potential sample selection bias. A probit model is applied to the binary (missing or non-missing) data on focal variables for 2411 firms with complete data on the control variables.
Variables
To capture change over time, we use the slope of the linear growth trajectory, encompassing a 5-year span. How to obtain the slopes of the linear growth trajectories of the focal variables and specify the relationships among these slopes will be elucidated in the next section. Free cash flow is measured as ‘the cash flow from operating activities minus capital maintenance expenditures’ (Mills et al., 2002). Research and development and advertising are two types of investments in intangible assets for which expenditure data are available. Sales refer to net sales, calculated as gross sales less discounts and sales returns and allowances. Sales data are from 2015 to 2019. Both advertising and R&D expenditures exhibit time-lag effects on sales, with the former often generating more immediate impacts than the latter (Cohen et al., 2010). A 2-year lag is applied between R&D expenditures and sales (Chun et al., 2015). Therefore, R&D expenditure data span from 2013 to 2017. A 1-year lag is adopted for advertising expenditures (Kim & Joo, 2013), covering advertising expenditures from 2014 to 2018. Free cash flow also has time-lag effects on R&D and advertising expenditures, with faster effects necessitating a 1-year time-lag. Due to varying time lags, two FCF variables are used. The data for the first FCF variable span from 2012 to 2016, while the data for the second one cover the period from 2013 to 2017. Note that lagging offers another advantage: It can help alleviate concerns about endogeneity (Pitariu & Ployhart, 2010).
We control for the following firm attributes:
Capital-labour ratio. The capital-labour ratio (denoted by CLR) is the ratio of capital to total labour employed. Firms tend to have a higher CLR when they aim to improve labour productivity through capital investment.
Firm leverage. Firm leverage (denoted by LVRG) is related to firm performance (Burrus et al., 2018; Fosu, 2013). In this study, LVRG is assessed as the ratio of total debt to total equity (Burrus et al., 2018).
Financial constraints. Financial constraints are constraints that come with money. Financial constraints exert influence on firm expenditures and firm performance (Golovko et al., 2022). In this study, financial constraints are measured using the size-age (SA) index developed by Hadlock and Pierce (2010).
Corporate governance. Corporate governance mechanisms are widely studied for their influence on firm outcomes, with institutional ownership (IO) playing a significant monitoring role in shaping firm performance, particularly in emerging economies (Abedin et al., 2022). Following established empirical practice, this study adopts both IO and analyst following (AF) as proxies for corporate governance, in line with prior literature (Chung et al., 2003).
Firm size. Firm size (denoted by SIZE), a standard control variable, is measured here as the natural logarithm of total assets (e.g. Fosu, 2013).
Firm age. Firm age (denoted by AGE), also a common control variable, is calculated as the number of years between a firm’s establishment and the year 2019.
Analytical strategy
We use a two-step approach to analyse dynamic mediation. The first step uses growth modelling to capture change over time and provides estimates for unobserved random slopes of the linear growth trajectories of FCF, R&D expenditures, advertising expenditures and sales for all firms, considering time lags. In the second step, SEM is used, with the slope estimates obtained from the first step as input, to analyse the structural dynamic relationships, including the direct effects of prior FCF changes on subsequent sales change, indirect effects through subsequent R&D expenditure change and advertising expenditure change, and the moderating effect of industry type (high-tech vs. non-high-tech) on the indirect effect through R&D expenditure change.
We modelled the moderating effect of industry type (high-tech vs. non-high-tech) on the relationship between FCF and R&D expenditure only, as there is no theoretical basis to support the moderating effect of industry type on the FCF–advertising expenditure relationship. Resource-based and innovation perspectives suggest that R&D investment in high-technology sectors is particularly contingent upon the availability of resources. In contrast, advertising expenditures in both high-tech and non-high-tech firms tend to be more routine and exhibit limited industry-level variation in response to fluctuations in FCF.
Because time lags for the effects of FCF vary, we employ two FCF variables. Let SALESit represent the sales of firm i at time t, t = 2015–2019, FCFLAG3it–3 represent 3-year-earlier FCF (2012–2016), FCFLAG2it–2 represent 2-year-earlier FCF (2013–2017), RDit–2 represent 2-year-earlier R&D expenditures (2013–2017), and ADit–1 represent 1-year earlier advertising expenditures (2014-2018). The level-1 growth model for FCFLAG3it–3 is given by (Equation 1):
where i = 1, …, n (sample size, 700 in this study), t = 2015–2019, Time denotes the independent variable for the five time points (5 years), and ε1it–3 are level-1 error series. Since the error series tends to be autocorrelated and autocorrelation should not be ignored, AR(1) (the first-order autoregressive process), commonly used for capturing autocorrelation (Littell et al., 2006, p. 175), is specified in the level-1 growth model. η10i and η11i denote the intercept and slope of the linear growth trajectory of FCFLAG3 for firm i. They are random coefficients and are specified in the level-2 growth model given by (Equation 2):
where γ10 and γ11 denote, respectively, the means of the random intercept η10 and the random slope η11, and δ10i and δ11i are level-2 errors for firm i. δ10i and δ11i are correlated errors with variances  and covariance σδ10δ11. The growth models for RDit–2, FCFLAG2it–2, ADit–1, and SALESit are specified in the same way.
Growth modelling can be handled by using hierarchical linear models (e.g. Chen et al., 2011). Estimates of random coefficients for individuals can be obtained with empirical Bayes estimation, taking into account the information from the entire sample. The empirical Bayes estimates are more precise than those resulting from OLS (Chen et al., 2011).
In the second step of the approach, we analyse the dynamic relationships among the random slopes by using SEM with the empirical Bayes estimates obtained from the first step as input. In addition to the firm attributes mentioned in the previous section, the average levels of FCF, R&D expenditures and advertising expenditures across years were held constant (Chen et al., 2011). Let FCFLAG3change, RDchange, FCFLAG2change, ADchange and SALESchange represent, respectively, η11, η21, η31 η41, and η51, denoting the random slopes (the rates of change) of the linear growth trajectories of FCFLAG3, RD, FCFLAG2, AD, and SALES. The assessment of the dynamic mediation requires Equation 3, Equation 4, Equation 5 and Equation 6, presented as:
where i = 1, …, 700, HT denotes the dummy variable with value of 1 if the firm is in a high-tech industry and 0 otherwise, FCFAVG, RDAVG, ADAVG, CLRAVG, LVRGAVG, SAAVG, IOAVG, AFAVG, and SIZEAVG denote, respectively, the average levels of FCF, R&D expenditures, advertising expenditures, CLR, LVRG, SA, IO, AF and SIZE across years, AGE denotes the firm age, and ν1 − ν4 are uncorrelated errors. β11 denotes the effect of 3-year-earlier FCF change on 2-year-earlier RD change. ϕ13 denotes the moderating effect of HT on the relationship between 3-year-earlier FCF change and 2-year-earlier RD change. Thus, ϕ11 is the effect of 3-year-earlier FCF change on 2-year-earlier RD change for non-high-tech firms, and (ϕ11 + ϕ13) is that for high-tech firms. ϕ21 denotes the effect of 2-year-earlier FCF change on 1-year-earlier AD change. ϕ31, ϕ32, ϕ33 and ϕ34 stand for, respectively, the effects of 3-year-earlier FCF change, 2-year-earlier RD change, 2-year-earlier FCF change, and 1-year-earlier AD change on sales change. It follows that the indirect effect (i.e. the mediating effect) of FCF change on sales change through RD change is given by β11ϕ32, that for high-tech firms is given by (ϕ11 + ϕ13)ϕ32, and that for non-high-tech firms is given by ϕ11ϕ32. The difference in the indirect effects between high-tech and non-high-tech firms is ϕ13ϕ32. The indirect effect of FCF change on sales change through AD change is ϕ21ϕ34. Structural equation modelling can be used to test for these effects. Given that the data are non-normal, the Satorra and Bentler (1994) correction, often referred to as a robust maximum likelihood method, is utilised. The tests are right-tailed. The variables are all winsorised at the upper and lower 1% to mitigate the influence of outliers.
As previously mentioned, of the 2411 firms with complete data on the control variables – CLRAVG, LVRGAVG, SAAVG, IOAVG, AFAVG, SIZEAVG and AGE, only 700 firms have non-missing data for the focal variables. Heckman’s two-step procedure consists of the substantive equation (one of Equations [3–6]) and the selection equation (with the predictors of the control variables). If the disturbances associated with the two equations are correlated, sample selection bias exists. The probit model in Step 1 is used with the dummy endogenous variable coded as 1 when all focal variables’ data are present and 0 otherwise to model selection. The resulting new variable, known as the inverse Mills ratio, is then added to the substantive equation in Step 2 to correct for the bias. On the other hand, if the disturbances are uncorrelated, there exists no sample selection bias and no correction is needed. The task can be handled by using PROC QLIM in SAS/ETS (SAS Institute Inc., 2015a). In this study, there is no sample selection bias, as the disturbances associated with the two equations are small and not significantly correlated (p > 0.78).
Given the non-normality of the data, robustness was assessed using the bias-corrected bootstrap approach. Simulation evidence from MacKinnon et al. (2004) indicates that this method provides superior resampling accuracy overall. For the right-tailed test for an effect, we examine the lower confidence limit only. If the lower bootstrap confidence limit at the 100(1 – α)% confidence level is greater than zero, then the effect estimate is significantly positive at the α significance level.
Ethical considerations
This article followed all ethical standards for research without direct contact with human or animal subjects.
Results
Growth modelling is the first step of the two-step approach. PROC MIXED in SAS/STAT (SAS Institute Inc., 2015b) was used to obtain the slopes of the focal variables, which served as input for the second-step analysis.
Table 1 presents descriptive statistics of Step 2 variables (including FCFLAG3change, RDchange, FCFLAG2change, ADchange, SALESchange, FCFAVG, RDAVG, ADAVG, CLRAVG, LVRGAVG, SAAVG, IOAVG, AFAVG, SIZEAVG, and AGE). Due to the particularly high correlation between FCFLAG2change and FCFAVG (0.99), which resulted in severe collinearity, FCFAVG was dropped from Equations (5) and (6). After dropping FCFAVG, the maximum of the variance inflation factors (VIF) was less than 10, indicating no severe collinearity (Kutner et al., 2005, p. 409).
| TABLE 1: Means, standard deviations and correlations for Step 2 variables (N = 700). |
The main SEM results by considering time lags for the dynamic relationships are reported in Table 2. Models 1, 2, 3, and 4 in the table correspond to Equations (3), (4), (5), and (6). Model 1 reports the results of the effect of 3-year-earlier FCF change on 2-year-earlier R&D expenditure change. Model 2 reports the results of the moderating effect of industry type on the effect of 3-year-earlier FCF change on 2-year-earlier R&D expenditure change. Model 3 reports the results of the effect of 2-year-earlier FCF change on 1-year-earlier advertising expenditure change. Model 4 reports the results of the effects of 3-year-earlier FCF change, 2-year-earlier R&D expenditure change, 2-year-earlier FCF change and 1-year-earlier advertising expenditure change on sales change. FCFAVG was not included as a control variable in Models 3 and 4 to avoid collinearity. Based on the cutoff criteria for fit indexes proposed by Hu and Bentler (1999) – comparative fit index (CFI) ≥ 0.95, standardised root mean square residual (SRMR) < 0.08 and root mean square error of approximation (RMSEA) < 0.06 – the model fit of the SEM incorporating Models 1, 3, and 4 (used to test for H1 and H4) is satisfactory (CFI = 0.99, SRMR = 0.036, RMSEA = 0.046). The SEM incorporating Models 2, 3, and 4 (used to test for H2 and H3) also demonstrates a satisfactory fit (CFI = 0.987, SRMR = 0.038, RMSEA = 0.043). The R2 values for Models 1, 2, 3 and 4 are 0.443, 0.463, 0.200 and 0.573, respectively. According to Cohen’s (1992) criteria for small, medium and large R2 values (0.0196, 0.1304 and 0.2592, respectively), these values indicate medium to large effect magnitudes.
| TABLE 2: Main structural equation modelling results for examining the dynamic relationships (N = 700). |
Right-tailed tests were conducted to test for the positive effects. It appears that the effect of 3-year-earlier FCF change on 2-year-earlier RD expenditure change and the effect of 2-year-earlier FCF change on 1-year-earlier AD expenditure change are both significantly positive ( = 0.307, p = 0.001;  = 0.06, p = 0.027). Therefore, H1 is supported. The moderating effect of HT is significant ( = 0.232, p = 0.035). Since the effect of 3-year-earlier FCF change on 2-year-earlier RD expenditure change for high-tech firms ( = 0.468, p < 0.001) and that for non-high-tech firms ( = 0.236, p = 0.027) are both significantly positive, and the former is greater than the latter, H2 is supported. Moreover, the effect of 2-year-earlier RD expenditure change and the effect of 1-year-earlier AD expenditure change on sales change are both significantly positive ( = 2.967, p = 0.022;  =11.657, p < 0.001). The mediating effect of RD expenditure change is significantly positive ( = 0.911, p = 0.036). While the mediating effect of RD expenditure change is significantly positive for both high-tech firms ( = 1.389, p = 0.029) and non-high-tech firms ( = 0.7, p = 0.06 < 0.1), the difference between the two groups (high-tech minus non-high-tech) is also significant ( = 0.689, p = 0.1). The mediating effect of AD expenditure change is significantly positive ( = 0.7, p = 0.056 < 0.1). Therefore, H3 and H4 are supported. Note that the mediating effect of 2-year-earlier RD expenditure change is complete (because the direct effect is non-significant ( = 1.506, p = 0.171), while that of 1-year-earlier AD expenditure change is partial (because the direct effect is significantly positive ( = 2.028, p = 0.049). Assessment of the mediating effect of RD expenditure change, moderated by industry type, and the mediating effect of AD expenditure change using SEM is presented in Figure 2.
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FIGURE 2: Assessment of the dynamic mediating effect of R&D expenditures, moderated by industry type and the dynamic mediating effect of advertising expenditures using SEM. |
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The test results for the hypothesised effects resulting from the bias-corrected bootstrap method (with 5000 replications) are summarised in Table 3. Robustness has been achieved since the test results for the effects are all consistent with those with SEM.
| TABLE 3: Lower bootstrap confidence limits at the 99%, 95% and 90% confidence levels for the hypothesised effects. |
Collectively, the findings illuminate the dynamic mediation mechanism involving R&D and advertising expenditure changes in shaping the connection between prior FCF change and subsequent sales change.
Discussion
The results indicate that higher levels of FCF stimulate sales growth by enabling greater investment in R&D and advertising. Moreover, the dynamic mediating effect of R&D expenditure change on the link between FCF change and subsequent sales change is contingent upon industry type, showing stronger mediation for high-tech firms. These findings are robust, as demonstrated by the implemented checks.
Practical implications
Our findings underscore the strategic importance of FCF allocation for driving innovation and market presence. Firms with significant increases in FCF should increasingly allocate resources to R&D and advertising expenditures. These investments offer firms a dual advantage of capturing innovation and augmenting market share, thereby fostering growth and competitiveness. Notably, our findings indicate that firms experiencing higher rates of FCF change can leverage these advantages by correspondingly enhancing their R&D and advertising expenditure change rates. Industry type emerges as a moderator, with high-tech firms benefiting more from increased R&D investments. The sales gains associated with greater FCF directed to R&D are especially strong in high-tech industries, highlighting the need for industry-informed resource allocation strategies. This research also affirms that achieving immediate benefits should never compromise the pursuit of sustainable, long-term value creation. Prudent investments in R&D and advertising, rather than immediate profit-generation activities, are crucial for sustaining competitive advantages.
Investing in its own R&D constitutes a significant opportunity for a firm to accumulate critical competencies through cumulative learning and knowledge accumulation, ultimately enhancing sustainable competitive advantages (Barley et al., 2017). Restraining R&D expenditures in order to sustain short-term financial gains undermines the creation of enduring value, as the long-term benefits of R&D spending are well-established while short-term effects are limited (Ravšelj & Aristovnik, 2020). The importance of FCF and R&D investments in value creation has been affirmed (Khidmat et al., 2019), suggesting the need for careful consideration before undertaking mergers, rewarding leadership or distributing to shareholders.
A striking example of the long-term consequences of misallocating FCF can be seen in Intel Corporation. Former Intel CEO Paul Otellini famously declined an offer from Apple’s Steve Jobs to develop chips for the first iPhone, ultimately ceding a lucrative market to competitors (Madrigal, 2013). Similarly, after CEO Brian Krzanich resigned in 2018, his successor, Bob Swan, prioritised stock buybacks and cost-cutting over R&D investment.
While these financial manoeuvres temporarily boosted stock prices, they drained funds that could have fuelled critical innovation. As a result, Intel lost its technological leadership to rivals like Advanced Micro Devices (AMD) and Taiwan Semiconductor Manufacturing Company Limited (TSMC), highlighting the risks of underinvesting in R&D for short-term financial gains. This reinforces the importance of maintaining a forward-looking approach to FCF allocation, ensuring that firms – especially those in high-tech industries – continue to invest in innovation rather than prioritising immediate shareholder returns at the expense of long-term competitiveness.
Effective coordination between R&D and marketing functions can create synergistic advantages that often drive organisational success (Griffin & Hauser, 1996). This type of integration may encompass areas such as facility planning, workforce movement, structural organisation, reward systems and managerial procedures. Joint initiatives between R&D and advertising further strengthen the positive impact on sales growth.
Our study expands prior research by further clarifying how R&D and advertising expenditures dynamically mediate the connection between FCF and sales. The dynamic mediation framework shows that investing FCF in R&D and advertising is beneficial for a firm’s sales performance. Notably, the impact of allocating FCF to R&D is found to be stronger in high-tech sectors than in other industries, highlighting a greater effect for such firms.
While increases in FCF are generally linked to greater sales, Brush et al. (2000) provide empirical support for the agency theory argument that in firms with abundant FCF and weak governance, managers may drive sales growth that lacks profitability. Strong governance is crucial, as it compels managers to invest FCF wisely.
Some useful practical recommendations are provided. Firstly, for firms with substantial changes in FCF, this study emphasises the importance of aligning resource allocation with strategic objectives. Specifically, as FCF increases, managers should allocate a proportionate share of the incremental resources to R&D and advertising. Doing so not only fosters innovation but also strengthens market presence, driving long-term growth and competitiveness. Secondly, industry type matters in resource allocation decisions. The dynamic mediating effect of R&D investments is more pronounced in high-tech industries. High-tech firms, characterised by technological volatility and innovation intensity, should be particularly attentive to the need for allocating increased FCF to R&D. Non-high-tech firms should consider a more balanced allocation between R&D and advertising to achieve better results. Thirdly, collaboration between R&D and advertising can lead to more innovative product development and more effective advertising strategies, creating synergistic effects that drive sales growth. Fostering an environment where these two functions work in tandem can yield substantial benefits. Fourthly, this study affirms that prioritising immediate returns should never undermine the pursuit of enduring value creation. While it may be tempting to channel FCF into immediate profit-generation activities, prudent investments in R&D and advertising can create enduring competitive advantages. Firms should avoid prioritising immediate financial returns at the expense of sustained investment in these essential areas. Lastly, given the dynamic nature of the relationships examined in this study, businesses should establish mechanisms for continuous monitoring and adaptation. Regularly assessing the effectiveness of resource allocation decisions and adjusting strategies in response to changes in FCF can help firms stay agile and competitive. By capitalising on the insights presented here, firms can make strategic decisions with greater precision and enhance their ability to sustain competitive advantages.
Limitations and future research
This research acknowledges certain limitations that highlight avenues for future investigation. Firstly, the temporal scope of the study is confined to the period from 2012 to 2019; further work is needed to determine if these results hold in other time frames. Secondly, the carryover effects of R&D and advertising are not taken into account. Thirdly, future research could expand beyond the binary high-tech vs. non-high-tech distinction. Use of more granular or alternative industry groupings (e.g. technology intensity, sectoral innovation levels or sub-industry clusters) might reveal additional or different moderation patterns, not only for R&D but also for advertising pathways. Fourthly, in addition to industry type (high-tech vs. non-high-tech), future research could examine other industry-level moderators that may shape the dynamic relationships among FCF, strategic investments and firm performance – such as industry growth rate, competitive intensity, technological dynamism, capital intensity, regulatory environment, environmental uncertainty, innovation or advertising intensity and industry life cycle stage. Examining these factors could provide deeper insights into the mechanisms through which the dynamic mediating effects identified in this study are moderated across different industry contexts. Fifthly, Roper and Hewitt-Dundas (2015) report that both R&D-driven and externally sourced knowledge flows yield positive impacts on innovative sales. Future studies could examine the dynamic mediating effect of external knowledge flows on the relationship between FCF change and sales change. Finally, the present analysis intentionally excludes the period affected by the COVID-19 pandemic in order to focus on dynamic patterns under relatively stable economic conditions. This decision was made to avoid confounding effects arising from extraordinary economic shocks and policy interventions present during the pandemic, which can substantially distort firm-level financial and strategic behaviours. However, we recognise that examining how the dynamic mediation pathways involving R&D and advertising expenditures may shift during the crisis is an important issue for understanding firm adaptation. Future research could address this limitation by extending the temporal window to include the pandemic years and comparing the mediating effects before and after this macroeconomic disruption. Such an extension would further illuminate how external shocks reshape resource allocation and performance dynamics.
Conclusion
This study contributes to the literature by elucidating how FCF change is related to sales change. It underscores the mediating roles of R&D and advertising expenditure changes, as well as the moderating effect of industry type on the mediation of R&D expenditure change. The resource-based and knowledge-based views have been empirically supported from a dynamic perspective in the context of strategic investments. The nuanced dynamic relationships provide valuable insights for firms aiming to strategically allocate financial resources to foster growth and competitiveness. Practical recommendations are provided accordingly.
Acknowledgements
The authors reported that they received funding from the Ministry of Science and Technology, which may be affected by the research reported in the enclosed publication. The author has disclosed those interests fully and has implemented an approved plan for managing any potential conflicts arising from their involvement. The terms of these funding arrangements have been reviewed and approved by the affiliated university in accordance with its policy on objectivity in research.
Competing interests
The authors reported that they received funding from the Ministry of Science and Technology, R.O.C., which may be affected by the research reported in this publication. The authors have disclosed those interests fully and declare that no competing interests exist.
CRediT authorship contribution
Jo-Han Cheng: Conceptualisation, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing. Cherng G. Ding: Conceptualisation, Formal analysis, Funding acquisition, Methodology, Project administration, Software, Supervision, Writing – review & editing. Yen-Wei Chang: Methodology, Validation, Visualisation, Writing – review & editing. Yuan-Yuan Chan: Data curation, Validation, Visualisation, Writing – review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Funding information
This work was supported by the Ministry of Science and Technology, Republic of China (grant numbers MOST 108-2410-H-009-049, MOST 109-2410-H-009-020).
Data availability
The SAS codes supporting the findings of this study are available from the corresponding author, Cherng G. Ding, upon reasonable request. Additionally, the data used in this study are subject to third-party restrictions. They are drawn from Compustat, U.S. Securities and Exchange Commission (SEC) filings, Thomson Reuters Institutional Holdings (13F), and the Institutional Brokers’ Estimate System (IBES). Compustat, 13F, and IBES data are accessible via Wharton Research Data Services (WRDS) under license. SEC filings are publicly available but must be accessed and used in accordance with SEC rules.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
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