Marketing with (or without) research and development: strategic investment drivers of sustained firm performance

In highly competitive and rapidly evolving markets, firms must make critical decisions about how to allocate financial resources to sustain competitiveness and performance. Among these, marketing and research and development (R&D) investments are particularly relevant, as they are aimed, respectively, at driving demand and renewing offerings (Morgan, 2012; Capon, Farley, & Hoenig, 2012). While some firms pursue visibility and market penetration through branding, others focus on technical innovation for long-term differentiation (Tavassoli & Karlsson, 2015). A third strategy combines both to enhance market share and profitability (Davcik & Sharma, 2016; Drechsler, Natter, & Leeflang, 2013).

Nevertheless, these alternative paths raise a fundamental question that remains underexplored: Under what conditions does each resource strategy or their combination, truly enhance firm performance? Although balancing marketing and R&D is widely viewed as essential for sustaining competitive advantage (Davcik & Grigoriou, 2019; Maury, 2018), its effectiveness, conceived as a sustained performance pattern (Powell, 2001), still depends on contextual and temporal factors that remain poorly understood. Most analyses focus on immediate impacts, overlooking delayed effects and their dependence on prior performance (Chakravarty & Grewal, 2011; Chen, Chan, Hung, Hsiang, & Wu, 2016). Marketing and R&D frequently respond reactively to underperformance (Chen & Habibi, 2023; Srinivasan, Lilien, & Sridhar, 2011), meaning that ignoring prior performance obscures how firms convert resources into value under varying initial conditions (Porto & Foxall, 2019; 2020).

Marketing may yield fast but short-lived returns (Katsikeas, Morgan, Leonidou, & Hult, 2016), while R&D can produce innovations that fail to gain market traction (Grimpe, Sofka, Bhargava, & Chatterjee, 2017). Combined, these investments can create valuable synergies when aligned with financial history and strategic context (Chen et al., 2016), particularly because their effects often depend on performance trajectories and sometimes emerge with a delay (Chakravarty & Grewal, 2011; Chen et al., 2016; Porto & Foxall, 2019; 2020). Underperforming firms may therefore benefit most from bold, combined reallocations (Srinivasan et al., 2011; Chen & Habibi, 2023).

Despite this potential, empirical research still treats marketing and R&D as largely separate domains. Most studies focus exclusively on either marketing (Hughes, Hughes, Yan, & Sousa, 2019; Kumar, Sriram, Luo, & Chintagunta, 2011) or R&D (Lantz & Sahut, 2005) or limit joint analyses to new product development (Grimpe et al., 2017; Leenders & Wierenga, 2008), thus overlooking broader outcomes such as market share or profitability. Few examine their relative or interactive effects on key financial results (Chen et al., 2016; Katsikeas et al., 2016).

This study addresses these limitations by examining the individual and combined impacts of marketing and R&D on market share and profitability over time, while accounting for prior performance levels. Using a contextualized, dynamic lens, grounded in the Marketing Firm theory and informed by behavioral economic perspectives on resource allocation, this research refines our understanding of when and how financial resources are transformed into sustainable advantage.

The Marketing Firm theory (Foxall, 2021) is an operant behavioral-economic framework (Foxall, 2020; Foxall, Oliveira-Castro, & Porto, 2025) that explains why, how and when firms exist (Coase, 1937), linking organizational and consumer behavior through reciprocal reinforcement histories that shape how firms learn to allocate resources and how consumers respond. Firms operate through two paths (Foxall, Oliveira-Castro, & Porto, 2021): direct resource expenditures, which can yield losses if not used well and a mediated path through consumers, whose purchases generate surplus when resources meet demand efficiently.

Marketing and R&D allocations influence market share and profitability, depending on how consumer choices reinforce the firm’s actions over time. Marketing expenditures are reinforced when they boost sales and market share (Porto & Oliveira-Castro, 2015), while profits arise when revenues exceed costs (Porto & Foxall, 2019). Thus, firms act as learning systems shaped by prior performance and contextual stimuli, with marketing and innovation functioning as interdependent drivers.

Financial outcomes may appear immediately or be delayed, depending on the timing of reinforcements and punishments (Hantula, 2001; Jablonsky & DeVries, 1972; Porto & da Silva, 2013). Marketing often produces rapid earnings through consumers’ behavioral responses shaped by history and competitive context (Porto & Foxall, 2020), whereas R&D may take longer to generate returns. Because market share and return on Assets (ROA) reflect distinct behavioral-economic outcomes, both are treated separately in the empirical analysis.

Three complementary theoretical perspectives help explain how these investments affect performance over time and under different starting conditions. The logic of diminishing marginal returns (Boulding & Staelin, 1990) suggests that incremental performance gains weaken as resources accumulate, a pattern increasingly confirmed in digital contexts (Sklenarz, Edeling, Himme, & Wichmann, 2024). The composition-based view argues that firms with scarce resources can “catch up” by creatively recombining marketing and innovation (Luo & Child, 2015), making their joint use especially effective when launched from a disadvantaged position. Finally, the behavioral theory of the firm interprets resource allocation as a response to unsatisfactory outcomes (Cyert & March, 1963; Gavetti, Greve, Levinthal, & Ocasio, 2012), meaning that increases in marketing or R&D, alone or in combination, often reflect strategic responses to performance gaps.

Viewed through the lens of the marketing firm theory, these perspectives show that marketing and R&D investments, separately or together, produce asymmetric performance paths shaped by historical learning and competitive contexts. Marketing tends to enhance market share more quickly, while R&D strengthens long-run profitability and their synergy supports sustained trajectories when resources are strategically applied.

Defining appropriate marketing budgets is critical because these allocations require sustained effort and consumer understanding (Rubera, Ordanini, & Calantone, 2012). Despite incurring costs, branding, advertising and sales generate future returns by reinforcing purchase behavior (Chen, 2012; Porto & Foxall, 2019). R&D enhances innovation, efficiency and value creation, strengthening competitiveness (Chen et al., 2016; Pathak, Sen, Jayaram, & Miller, 2019). Firms allocating more to both resources must therefore explore and exploit opportunities simultaneously (Cowling, Liu, & Vorley, 2024; Rubera et al., 2012).

R&D investments support profitability by enabling sustainable advantages rooted in valuable, rare, inimitable and non-substitutable innovations (Barney, 1991; Saranga, George, Beine & Arnold, 2018). However, their financial impact is difficult to measure due to time lags between investment and returns (Ehie & Olibe, 2010). Although R&D creates value, its direct effects depend on the criteria used to evaluate performance, often obscuring causal inference (Bouaziz, 2016). Cumulative investments generally foster long-term profitability (Rubera & Tellis, 2014), with short-term losses and later gains (Karna, Mavrovitis, & Richter, 2022; Rađenović, Krstić, Janjić, & Vujatović, 2023) or immediate returns (Mubarok, Sultan, Wibowo, & Wongsuwatt, 2023). These inconsistencies may reflect the fact that short-term studies emphasize gross R&D expenditure, while long-term analyses focus on capitalized R&D assets measured relative to total assets rather than raw spending. Absolute spending and longitudinal evaluation often provide more unmistakable evidence of R&D’s contribution.

R&D can produce short- and long-term market-share growth (Uysal, 2021) by driving innovation, brand differentiation and consumer alignment (Sharma, Davcik, & Pillai, 2016). Innovations also reinforce utilitarian buying patterns through value-enhancing responses (Foxall et al., 2021). Accordingly, we propose the following hypotheses:

The impact of marketing investment on firm performance is well documented (Krasnikov & Jayachandran, 2008). High marketing spending creates competitive advantages by raising entry barriers, differentiating brands and stabilizing market share, thereby improving profitability (Maury, 2018). Porto and Foxall (2022) show that marketing has direct, positive and immediate effects on market share across countries, though these effects dissipate within a year. Because market share tends to display long-run stationarity (Dekimpe & Hanssens, 1995; Pauwels & Hanssens, 2007), marketing yields only temporary gains (Dekimpe & Hanssens, 1999) and faces diminishing returns (Boulding & Staelin, 1990; Hanssens, Wang, & Zhang, 2016; Sklenarz et al., 2024). Therefore:

Investing in R&D and marketing jointly enhances performance by combining technological innovation and market responsiveness, thereby supporting product development and sustained superior outcomes (Griffin & Hauser, 1996; Hughes et al., 2019; Powell, 2001; Vorhies, Orr, & Bush, 2011). This alignment is essential to the success of innovation (Stock & Reiferscheid, 2014). However, while individual effects are well established, the persistence of their joint impact remains uncertain. Koshksaray, Quach, Trinh, Keivani, and Thaichon (2023) show that marketing improves R&D outcomes by aligning them with consumer needs, whereas R&D strengthens marketing via distinctive products. Such complementarities may yield limited immediate effects on market share but contribute to profitability when reinforced over time (Huang, 2015; Krishnan, Tadepalli, & Park, 2009; Griffin & Hauser, 1996; Foxall, 1988, 2021). Thus:

Market share and profitability represent distinct dimensions of performance: the former signals competitiveness, while the latter reflects efficiency (Farris, Bendle, Pfeifer, & Reibstein, 2010; Morgan, 2012). A higher market share can enhance pricing power and profits (Bhattacharya, Morgan, & Rego, 2022), although these effects vary with timing (Edeling & Himme, 20182018). According to Maury (2018), sustained levels of market share and prior profitability foster profitability persistence, as both resources and outcomes, such as past performance, contribute to superior future results.

Assessing both market share and profitability over time reveals how financial history shapes the marginal value of marketing and R&D resources, signaling efficiency and market power (Bhattacharya et al., 2022). It also shows how allocations anchored in historical gains and losses influence performance outcomes (Wang & Lou, 2020; Porto & Foxall, 2019). Firms with weak performance are more likely to invest in marketing or combine it with innovation to overcome inefficiencies (Gavetti et al., 2012; Luo & Child, 2015; Porto & Foxall, 2020; Sharma et al., 2016). In contrast, high-profit firms sustain advantages through synergies and efficient allocations (Maury, 2018; Saranga et al., 2018), while those with high profitability but low market share tend to reinvest in marketing and R&D for future gains (Lee, 2014). Therefore:

These prior outcomes generate distinct resource-response patterns, leading to the following sub-hypotheses:

This study evaluates these hypotheses using two empirical models (Figure 1). The first model estimates the direct and moderating effects of marketing and R&D on market share and profitability (ROA) from t to t + 3, thereby addressing H1–H4. The second model tests H5a–H5d, incorporating prior performance as a moderator. This design captures how these resources influence performance over time.

The study used secondary data (2000–2017) from S&P Capital IQ, covering 1,030 publicly traded firms in the USA (91.3%) and the UK (8.7%). We estimated 24 models: 16 tested H1–H4 using market share and ROA from t to t + 3, with and without interactions and eight tested H5a–H5d on ROA at t + 1, segmented by prior performance. Firms operate in information technology (48.4%), healthcare (25.9%), industrials (8.4%), consumer discretionary (8.2%), staples (3.5%), materials (3.0%), energy (1.0%), financials (0.9%) and telecom (0.7%), with 177–6,989 observations per variable.

Unweighted marketing and R&D expenditures were used to avoid endogeneity with asset- or revenue-based dependent variables. Market share and ROA were retained in their original metrics and all continuous variables were log-transformed to estimate elasticities and reduce variance. Definitions and descriptive statistics are provided in Supplementary material Table S1 and correlations using raw and log-transformed variables are reported in Supplementary material Table S2 and Supplementary material Table S3. Log-transformed correlations were adopted to address nonlinearity in the original variables, thereby improving the linearity of multivariate estimation.

Control variables followed marketing–finance standards (Memon, Thurasamy, Ting, Cheah, & Chuah, 2024), including firm size, industry competitiveness, GDP growth and country, as well as time and sector dummies. These controls capture structural and contextual factors affecting both competitiveness (market share) and profitability (ROA). Panel diagnostics supported the use of generalized estimating equations (GEE), which estimate population-average effects and accommodate within-firm dependence through a working correlation with robust errors (Ziegler, 2011). All diagnostic tests (stationarity, autocorrelation, multicollinearity, Lagrange multiplier test, Hausman, White and variance inflation factor) are reported in Supplementary material (Diagnostics_S4). Using Quasi-likelihood under the independence model criterion for selection, an auto-regressive(1) structure minimized model error for H1–H4, whereas an unstructured correlation was optimal for H5a–H5d. The detailed criteria are provided in Supplementary material (ModelCriteria_S5).

The first models assess the lead effects of resource allocation at time t on market share and ROA from t + 1 to t + 3, with and without interaction terms, to determine whether effects persist or decay over time. The remaining eight models evaluate how prior performance influences the impact of current marketing and R&D on future profitability (t + 1). Prior performance was classified using z-scores of market share and ROA relative to industry-year means (z = 0), enabling quadrants that represent competitive achievement conditions and symmetric sample distribution (Davcik & Sharma, 2016). Figure 2 depicts these quadrants and equations (1)–(4) model the effects:

While (i,t + 1, t + 2, t + 3) indicates the performance outcome metric (market share or ROA) of firm i across time, equations (1) and (2) are repeated across years (t to t + 3). bR&DI(i,t) represents the R&D investment variables of firm i in time t. bMI(i,t) indicates the marketing investment variables of firm i in time t. bR&DI(i,t)∗MI(i,t) represents both the R&D and marketing investment variables together of firm i in time t. CVi,t indicates the control variables of firm i in time t. bMI(i,t) is the constant term and ri, t is the working residual. For equations (3) and (4), Y(i,t+1) represents the return-on-assets variable for firm i at time t + 1, given a cluster of prior performance outcomes. k indicates the parameter for a prior cluster’s performance.

The results are divided into three sections: the first examines marketing and R&D effects on market share across time (t, t + 1, t + 2, t + 3), testing H1 and H3; the second evaluates their impact on return on assets (ROA) over the same periods, testing H2 and H4; finally, the third assesses ROA in t + 1, considering prior performance achievements, testing H5a–d.

The results in Table 1 indicate that marketing and R&D investments have a positive impact on market share over time. The log of marketing investment consistently yields a positive and significant result (p ≤ 0.01), with a strong immediate effect at t (B = 0.27; p ≤ 0.01) that declines at t + 2 and t + 3 (B = 0.14; p ≤ 0.01), supporting H3. Likewise, the log of R&D investment remains positive and significant (p ≤ 0.01), with coefficients of 0.11 at t, 0.08 at t + 1, 0.12 at t + 2 and 0.14 at t + 3, indicating stable yet variable effects.

The Wald test shows a significant drop in the marketing effect from t to t + 2 (z = 3.06; p ≤ 0.05), followed by stability at t + 2 and t + 3 (z = 0.00; p > 0.05). R&D effects did not significantly increase over time, though they remained low, significant and stable (e.g. t + 1 to t + 3: z = −1.20; p > 0.05), supporting H1. The marketing–R&D interaction was non-significant, with near-zero coefficients and high errors, indicating no synergy on market share.

Other variables also show notable effects: total assets positively affect market share (B = 0.51), while industry competition negatively affects market share (B = −0.61). UK firms outperform US firms (B = 1.02) and the effect on GDP growth is not statistically significant. Model fit is strong (R²: 58.0%–60.9%) and QICC is much lower than that for null models (e.g. 31,318 vs 1,023,613 at t).

Figure 3 illustrates a significant positive effect of marketing investment on market share, which peaks at t and declines significantly by t + 2. R&D remains significant but shows slight variation over time.

Table 2 shows that marketing investments have no significant impact on ROA, with coefficients near zero across all coefficients. R&D investments are positive and significant from t (0.08; p ≤ 0.05) to t + 2 (0.11; p ≤ 0.01), peaking at t + 1 (0.13; p ≤ 0.01) and becoming non-significant at t + 3 (0.07; p > 0.05). The Wald test (z = −0.53; p > 0.05) confirms no significant variation, supporting H2.

The interaction between marketing and R&D is significant (p ≤ 0.01), with a stable coefficient of 0.02 across all periods, indicating a consistent synergistic effect on ROA. Wald tests show no significant change over time (z = 0.80; p > 0.05 from t + 1 to t + 3), supporting H4. Control variables reveal that total assets reduce ROA (e.g. B = −0.13 at t + 1; −0.15 at t + 2, p ≤ 0.01), indicating diminishing returns for larger firms. Country effects are minor and non-significant, while competition and GDP growth are mostly irrelevant, except for GDP at t + 1 (B = −0.02; p ≤ 0.10).

The models show moderate explanatory power (R²: 8.9%–11.1%). Lower QICC values – from 3,762 at t to 2,867 at t + 3 – indicate a better fit than null models. Findings underscore the importance of R&D and its synergy with marketing in enhancing ROA, while firm size and market context have nuanced effects.

Figure 4 shows that the overall joint effect of marketing and R&D investments on return on assets is low but significantly positive, despite fluctuations.

Table 3 models demonstrate high explanatory power (R²: 50.8%–72.2%) and an improved fit (lower QICC) compared to intercept-only models. While interaction models raise R², they reduce parsimony. Results confirm that marketing, R&D and their interaction differ in effectiveness across firms’ prior performance, supporting H5.

For firms with low prior market share and ROA, marketing investment significantly increases ROA at t + 1 (B = 0.48; p ≤ 0.01), whereas R&D investment has a negative effect (B = −0.46; p ≤ 0.01). In the presence of the interaction term, these effects persist and a significant synergy emerges (B = 0.05; p ≤ 0.01). For firms with low market share and high ROA, marketing (B = 0.11; p ≤ 0.10) and R&D (B = 0.09; p ≤ 0.10) exhibit modest positive effects; however, these effects lose significance when the variables interact. Still, the interaction is significant (B = 0.08; p ≤ 0.01), showing synergy.

Marketing investments moderately affect firms with high prior market share and low ROA (B = 0.23; p ≤ 0.05), while R&D and interaction terms are not significant. For firms with high market share and ROA, neither investment is significant in isolation, but their interaction indicates marginal synergy (B = 0.02; p ≤ 0.05), indicating modest gains from combined strategies.

The evidence confirms the impact of marketing and R&D investments on firm performance over time and across varying prior performance conditions. By validating the proposed hypotheses, the findings advance the literature and emphasize the strategic relevance of these investments for practice. Some results may seem expected, but they clarify how and when these investments yield returns.

The evidence for H1 supports a sustained, context-sensitive effect of R&D on market share, consistent with its role in innovation-driven competitive advantage (Rubera & Tellis, 2014). While prior studies (Karna et al., 2022; Ehie & Olibe, 2010) emphasize the long-term benefits, our findings indicate that R&D already contributes to market share in the current year. This reinforces the importance of aligning R&D with both short- and long-term strategic goals.

For profitability, H2 supports the notion that R&D investments consistently improve ROA over time, thereby addressing debates about their short-term versus long-term effects (Karna et al., 2022; Mubarok et al., 2023). By showing returns in both periods, the study reconciles opposing views and reinforces the value of innovation-driven strategies (Capon et al., 2012). This sustained effect emerged from using unweighted R&D data, indicating that the absolute investment volume itself reveals this prolonged impact.

The validation of H3 extends prior research by empirically supporting the transient impact of marketing on market share (Dekimpe & Hanssens, 1999; Hanssens et al., 2016). While marketing generates substantial initial gains, its influence tends to fade within a few years, underscoring the need for continuous reinvestment to sustain competitiveness, particularly for top-performing firms, where further gains become increasingly marginal.

Additionally, H4 supports the synergistic effect of combining marketing and R&D, demonstrating that their joint impact on profitability exceeds the sum of their isolated contributions. Though classified as operating expenses, these outlays should be seen as strategic investments in competitiveness and profitability. They provide key resources for building sustainable advantage (Davcik & Sharma, 2016) and long-term returns (Maury, 2018). This result complements earlier findings (Huang, 2015; Koshksaray et al., 2023; Krishnan et al., 2009) by showing that even modest synergy persists over time, reinforcing the value of integrating marketing and innovation-based strategies.

The analysis of prior achievements – combining profit efficiency (ROA) and market power (market share) – demonstrates how historical performance shapes the effectiveness of marketing and R&D, supporting the moderation hypothesis (H5). These results align with Foxall’s (2021) Marketing Firm theory, which asserts that a firm’s learning history guides resource allocation and outcomes. In this view, market share and profit efficiency reflect competitive advantage derived from aggregate consumer choice and influence future performance. The theory also links past resource expenditure to future behavior, framing marketing and R&D as consumer-oriented investments aimed at boosting future profitability and overall performance.

For firms with low profit efficiency and market power, results underscore the corrective role of marketing and its synergy with R&D. Marketing acts as an immediate tool to re-engage consumers and address competitive gaps. At the same time, R&D adds value by aligning innovation with demand. These findings extend those of Maury (2018) and Bhattacharya et al. (2022), demonstrating that even disadvantaged firms can enhance profitability persistence, thereby supporting the catch-up view (Luo & Child, 2015). They support H5a by demonstrating that marketing, either alone or in conjunction with R&D, significantly enhances future profitability in structurally weak firms.

Firms with high profit efficiency but low market power show a distinct pattern: financial surplus enables increased marketing and R&D investments, efficiently expanding profitability. These findings support H5b, as this group achieved the most significant profitability gains from combining both investments. This supports prior research (Katsikeas et al., 2016; Maury, 2018), emphasizing profit efficiency as a key condition for aligning innovation with consumer-oriented behavior (Foxall, 2021).

Firms with low profit efficiency but a high level of market power improved profitability only through marketing investments, with R&D and interaction effects showing no significance. This supports H5c, suggesting that even with operational inefficiencies, firms can convert structural advantages into financial gains via market-oriented strategies. This finding supports prior evidence that marketing alone can enhance profitability, particularly by leveraging market presence and responsiveness (Katsikeas et al., 2016), even in the absence of internal efficiency gains.

For firms with high profit efficiency and strong market power, results support H5d: neither marketing nor R&D alone impacts future profitability, whereas their combination has a modest but significant effect. This aligns with the notion of diminishing returns – firms with high strategic maturity may have already exhausted core resource benefits, with additional investments yielding limited value (Boulding & Staelin, 1990; Edeling & Himme, 2018; Bhattacharya et al., 2022). The finding refines marketing firm theory by showing that prior success moderates the marginal utility of further resource deployment (Foxall, 2021; Porto & Foxall, 2019).

Managers should tailor their marketing and R&D investments according to their firm’s strategic profile. Firms that invest heavily in marketing tend to see immediate gains in market share, but these effects typically fade after two years. Those investing primarily in R&D experience modest yet sustained improvements in profitability and competitiveness over three years. Firms that increase both simultaneously benefit from synergy effects that enhance long-term profitability.

Resource allocation should reflect prior profit efficiency and market power to generate further profitability. Firms with low levels in both dimensions benefit from marketing alone and its integration with R&D. Firms with high efficiency but low market power gain the most from synergy. In contrast, those with low efficiency and high market power should focus solely on marketing. Firms already performing well in both dimensions experience only marginal returns from synergies.

The key message is that the payoff of marketing and R&D depends on the firm’s existing strategic position. These differentiated patterns underscore the importance of aligning investment strategies not only with general principles of innovation and marketing but also with the firm’s performance history. Ultimately, understanding how firm history conditions the payoffs of strategic investments enables both scholars and practitioners to move beyond linear assumptions and embrace a more dynamic, context-sensitive view of sustained performance.

Although this study offers valuable insights, it presents limitations. The focus on US and UK firms may limit generalizability, suggesting the need for broader samples in future research. While GEE captures population-level effects, stationary time-series models may be better suited for assessing temporal stability. The 2000–2017 data set may not accurately reflect recent advances in digital marketing and innovation, underscoring the value of more recent data. Future studies should also investigate how industry context, firm size and regulatory environments influence marketing – R&D synergies, particularly in emerging markets, where instability and resource scarcity present unique challenges to strategic allocation and performance outcomes.

The supplementary material for this article can be found online.