Introduction
Capable top managers are increasingly vital for organizational performance in today’s rapidly changing business environment. Their capabilities enable organizations to effectively adapt and respond to a constantly evolving business landscape (Aguinis, Audretsch, Flammer, Meyer, Peng, & Teece, Reference Aguinis, Audretsch, Flammer, Meyer, Peng and Teece2022; Heubeck & Meckl, Reference Heubeck and Meckl2023). Managers are essential for incumbent firms as they accelerate innovation strategies and help navigate the evolving business landscape (Wallin, Pihlajamaa, & Malmelin, Reference Wallin, Pihlajamaa and Malmelin2022; Weill & Woerner, Reference Weill and Woerner2015).
Although the current competitive landscape necessitates strong management capabilities (Helfat & Martin, Reference Helfat and Martin2015b; Heubeck & Meckl, Reference Heubeck and Meckl2023) required to sense and seize opportunities and transform resources (Helfat et al., Reference Helfat, Finkelstein, Mitchell, Peteraf, Singh, Teece and Winter2007; Teece, Reference Teece2007), existing literature predominantly focuses on firm-level capabilities in the context of organizational change (e.g., Farzaneh, Wilden, Afshari, & Mehralian, Reference Farzaneh, Wilden, Afshari and Mehralian2022; Ferreira, Coelho, & Moutinho, Reference Ferreira, Coelho and Moutinho2020). Thus, there exists a scarcity of research examining the critical linkages between dynamic managerial capabilities (DMCs), innovation, and firm performance.
Building on Adner and Helfat’s (Reference Adner and Helfat2003) DMC theory, this study shifts the prevailing focus from a macrolevel perspective on organizational change to a microlevel lens, examining the intricate dynamics of DMCs at the individual manager level and their implications on critical organizational outcomes. While existing research generally supports the positive effects of DMCs on performance and innovation (Heubeck, Reference Heubeck2023a), it tends to focus separately on their direct benefits for firm performance or innovation (e.g., Guan, Deng, & Zhou, Reference Guan, Deng and Zhou2022; Heubeck & Meckl, Reference Heubeck and Meckl2022b; Tabares, Tavera, Álvarez Barrera, & Escobar-Sierra, Reference Tabares, Tavera, Álvarez Barrera and Escobar-Sierra2022). As a result, there is a lack of research exploring the indirect performance benefits of DMCs, which is significant as DMC theory builds on a two-staged notion, where DMCs impact performance outcomes through their intermediate effect on resource portfolio orchestration (Adner & Helfat, Reference Adner and Helfat2003; Helfat & Martin, Reference Helfat and Martin2015b).
Therefore, this study aims to comprehensively examine the mechanisms of resource orchestration through which DMCs translate into firm performance. Specifically, it will argue that research and development (R&D) investments represent a critical resource deployment decision for chief executive officers (CEOs) in rapidly changing environments. These decision-making processes are contingent upon the strength of managerial capabilities, and strong DMCs enable managers to navigate their organizations effectively in rapidly changing business environments (Adner & Helfat, Reference Adner and Helfat2003; Helfat & Martin, Reference Helfat and Martin2015b). Hence, the first research question is: How do DMCs influence firm performance, and to what extent do they have an impact?
This study further aims to extend DMC theory by integrating it with the concept of organizational slack, which has been extensively studied in the context of innovation and firm performance because it provides surplus resources that foster risk-taking and reduce immediate success pressures (Cyert & March, Reference Cyert and March1963; Nohria & Gulati, Reference Nohria and Gulati1996). These slack resources may enhance CEOs’ inclination to invest resources in innovation. Therefore, this study proposes that organizational slack moderates the indirect relationship between DMCs, innovation, and firm performance. Hence, the second research question is: How does organizational slack influence the relationship between DMCs, R&D intensity, and firm performance?
This study significantly contributes to the management literature. First, it advances DMC theory by providing new insights into the mechanisms through which DMCs influence firm performance, contributing to the growing body of microlevel studies on strategic change (e.g., Heubeck & Meckl, Reference Heubeck and Meckl2022b; Korherr, Kanbach, Kraus, & Mikalef, Reference Korherr, Kanbach, Kraus and Mikalef2022). Second, it highlights the specific management capabilities required to drive strategic change and firm performance in the context of a digitally transformed economy, adding to the research stream that examines management capabilities in today’s altered competitive reality (e.g., Heubeck, Reference Heubeck2023b; Warner & Wäger, Reference Warner and Wäger2019; Wrede, Velamuri, & Dauth, Reference Wrede, Velamuri and Dauth2020). Third, it offers a different perspective on DMC theory, predominantly influenced by an Anglo-American perspective (e.g., Adner & Helfat, Reference Adner and Helfat2003; Bendig, Wagner, Jung, & Nüesch, Reference Bendig, Wagner, Jung and Nüesch2022; Ener, Reference Ener2019), by testing its propositions on a sample of German DAX firms. These findings contribute to a more comprehensive understanding of DMCs within a broader international context. Fourth, it complements the causal mechanism between DMCs, R&D intensity, and firm performance by considering organizational slack as a crucial contingency factor, providing a comprehensive understanding of their interaction and impact. These insights advance DMC theory by uncovering the black box between DMCs and firm performance and identifying contingencies involved. This study is relevant for both researchers seeking to expand their knowledge and practitioners aiming to enhance strategic decision-making in a competitive environment.
The remainder of this article is structured as follows: The following section reviews the theoretical background, and the “Hypotheses development” section establishes the research hypotheses. The “Data collection, sample description, and research methodology” section provides an overview of the data collection methods, sample characteristics, and research methodology. Hypothesis test results are presented in the “Results” section, with robustness assessments in the “Supplemental analyses” section. The “Discussion and research implications” section concludes the article by discussing the findings, their theoretical and practical implications, and research limitations and recommendations.
Theoretical background
In contrast to firm-level oriented dynamic capability theory (e.g., Eisenhardt & Martin, Reference Eisenhardt and Martin2000; Teece, Pisano, & Shuen, Reference Teece, Pisano and Shuen1997), DMC theory adopts a microlevel viewpoint, asserting that managers’ dynamic capabilities are the driving force behind the development of value-creating organizational strategies (Adner & Helfat, Reference Adner and Helfat2003; Helfat & Martin, Reference Helfat and Martin2015b). DMC theory addresses the neglected aspect of agency in strategic decision-making (Aguinis et al., Reference Aguinis, Audretsch, Flammer, Meyer, Peng and Teece2022; Beck & Wiersema, Reference Beck and Wiersema2013).
According to DMC theory, top managers serve as the primary strategic architects within firms, responsible for managing the adaptability and effectiveness of organizational strategies in dynamic environments (Helfat & Martin, Reference Helfat and Martin2015b). Managers leverage their unique DMCs to orchestrate a firm’s resource portfolio and make strategic decisions based on their individual-level capabilities (Adner & Helfat, Reference Adner and Helfat2003; Beck & Wiersema, Reference Beck and Wiersema2013; Martin, Reference Martin2011). Therefore, DMCs are the root cause of competitive advantages, while the longevity of these advantages hinges on the strength of DMCs (Adner & Helfat, Reference Adner and Helfat2003; Heubeck & Meckl, Reference Heubeck and Meckl2022c). Figure 1 summarizes these causal mechanisms.
Figure 1. Firm-level effects of dynamic managerial capabilities, based on Beck and Wiersema (Reference Beck and Wiersema2013).
DMC theory identifies three key managerial resources (Adner & Helfat, Reference Adner and Helfat2003). The first is managerial human capital, encompassing managers’ knowledge, expertise, and skills (Beck & Wiersema, Reference Beck and Wiersema2013; Castanias & Helfat, Reference Castanias and Helfat2001). Two distinct types of human capital exist: general human capital, which comprises generic skills with high transferability acquired through general life and work experiences, and firm-specific human capital, which refers to highly specific skills with low transferability resulting from learning in a particular firm (Bailey & Helfat, Reference Bailey and Helfat2003; Becker, Reference Becker1983; Castanias & Helfat, Reference Castanias and Helfat2001). The unique configuration of managerial human capital plays a decisive role in strategic decision-making and contributes to the strategic disparities observed between firms, given the significant variations in the breadth and depth of managerial skills (Adner & Helfat, Reference Adner and Helfat2003; Beck & Wiersema, Reference Beck and Wiersema2013).
The second is managerial social capital, which refers to the relationships managers build over time through repeated interaction or shared experiences (Adler & Kwon, Reference Adler and Kwon2002). Social capital benefits managers by granting them power, control, and influence (Adler & Kwon, Reference Adler and Kwon2002; Blyler & Coff, Reference Blyler and Coff2003), providing resources and capabilities (Beck & Wiersema, Reference Beck and Wiersema2013; Helfat & Martin, Reference Helfat and Martin2015b), and facilitating learning (Kogut & Zander, Reference Kogut and Zander1992; Zander & Kogut, Reference Zander and Kogut1995).
The third is managerial cognition, which encompasses two cognitive mechanisms influencing strategic decision-making: cognitive processes and structures. Cognitive processes involve how managers gather, interpret, and store information, while cognitive structures are simplified mental representations of specific information environments (Colman, Reference Colman2015; Walsh, Reference Walsh1995). Managerial cognition is a complex resource with inherent ambiguity (Tripsas & Gavetti, Reference Tripsas and Gavetti2000; Walsh, Reference Walsh1995). Past experiences can serve as a ‘useful simplicity’ (Walsh, Reference Walsh1995, p. 306), facilitating decision-making processes and allowing managers to connect new information with existing knowledge (Durán & Aguado, Reference Durán and Aguado2022; Karhu & Ritala, Reference Karhu and Ritala2020). However, managerial cognition can also introduce limitations and biases in information search and interpretation (Tripsas & Gavetti, Reference Tripsas and Gavetti2000; Walsh, Reference Walsh1995).
Hypotheses development
This section introduces the research model depicted in Fig. 2, proposing that DMCs enhance firm performance by facilitating R&D spending. The first hypothesis builds on the notion that innovation is crucial for long-term competitiveness and growth (Caloghirou, Giotopoulos, Kontolaimou, & Tsakanikas, Reference Caloghirou, Giotopoulos, Kontolaimou and Tsakanikas2022; Sciascia, Nordqvist, Mazzola, & De Massis, Reference Sciascia, Nordqvist, Mazzola and De Massis2015) and asserts that CEOs with strong DMCs possess the necessary capabilities to implement innovative strategies in dynamic environments (Heubeck, Reference Heubeck2023a; Heubeck & Meckl, Reference Heubeck and Meckl2022c; Warner & Wäger, Reference Warner and Wäger2019).
Figure 2. Research model.
Organizational slack occupies a pivotal role in a firm’s resource portfolio, particularly concerning innovation initiatives. It plays a vital role in shaping the feasibility and sustainability of innovation projects while also influencing the capacity of managers to allocate resources for innovation. Organizational slack thus empowers managers with the means to access ample resources, thereby facilitating the realization of innovation projects and the continuous support of innovation-related investments (Nohria & Gulati, Reference Nohria and Gulati1996; Tabesh, Vera, & Keller, Reference Tabesh, Vera and Keller2019; Wang, Guo & Yin, Reference Wang, Guo and Yin2017).
DMC theory, with its emphasis on the role of managers in driving strategic change through resource allocation (Helfat & Martin, Reference Helfat and Martin2015b; Heubeck, Reference Heubeck2023a; Sirmon, Hitt, Ireland, & Gilbert, Reference Sirmon, Hitt, Ireland and Gilbert2011), intricately converges with the realm of organizational slack. DMCs encapsulate a manager’s proficiency in leveraging resources, orchestrating change, and steering the organization toward innovation and growth (Helfat et al., Reference Helfat, Finkelstein, Mitchell, Peteraf, Singh, Teece and Winter2007; Heubeck & Meckl, Reference Heubeck and Meckl2023; Sirmon & Hitt, Reference Sirmon and Hitt2009). Within this context, managers with strong DMCs excel in organizations enriched with substantial slack resources. This organizational setting provides these capable managers the latitude to channel resources toward innovation-centric initiatives without stringent resource constraints (Beck & Wiersema, Reference Beck and Wiersema2013; Sirmon & Hitt, Reference Sirmon and Hitt2009; Sirmon et al., Reference Sirmon, Hitt, Ireland and Gilbert2011).
This study posits that because organizational slack functions as a buffer, enabling experimentation, risk-taking, and strategic exploration (Daniel, Lohrke, Fornaciari, & Turner, Reference Daniel, Lohrke, Fornaciari and Turner2004; Nohria & Gulati, Reference Nohria and Gulati1996; Tabesh, Vera, & Keller, Reference Tabesh, Vera and Keller2019), and DMCs provide the essential skill set to harness these resources and direct them toward innovation-focused pursuits (Heubeck, Reference Heubeck2023a; Sirmon & Hitt, Reference Sirmon and Hitt2009; Sirmon et al., Reference Sirmon, Hitt, Ireland and Gilbert2011), slack resources can amplify the performance benefits of DMCs through increasing R&D spending.
Specifically, this study postulates that the three types of slack resources – available, recoverable, and potential – positively moderate the relationship between DMCs and R&D intensity, thereby enhancing firm performance. This proposition stems from the idea that the surplus resources offered by organizational slack complement the resource-leveraging capabilities contained in DMCs, creating a symbiotic relationship that nurtures innovation. This interplay not only triggers the initiation of innovation projects but also sustains innovation endeavors over time, ultimately contributing to organizational success.
In summary, this study seeks to advance DMC theory by empirically investigating how individual-level management capabilities influence firm performance. Additionally, it explores the role of slack resources as moderators, considering the influence of a firm’s resource endowment on how CEOs leverage their DMCs to drive firm performance.
DMCs, R&D intensity, and firm performance
Innovation is crucial for building and sustaining competitive advantage in today’s dynamic environment (Damanpour & Aravind, Reference Damanpour and Aravind2012; Schumpeter, Reference Schumpeter2006). However, innovation investments come with risks and short-term losses (Kline & Rosenberg, Reference Kline, Rosenberg and Rosenberg2009; Teece, Reference Teece2012). With the pervasive influence of digital technology, managers, particularly in incumbent firms, must allocate significant resources to R&D (Wallin, Pihlajamaa, & Malmelin, Reference Wallin, Pihlajamaa and Malmelin2022; Weill & Woerner, Reference Weill and Woerner2015).
Despite the importance of R&D investments for organizational survival, the underlying agency in these decisions has received limited attention in the literature (Heubeck & Meckl, Reference Heubeck and Meckl2022c; Korherr et al., Reference Korherr, Kanbach, Kraus and Mikalef2022; Wrede et al., Reference Wrede, Velamuri and Dauth2020). DMC theory provides a suitable perspective to examine whether management capabilities enable firms to pursue innovation in dynamic environments (Adner & Helfat, Reference Adner and Helfat2003; Helfat & Martin, Reference Helfat, Martin, Shalley, Hitt and Zhou2015a). Strong DMCs enable managers to identify opportunities and threats in a timely manner (sensing), capitalize on opportunities or respond to emerging threats (seizing), and modify the firm’s resource portfolio (reconfiguring) (Helfat et al., Reference Helfat, Finkelstein, Mitchell, Peteraf, Singh, Teece and Winter2007; Teece, Reference Teece2007). These capabilities ensure that managers make the right strategic choices at the right time, thus laying the foundation for firm performance (Sousa-Zomer, Neely, & Martinez, Reference Sousa-Zomer, Neely and Martinez2020; Teece, Reference Teece2014; Warner & Wäger, Reference Warner and Wäger2019).
The performance benefits of strong DMCs arise from the synergistic interactions among their subcomponents. The diverse and in-depth skill set of strong DMCs enhances opportunity and threat sensing (Bock, Opsahl, George, & Gann, Reference Bock, Opsahl, George and Gann2012; Tasheva & Nielsen, Reference Tasheva and Nielsen2022). Additionally, strong DMCs facilitate sensemaking through complementary information and perspectives (Alguezaui & Filieri, Reference Alguezaui and Filieri2010; Manev & Elenkov, Reference Manev and Elenkov2005) and improve information processing accuracy and speed (Helfat & Martin, Reference Helfat, Martin, Shalley, Hitt and Zhou2015a; Heubeck & Meckl, Reference Heubeck and Meckl2022a).
Strong DMC subcomponents contribute to effective opportunity seizing. Human capital enables proficient decision-making when exploring and exploiting commercial opportunities (Helfat & Martin, Reference Helfat, Martin, Shalley, Hitt and Zhou2015a; Hitt, Ireland, & Hoskisson, Reference Hitt, Ireland and Hoskisson2017). Social capital leverages executive power to access and mobilize external resources and capabilities that benefit opportunity seizing (Burt, Reference Burt2009; Helfat & Martin, Reference Helfat, Martin, Shalley, Hitt and Zhou2015a). Managerial cognition improves information processing, which is crucial for making appropriate investment choices (Durán & Aguado, Reference Durán and Aguado2022; Heubeck & Meckl, Reference Heubeck and Meckl2022a; Tripsas & Gavetti, Reference Tripsas and Gavetti2000).
The DMC subcomponents are critical for reconfiguring the resource portfolio. Superior human capital allows managers to efficiently modify a firm’s resource portfolio (Guo, Xi, Zhang, Zhao, & Tang, Reference Guo, Xi, Zhang, Zhao and Tang2013; Helfat & Martin, Reference Helfat, Martin, Shalley, Hitt and Zhou2015a). Strong social capital provides access to critical resources and capabilities (Beck & Wiersema, Reference Beck and Wiersema2013; Blyler & Coff, Reference Blyler and Coff2003), supporting resource reconfiguration and strategy execution (Fukuyama, Reference Fukuyama1996; Helfat & Martin, Reference Helfat and Martin2015b). Extensive cognitive skills continuously update mental representations of a firm’s asset portfolio, informing executive decision-making with accurate abstractions of its resource endowment (Heubeck & Meckl, Reference Heubeck and Meckl2022c; Tripsas & Gavetti, Reference Tripsas and Gavetti2000; Walsh, Reference Walsh1995).
Strong DMCs benefit from the synergistic interactions among their subcomponents (Helfat & Martin, Reference Helfat, Martin, Shalley, Hitt and Zhou2015a; Heubeck, Reference Heubeck2023b). Social ties leverage managers’ human capital, facilitating knowledge exchange and access to complementary skills (Adner & Helfat, Reference Adner and Helfat2003; Blyler & Coff, Reference Blyler and Coff2003). Cognitive capabilities enhance learning processes and interpretation of unfamiliar knowledge (Heubeck & Meckl, Reference Heubeck and Meckl2022a; Tripsas & Gavetti, Reference Tripsas and Gavetti2000; Walsh, Reference Walsh1995). Social capital and managerial cognition reinforce each other, influencing information interpretation and network influence (Adner & Helfat, Reference Adner and Helfat2003; Burt, Reference Burt2009; Krackhardt, Reference Krackhardt1990).
In conclusion, managers with strong DMCs possess an extensive skillset, comprehensive social network, and efficient cognitive abilities, fostering firms’ innovative capacities through high R&D investments. Strong DMCs form the foundation of continuous innovation, essential for superior firm performance, suggesting that strong DMCs enhance firm performance by facilitating R&D investments. More formally
Hypothesis 1: R&D intensity mediates the relationship between DMCs and firm performance. Specifically, strong DMCs indirectly enhance firm performance by increasing R&D intensity.
Moderation role of organizational slack
Adequate resource allocation to R&D is crucial for organizations to pursue innovation (Barker & Mueller, Reference Barker and Mueller2002; Geiger & Cashen, Reference Geiger and Cashen2002). Slack resources are vital in this process as they enable firms to explore new ideas and alleviate performance pressures associated with innovation projects. Slack resources refer to resources exceeding current operational demands, such as unused budgets, surplus inventory, or idle staff. They provide flexibility and resilience in dynamic environments (Daniel et al., Reference Daniel, Lohrke, Fornaciari and Turner2004; Marlin & Geiger, Reference Marlin and Geiger2015). Additionally, slack resources create a stable and supportive environment for innovation (Bourgeois, Reference Bourgeois1981; Nohria & Gulati, Reference Nohria and Gulati1996; Tabesh, Vera, & Keller, Reference Tabesh, Vera and Keller2019). Insufficient slack resources can impede innovation by hindering R&D investments or prioritizing efficiency over exploration (Wang et al., Reference Wang, Guo and Yin2017; Yasai-Ardekani, Reference Yasai-Ardekani1986).
Slack resources can be categorized based on location, deployability, and discretion (Geiger & Cashen, Reference Geiger and Cashen2002; Sharfman, Wolf, Chase, & Tansik, Reference Sharfman, Wolf, Chase and Tansik1988). Organizational slack encompasses three forms: available slack (highly discretionary, unabsorbed financial resources), recoverable slack (excess resources already absorbed within the organization structure that can be mobilized if needed), and potential slack (additional resources from the external environment). These slack types contribute to organizational adaptability, innovativeness, and promote risk-taking, creativity, and experimentation (Bourgeois & Singh, Reference Bourgeois and Singh1983; Marlin & Geiger, Reference Marlin and Geiger2015; Tan & Peng, Reference Tan and Peng2003).
Moderation effect of available slack
Available slack resources enhance the positive relationship between DMCs and R&D intensity by improving managers’ ability to sense and seize opportunities and reconfigure organizational resources. With access to available slack, skilled CEOs can strategically invest in R&D initiatives without being constrained by short-term financial concerns (Cyert & March, Reference Cyert and March1963; Woodman, Sawyer, & Griffin, Reference Woodman, Sawyer and Griffin1993), allowing them to allocate funds for exploring new ideas, experimenting with innovation, and undertaking long-term research (Ashwin, Krishnan & George, Reference Ashwin, Krishnan and George2016; Nohria & Gulati, Reference Nohria and Gulati1996; Wang et al., Reference Wang, Guo and Yin2017).
Additionally, the immediate availability of available slack facilitates timely decision-making and resource allocation. Managers can quickly mobilize these resources to capitalize on emerging opportunities or respond to market demands without facing delays or bureaucratic obstacles (Bradley, Shepherd & Wiklund, Reference Bradley, Shepherd and Wiklund2011; Woodman, Sawyer, & Griffin, Reference Woodman, Sawyer and Griffin1993). This agility in resource deployment enables firms to seize competitive advantages and adapt to changing market dynamics more effectively.
Available slack cultivates an innovative culture within the organization by reducing immediate success pressures and enabling managers to make long-term R&D investments (Audia & Greve, Reference Audia and Greve2006; Kim, Kim, & Lee, Reference Kim, Kim and Lee2008; Nohria & Gulati, Reference Nohria and Gulati1996). It is vital in facilitating R&D investments by providing managers with the necessary funds to take risks and explore opportunities in an innovative environment (Bradley et al., Reference Bradley, Shepherd and Wiklund2011; Marlin & Geiger, Reference Marlin and Geiger2015; Nohria & Gulati, Reference Nohria and Gulati1996).
The positive relationship between DMCs and firm performance is expected to be strengthened by available slack. Skilled CEOs can allocate more resources to R&D when they have access to available slack, which is a buffer against success pressures and allows for greater flexibility (Bourgeois & Singh, Reference Bourgeois and Singh1983; Cyert & March, Reference Cyert and March1963; Nohria & Gulati, Reference Nohria and Gulati1996). The high deployability and immediate availability of available slack enable managers to seize opportunities and foster an innovative learning culture (Ashwin et al., Reference Ashwin, Krishnan and George2016; Bentley & Kehoe, Reference Bentley and Kehoe2020; Geiger & Cashen, Reference Geiger and Cashen2002).
More precisely, the three subcomponents of DMCs serve as valuable resources for effectively utilizing available slack to foster innovation. First, harnessing a manager’s skillset, knowledge, creativity, adaptability, and collaborative proficiencies, human capital empowers managers to identify, strategize, and successfully execute innovative ideas (Guan, Deng, & Zhou, Reference Guan, Deng and Zhou2022; Guo et al., Reference Guo, Xi, Zhang, Zhao and Tang2013; Heubeck & Meckl, Reference Heubeck and Meckl2022c), optimizing the utilization of existing resources. Second, social capital can also play a pivotal role in capitalizing on available slack for innovation as it contributes to the effective utilization of available slack by fostering collaboration, facilitating knowledge exchange, enabling external partnerships, and cultivating an environment of trust and support (Alguezaui & Filieri, Reference Alguezaui and Filieri2010; Kogut & Zander, Reference Kogut and Zander1992; Manev & Elenkov, Reference Manev and Elenkov2005). The cultivation and sustenance of robust social connections, both internally and externally, can lead to the discovery of novel business prospects, the consolidation of resource pools, and the successful implementation of innovations (Beck & Wiersema, Reference Beck and Wiersema2013; Blyler & Coff, Reference Blyler and Coff2003; Helfat & Martin, Reference Helfat and Martin2015b). Third, managerial cognition holds the potential to enhance the exploitation of available slack for innovation significantly, bolstering managerial capabilities in recognizing opportunities, mitigating risks, making astute strategic choices, and flexibly adapting to dynamic environments (Helfat & Martin, Reference Helfat and Martin2015b; Heubeck & Meckl, Reference Heubeck and Meckl2022a; Tasheva & Nielsen, Reference Tasheva and Nielsen2022). This skill is imperative for effectively channeling available resources to drive innovative projects and initiatives. Consequently, an organization’s success in leveraging available slack for innovation is closely tied to the cognitive abilities of its managers and their capacity to envision, implement, and capitalize on innovation projects.
In summary, slack resources can significantly enhance managers’ dynamic capabilities by providing a reservoir of resources that can be strategically deployed to foster innovation, respond to changes, explore new opportunities, and facilitate learning. Due to their highly discretionary nature, available slack resources can leverage managers’ human capital, social capital, and cognition. This argumentation leads to the following hypothesis:
Hypothesis 2: Available slack positively moderates the indirect positive effect of DMCs on firm performance by amplifying the DMC–R&D intensity relationship.
Moderation effect of recoverable slack
Although not as readily deployable as available slack (Geiger & Cashen, Reference Geiger and Cashen2002), recoverable slack plays a significant role in facilitating innovation investments. It serves as a buffer against business volatility and allows firms to sustain their innovation efforts even during challenging market conditions (Bourgeois & Singh, Reference Bourgeois and Singh1983; Bradley et al., Reference Bradley, Shepherd and Wiklund2011; Godoy-Bejarano, Ruiz-Pava, & Téllez-Falla, Reference Godoy-Bejarano, Ruiz-Pava and Téllez-Falla2020; Greve, Reference Greve2003).
Allocating recoverable slack requires strategic decision-making with a long-term orientation, as there may be a time lag between the decision to reallocate recoverable slack and the availability of these resources. Nevertheless, the availability of recoverable slack in future periods functions as resource insurance, instilling long-term-oriented thinking in managers and making them more likely to allocate sufficient resources toward R&D (Chandler, Scott, Stodder, & Tworoger, Reference Chandler, Scott, Stodder and Tworoger2011; Lin, Cheng, & Liu, Reference Lin, Cheng and Liu2009; Wiersma, Reference Wiersma2017).
Mobilizing recoverable slack can be challenging due to resource embedding and interrelatedness (Mishina, Pollock, & Porac, Reference Mishina, Pollock and Porac2004). Strong DMCs are essential in effectively utilizing recoverable slack for innovation. They equip managers with the necessary skills to identify, reallocate, and leverage recoverable slack (Wang et al., Reference Wang, Guo and Yin2017). Human capital equips managers with the necessary skills to identify and mobilize recoverable slack toward innovation (Heubeck & Meckl, Reference Heubeck and Meckl2022c, Reference Heubeck and Meckl2022b). Through their social capital, they can obtain support from stakeholders and make more comprehensive decisions about mobilizing recoverable slack efficiently due to informational benefits (Blyler & Coff, Reference Blyler and Coff2003; Guo et al., Reference Guo, Xi, Zhang, Zhao and Tang2013; Heubeck & Meckl, Reference Heubeck and Meckl2022c). Leveraging cognition is also crucial for firm performance because cognitively skilled managers can also make more informed decisions about how to mobilize recoverable slack for innovation as they base their decision-making on an accurate abstraction of the firm’s resource portfolio (Beck & Wiersema, Reference Beck and Wiersema2013; Helfat & Martin, Reference Helfat and Martin2015b; Heubeck, Reference Heubeck2023a). Thus, managers’ human capital, social capital, and cognition collectively shape their ability to utilize recoverable slack for innovation. Their skills, relationships, and cognitive abilities influence the identification, allocation, and execution of innovative initiatives that utilize recoverable resources effectively while maintaining the stability of core operations.
Therefore, strong DMCs are vital in mobilizing recoverable slack for innovation within organizations. They empower managers to navigate the challenges associated with resource mobilization, ensuring that recoverable slack is utilized effectively for R&D. By leveraging their DMCs, managers can overcome the complexities from resource embedding and interrelatedness, thereby harnessing the full potential of recoverable slack to drive innovation and ultimately enhance firm performance. Consequently, strong DMCs allow managers to make informed decisions about the most effective utilization of recoverable slack for innovation. This argumentation leads to the following hypothesis:
Hypothesis 3: Recoverable slack positively moderates the indirect positive effect of DMCs on firm performance by amplifying the DMC–R&D intensity relationship.
Moderation effect of potential slack
While different from available and recoverable slack, potential slack shares characteristics that promote experimentation and innovation. Managers with access to potential slack are less concerned about short-term costs and potential failure in R&D investments (Geiger & Cashen, Reference Geiger and Cashen2002; Marlin & Geiger, Reference Marlin and Geiger2015), suggesting its facilitative role in R&D spending when managers possess the necessary capabilities.
Furthermore, potential slack is less likely to result in suboptimal investment behavior because managers face capital market pressures that encourage appropriate investment decision-making (Bourgeois & Singh, Reference Bourgeois and Singh1983; Geiger & Cashen, Reference Geiger and Cashen2002). Acquiring potential slack signals the firm’s commitment to innovation and long-term growth in the capital market (Chandler et al., Reference Chandler, Scott, Stodder and Tworoger2011; Geiger & Cashen, Reference Geiger and Cashen2002; Marlin & Geiger, Reference Marlin and Geiger2015). Actively seeking and acquiring external resources demonstrates the firm’s intention to invest in R&D, enhance reputation, attract financial support, and strengthen external partnerships (Bourgeois & Singh, Reference Bourgeois and Singh1983; Daniel et al., Reference Daniel, Lohrke, Fornaciari and Turner2004; Herold, Jayaraman, & Narayanaswamy, Reference Herold, Jayaraman and Narayanaswamy2006). This positive signaling effect fosters trust and confidence from stakeholders, facilitating innovation and improving firm performance.
When DMCs are strong, the presence of potential slack further amplifies their positive impact by offering additional resources for innovation initiatives, allowing managers to leverage their capabilities to a greater extent. Regarding the three subcomponents of DMCs, potential slack provides the resources necessary for skilled managers to invest in R&D. As managers harness their human capital, potential slack fuels their ability to drive innovation through informed decisions and creative problem-solving. Further, with potential slack available, managers can expand their networks, forging connections with external partners and experts who can contribute to innovative projects. Finally, potential slack complements managers’ cognitive abilities, such as strategic thinking and adaptability, by offering managers the opportunity to allocate cognitive resources strategically. Thus, potential slack allows skilled managers to deploy their DMCs to identify opportunities, analyze risks, and make insightful decisions that drive innovative initiatives.
To summarize, potential slack enhances the relationship between DMCs and firm performance by enabling financial flexibility, fostering a long-term perspective, and generating positive signaling effects. The interaction between potential slack and DMCs is crucial in how external resources and internal capabilities jointly impact firm outcomes. Potential slack provides the flexibility needed for managers to pursue innovation initiatives. These initiatives often require reallocating resources from existing operations, which potential slack can facilitate without disrupting core functions. Thus, potential slack can encourage managers to think beyond incremental changes and embrace more ambitious innovation endeavors. When potential slack exists, managers can harness their combined human capital, social capital, and cognitive abilities more effectively for innovation – this synergy between DMCs and potential slack leads to enhanced innovation outcomes that ultimately benefit firm performance.
Considering potential slack alongside DMCs, this hypothesis contributes to a comprehensive understanding of leveraging external resources and internal capabilities for innovation and superior performance. Based on these arguments, this study proposes that managers with strong DMCs are more likely to allocate greater resources to R&D when recoverable slack is available. More formally
Hypothesis 4: Potential slack positively moderates the indirect positive effect of DMCs on firm performance by amplifying the DMC–R&D intensity relationship.
Data collection, sample description, and research methodology
Data collection and sample description
The study used firms listed in the DAX 30 index between 2010 and 2019 to avoid potential survivorship bias (Brown, Goetzmann, Ibbotson, & Ross, Reference Brown, Goetzmann, Ibbotson and Ross1992). This time frame was chosen to exclude the Global Financial Crisis and the COVID-19 pandemic (Issah, Anwar, Clauss, & Kraus, Reference Issah, Anwar, Clauss and Kraus2023; Kraus et al., Reference Kraus, Clauss, Breier, Gast, Zardini and Tiberius2020).
The initial sample consisted of 42 firms from Thomson Reuter’s Refinitiv Eikon database. Missing data were manually collected from annual reports, company websites, online networks, and media outlets (Heubeck & Meckl, Reference Heubeck and Meckl2023; Seo, Lee, & Park, Reference Seo, Lee and Park2022). If a firm had multiple CEOs in a year, the CEO at the beginning of that year was selected.
The final sample includes 31 firms actively conducting R&D (exclusion of 9 firms from industries with no R&D spending) and explicitly reporting their R&D activities in their financial statements (exclusion of 2 firms with no R&D activities in the traditional sense) (Koh & Reeb, Reference Koh and Reeb2015). For more details on the sample composition by industry codes, see Table 1.
Table 1. Sample composition
Measurement of variables
Dependent variable
This study employed return on assets (ROA) to measure the dependent variable firm performance. ROA is a widely accepted accounting measure of performance, calculated by dividing a firm’s net operating profit by its total assets. This performance metric has been extensively utilized in the management literature (Adams, Bessant, & Phelps, Reference Adams, Bessant and Phelps2006; Richard, Devinney, Yip, & Johnson, Reference Richard, Devinney, Yip and Johnson2009).
To ensure the robustness of results, two additional measures of firm performance were considered. Return on equity (ROE) was used to capture the value generated for shareholders, representing the ratio of net profit to shareholder’s equity (Armour & Teece, Reference Armour and Teece1978; Richard et al., Reference Richard, Devinney, Yip and Johnson2009). Tobin’s q, defined as the ratio of asset market value to asset replacement costs (Daines, Reference Daines2001; Singhal, Fu, & Parkash, Reference Singhal, Fu and Parkash2016), was employed as a supplemental performance measure.
Independent variable
DMCs were operationalized by measuring their three subcomponents individually and then aggregating the Z-standardized measures to calculate the composite measure of DMCs.
Building on Castanias and Helfat’s (Reference Castanias and Helfat1991, Reference Castanias and Helfat2001) managerial rents model, which differentiates between a generic and a firm-specific dimension of managers’ human capital, the measure of managerial human capital consisted of two underlying dimensions: (1) generic human capital, assessed by categorizing CEOs’ age into four age intervals (Colombo & Grilli, Reference Colombo and Grilli2005; Horng, Lee, & Chen, Reference Horng, Lee and Chen2001); and (2) firm-specific human capital, measured in terms of years of tenure within the firm (Bailey & Helfat, Reference Bailey and Helfat2003; Tabesh, Vera, & Keller, Reference Tabesh, Vera and Keller2019).
Managerial social capital was operationalized by quantifying the number of active or previous corporate affiliations (Holzmayer & Schmidt, Reference Holzmayer and Schmidt2020).
Based on recent research, managerial cognition was assessed using two indicators (Heubeck & Meckl, Reference Heubeck and Meckl2022b, Reference Heubeck and Meckl2023). The first indicator was the field of education, coded as 0 for business-related degrees and 1 for STEM (science, technology, engineering, mathematics) degrees (Greven, Kruse, Vos, Strese, & Brettel, Reference Greven, Kruse, Vos, Strese and Brettel2022). The second indicator was the level of education, with bachelor’s, master’s, and doctorate degrees assigned values of 0, 1, and 2. This operationalization captures how educational background influences cognitive processes and biases in R&D investment decision-making (Daellenbach, McCarthy, & Schoenecker, Reference Daellenbach, McCarthy and Schoenecker1999; Rodenbach & Brettel, Reference Rodenbach and Brettel2012). Managers become increasingly attached to their cognitions with higher education levels (Geletkanycz & Black, Reference Geletkanycz and Black2001; Musteen, Barker, & Baeten, Reference Musteen, Barker and Baeten2006), while the field of education shapes the nature of their cognition. Education in STEM disciplines enhances managers’ receptiveness to long-term R&D investments (Cummings & Knott, Reference Cummings and Knott2018) and improves their ability to assess investment returns compared to their business-educated counterparts (Hayes & Abernathy, Reference Hayes and Abernathy1980). Therefore, managers’ cognitive processes and R&D investment choices vary based on differences in field and level of education (Marvel & Lumpkin, Reference Marvel and Lumpkin2007; Rodenbach & Brettel, Reference Rodenbach and Brettel2012), which remain relatively stable over time (Epstein & Pacini, Reference Epstein, Pacini, Chaiken and Trope1999; Marzi, Fakhar Manesh, Caputo, Pellegrini, & Vlačić, Reference Marzi, Fakhar Manesh, Caputo, Pellegrini and Vlačić2023).
Mediating variable
R&D intensity represents the financial investment dedicated to innovation projects and is calculated as yearly R&D spending divided by total sales (Adams, Bessant, & Phelps, Reference Adams, Bessant and Phelps2006). R&D intensity captures both the development of internal knowledge (Sciascia et al., Reference Sciascia, Nordqvist, Mazzola and De Massis2015) and the absorption of external knowledge (Cohen & Levinthal, Reference Cohen and Levinthal1989, Reference Cohen and Levinthal1990), signifying the proactive approach of CEOs in driving innovation by strategically allocating financial resources, as supported by research (e.g., Barker & Mueller, Reference Barker and Mueller2002; Kor, Reference Kor2006; Lim, Reference Lim2015).
Moderating variables
This study measured organizational slack across its three dimensions (Bourgeois & Singh, Reference Bourgeois and Singh1983; Geiger & Cashen, Reference Geiger and Cashen2002; Marlin & Geiger, Reference Marlin and Geiger2015). Available slack was assessed using three indicators: current ratio (total assets divided by total liabilities), quick ratio (sum of total cash, short-term investments, and accounts receivable divided by total liabilities), and working capital (difference between current assets and current liabilities divided by total sales). Recoverable slack was operationalized by examining the ratio of selling, general, and administrative expenses to total sales. Potential slack was calculated as the average of three ratios: total debt to total equity, total debt to total sales, and total debt to total assets.
Control variables
The research model included additional control variables to ensure the analysis’ robustness. At the managerial level, two variables were initially considered. CEO nationality, a dummy variable coded as 0 if the current CEO is German and 1 otherwise, aims to capture the influence of national culture on decision-making. CEO gender was included as a dummy variable, coded as 0 if the current CEO is male and 1 if female, to account for possible differences in risk-taking (Faccio, Marchica, & Mura, Reference Faccio, Marchica and Mura2016; Ho, Li, Tam, & Zhang, Reference Ho, Li, Tam and Zhang2015). Because all CEOs in the sample identified as male, CEO gender was excluded from further analysis.
Three control variables were incorporated to account for firm characteristics. To account for influences on organizational structure and culture, firm age, measured as the years since founding, and firm size, calculated as the logarithm of the total number of employees, were included (Audia & Greve, Reference Audia and Greve2006; Chandy & Tellis, Reference Chandy and Tellis2000). Industry dummies were introduced to capture possible variations between manufacturing (NAICS codes 21 and 31–33; coded as 0) and service industries (NAICS codes 22, 42, 44–45, 51, and 62; coded as 1) (Dalziel, Reference Dalziel2007).
At the governance level, four control variables were included. Board size, indicating the total number of directors on a firm’s board, was considered due to its possible impact on corporate governance effectiveness (Goodstein, Gautam, & Boeker, Reference Goodstein, Gautam and Boeker1994). Board independence, measured as the ratio of independent directors to board size, was included to assess its potential influence on board functioning (Hillman & Dalziel, Reference Hillman and Dalziel2003). Board meeting frequency, representing the number of board meetings in a financial year, was incorporated due to its influence on corporate governance efficacy (Conger, Finegold, & Lawler, Reference Conger, Finegold and Lawler1998; Lipton & Lorsch, Reference Lipton and Lorsch1992). Directorial tenure, measured as the average number of years directors have served on the board, was included to examine its effects on supervising executives (Hillman, Shropshire, Certo, Dalton, & Dalton, Reference Hillman, Shropshire, Certo, Dalton and Dalton2011) and support for organizational change (Golden & Zajac, Reference Golden and Zajac2001).
Year dummies were also included to account for variations in R&D spending and firm performance across different years.
Statistical procedure
Hierarchical regression analysis was performed using IBM SPSS Statistics 29.0. The PROCESS plugin (Hayes, Reference Hayes2018) with bootstrapping and heteroscedasticity-consistent inference HC4 (Cribari-Neto) was employed to evaluate the significance of mediation and moderated mediation effects.
Mediation was assessed based on Baron and Kenny’s (Reference Baron and Kenny1986) three conditions: (1) significant total effect of the independent variable on the dependent variable, (2) significant effect of the independent variable on the mediator, and (3) significant effect of the independent variable on the dependent variable in the full regression model. Recent research by Hayes (Reference Hayes2009) and Zhao, Lynch, and Chen (Reference Zhao, Lynch and Chen2010) suggests that mediation still be present if Conditions 2 and 3 are met, even if Condition is not. Thus, this study evaluated mediation effects based on the fulfillment of Conditions 2 and 3 and utilized the PROCESS plugin for effect size estimation and confidence intervals using bootstrap inference. Sobel’s test (Baron & Kenny, Reference Baron and Kenny1986; Sobel, Reference Sobel1982) was also used to validate the robustness of the mediation effects.
The PROCESS plugin was employed to test moderated mediation effects. By utilizing heteroscedasticity-consistent inference HC4 (Cribari-Neto) and mean-centered interaction terms, the moderated mediation effects of the a-path are calculated, and conditional mediation (CoMe) indices along with the respective confidence intervals are constructed using bootstrap inference. Significant moderated mediation effects were assessed through conditional effects analysis and indirect effects sizes at different moderator values ( – SD, Mean, + SD).
Results
Table 2 presents a summary of descriptive statistics and bivariate results. The average firm within the sample has an average age of 98.89 years and employs 119,353.13 employees. The sampled firms span seven industry sectors, with 70.97% operating in manufacturing and 29.03% in service industries (refer to Table 1 for details).
Table 2. Descriptive statistics and bivariate results
Notes: R&D = research and development, ROA = return on assets, SD = standard deviation,
*** p < 0.001,
** p < 0.01,
* p < 0.05; N = 239.
Table 3 presents the hierarchical regression analysis results, comprising four regression models. Models 1 and 2 focus on R&D intensity as the dependent variable. Model 1 includes control variables regressed on R&D intensity, while Model 2 incorporates the study variables. The study variables account for an additional 20.6% of the variance in R&D intensity.
Table 3. Hierarchical regression results
Notes: b = regression coefficient, β = standardized regression coefficient, DV = dependent variable, N = sample size, p = significance value, R 2 = coefficient of determination, SE = standard error, R&D = research and development, ROA = return on assets,
*** p < 0.001,
** p < 0.01,
* p < 0.05.
Models 3 and 4 examine firm performance (ROA) as the dependent variable. Model 3 considers control variables regressed on firm performance, while Model 4 includes the study variables. Including the study variables explains an additional 26.0% of the variance in firm performance.
To assess multicollinearity, variance inflation factors (VIF) were calculated for each variable and model. VIF values below 2.5 indicate no significant influence of multicollinearity (Johnston, Jones, & Manley, Reference Johnston, Jones and Manley2018). Correlation coefficients further confirm the absence of significant multicollinearity (Kennedy, Reference Kennedy2008).
The research model is constructed based on well-established theories and empirical evidence, minimizing endogeneity concerns (Wintoki, Linck, & Netter, Reference Wintoki, Linck and Netter2012). It is built on a priori theorizing, drawing from well-established theories and empirical evidence to guide the expected relationships between variables. According to Li, Ding, Hu, and Wan (Reference Li, Ding, Hu and Wan2021), the research is not threatened by dynamic endogeneity if the independent and dependent variables operate at different levels, remain time-invariant, or change slowly. In this study, DMCs are measured at the individual level, while firm performance is an organizational outcome, and DMCs demonstrate considerable consistency over time. Therefore, there is minimal risk of dynamic endogeneity from a theoretical standpoint. Furthermore, as suggested by Li et al. (Reference Li, Ding, Hu and Wan2021), regressing the independent variable on the lagged dependent variable yielded no significant effect. Thus, the research model exhibits no signs of endogeneity from both theoretical and empirical perspectives, allowing for a confident interpretation of the findings as causal relationships based on the robust theoretical logic of the research model.
The hypothesis test results, presented in Tables 3–5, are summarized in this section. Effect sizes are defined according to Cohen’s (Reference Cohen1988) criteria: weak effect (β > 0.02), moderate effect (β > 0.15), and strong effect (β > 0.35).
Hypothesis 1 posits that the relationship between DMCs and firm performance is mediated by R&D intensity. Model 2 fulfills Condition 2, with DMCs significantly and moderately positively affecting the mediator R&D intensity (b = 0.028, β = 0.135, p < 0.001). Additionally, Model 4 satisfies Condition 3, as R&D intensity has a highly significant and strong positive impact on firm performance (b = 29.213, β = 0.341, p < 0.001). Thus, Hypothesis 1 is supported. The effect sizes and confidence intervals of the mediation effect, calculated through bootstrapping inference (see Table 4), reveal a positive and significant indirect effect of DMCs on firm performance via R&D intensity (b = 0.820, 99% CI: [0.225, 1.556]). Sobel’s test further confirms Hypothesis 1 (b = 0.821, p = 0.003).
Table 4. Indirect effects: Bootstrapping regression and Sobel’s test results with robustness checks
Notes: DMC = dynamic managerial capability, b = regression coefficient, β = standardized regression coefficient, p = significance value, SE = standard error,
*** p < 0.001,
** p < 0.01, *p < 0.05; bootstrap inference for model coefficients with heteroscedasticity-consistent robust standard errors (HC4) and covariance matrix estimator, number of bootstrap samples = 5000, N = 239.
Table 5 presents the moderation effects of the three slack types on the DMC–R&D intensity–firm performance relationship, specifically focusing on the DMC–R&D path. Hypothesis 2 suggests that available slack positively moderates this relationship. The interaction between DMCs and available slack is positive and significant (b = 0.039, p < 0.001), leading to an 8.0% increase in explained variance. Bootstrapping inference reveals a positive and significant CoMe index (b = 1.226, 99% CI: [0.073, 2.465]). Therefore, Hypothesis 2 is supported.
Table 5. Moderated mediation effects
Notes: DMC = dynamic managerial capability, b = regression coefficient, CoMe = conditional mediation, P = significance value, SE = standard error,
*** p < 0.001,
** p < 0.01, *p < 0.05; bootstrap inference for model coefficients with heteroscedasticity-consistent robust standard errors (HC4) and covariance matrix estimator, mean centered interaction terms, number of bootstrap samples = 5000, N = 239.
Hypothesis 3 proposes that recoverable slack positively moderates the indirect relationship between DMCs and R&D intensity. The interaction term is significant and positive (b = 0.288, p < 0.001), resulting in a 16.4% increase in explained variance. The CoMe index is positive and significantly different from zero (b = 10.268, 99% CI: [4.048, 17.489]). Thus, Hypothesis 3 is supported.
However, Hypothesis 4, proposing that potential slack moderates the positive relationship between DMCs and R&D intensity, is rejected. The interaction term is insignificant and negative (b = – 0.022, p = 0.065), and the CoMe index is negative and not significantly different from zero due to the CI including zero (95% CI: [–1.549, 0.060]).
An overview of the hypothesis test results can be found in Table 6, and Fig. 3 illustrates the research model with unstandardized regression coefficients.
Figure 3. Research model with unstandardized regression coefficients.
Table 6. Summary of hypothesis results
Notes: DMC = dynamic managerial capability, R&D = research and development; supported if P < 0.05.
Supplemental analyses
To ensure the robustness of the findings, two alternative measures of firm performance were examined as dependent variables (see Table 4), considering the potential influence of the performance measure choice on the results.
First, the dependent variable ROA was replaced with the alternative performance measure ROE. The bootstrapping results show a positive coefficient (b = 0.931) that significantly differs from zero, but only at the 10% significance level (90% CI: [0.001, 0.113]). Sobel’s test does not confirm the significance of this indirect effect (b = 0.933, p = 0.121). Thus, the evidence regarding the indirect relationship between DMCs and firm performance (ROE) via R&D intensity is mixed. However, as bootstrapping produces more robust results than Sobel’s test (Preacher & Hayes, Reference Preacher and Hayes2004; Shrout & Bolger, Reference Shrout and Bolger2002), the evidence shows some support for Hypothesis 1 when using ROE as the performance measure.
Second, Tobin’s q was incorporated as an alternative measure of firm performance. The results support Hypothesis 1 using Tobin’s q as the performance measure. The bootstrapping analysis provides evidence for a positive effect (b = 0.931) that significantly differs from zero (99% CI: [0.092, 0.302]). Sobel’s test confirms the significance of this indirect effect (b = 0.619, p < 0.001). In conclusion, the supplementary analyses using alternative measures of firm performance provide additional support for the robustness of the results.
To examine significant moderated mediation effects in greater detail, indirect conditional effects of the focal predictor were calculated at different moderator values (see Appendix 1). The supplementary analysis also involved computing the conditional indirect effects (presented in Appendix 2). The findings reveal that the association between DMCs and R&D intensity is contingent upon the level of available and recoverable slack. The moderating effect of available slack on the DMC–R&D intensity relationship is positive and becomes more pronounced as the level of available slack increases. These results align with the observed indirect effect at different values of the moderator, indicating that available slack enhances the positive impact of DMC on firm performance through R&D intensity across all levels of the moderator.
Finally, conditional effects were examined for the second significant moderation effect. The additional analysis demonstrates that the effect size of the moderator, recoverable slack, also varies across different levels. No significant moderation effect is observed at low levels, whereas at medium and high levels, a positive and significant moderation effect is present, with its magnitude increasing as the level of recoverable slack rises. Consequently, higher levels of recoverable slack amplify the indirect relationship between DMCs, R&D intensity, and firm performance, particularly at medium and high levels of the moderator.
Discussion and research implications
Discussion
This study examined how DMCs contribute to firm performance through increased R&D spending in the context of hypercompetition. The role of slack resources as a moderator in the DMC–R&D intensity relationship was also explored. Based on longitudinal data from 31 German DAX firms, the study provides valuable insights into the importance of microlevel capabilities and resource availability for organizational success.
The findings robustly support the hypothesis that DMCs enhance firm performance by facilitating R&D investments. This suggests that DMCs alone do not directly impact firm performance, but they influence CEOs to allocate more resources to R&D. This enables firms to develop innovative products, services, technologies, and processes, leading to competitive advantage and improved overall performance (Beck & Wiersema, Reference Beck and Wiersema2013; Helfat & Martin, Reference Helfat and Martin2015b; Heubeck, Reference Heubeck2023b).
Furthermore, this study demonstrates that specific types of slack resources enhance the relationship between DMCs and R&D intensity, thereby strengthening the performance benefits of DMCs. Internal slack resources, such as financial reserves or excess capacity (Geiger & Cashen, Reference Geiger and Cashen2002; Tan & Peng, Reference Tan and Peng2003), were found to be more effective in facilitating R&D investments and leveraging the benefits of DMCs compared to external resources. However, potential slack, an external resource, did not have the same impact. These findings highlight the multidimensional nature of slack resources, indicating that different types have varying implications for organizational success, as suggested by previous research (Geiger & Cashen, Reference Geiger and Cashen2002; Marlin & Geiger, Reference Marlin and Geiger2015).
This study emphasizes the crucial role of recoverable slack as the primary moderator, followed by available slack. This finding suggests that recoverable slack, such as excess capacity or staff, is a buffer to maintain consistent R&D investments and absorb uncertainties without compromising their R&D activities (Daniel et al., Reference Daniel, Lohrke, Fornaciari and Turner2004; Marlin & Geiger, Reference Marlin and Geiger2015). Consequently, this study demonstrates that leveraging recoverable slack allows firms to sustain their investment in R&D, leading to improved performance. The analysis also indicates that the strength of the moderation effect of recoverable slack depends on its level. While it is insignificant at low levels, its positive effect becomes significant and more pronounced with higher levels of recoverable slack. Therefore, the study suggests that firms should aim to maintain high levels of recoverable slack to maximize its benefits for firm performance.
Furthermore, this study uncovers that available slack positively moderates the relationship between DMCs and R&D intensity. The strength of this effect increases when managers have more discretion in allocating available slack resources. While the moderation effect of available slack is smaller than recoverable slack, it still plays a beneficial role in facilitating R&D investments. The smaller effect of available slack suggests that while readily available resources are advantageous, their impact may be less pronounced than recoverable slack. This could be because converting available slack into active resources requires additional efforts, whereas recoverable slack is already embedded within the organization and easily redeployed. Nonetheless, the findings emphasize that available slack contributes positively to the relationship between DMCs and R&D intensity, highlighting the importance of considering all surplus resources in supporting R&D activities that drive firm performance.
Contrasting with previous research (e.g., Carnes, Xu, Sirmon, & Karadag, Reference Carnes, Xu, Sirmon and Karadag2019; Daniel et al., Reference Daniel, Lohrke, Fornaciari and Turner2004; Geiger & Cashen, Reference Geiger and Cashen2002), this study does not support the notion that potential slack leads to innovation or subsequent performance advantages. As an external form of slack, potential slack differs significantly from internal slack because it is not as readily accessible for managers, requiring the mobilization and internalization of external resources (Marlin & Geiger, Reference Marlin and Geiger2015). Mobilizing potential slack is time-consuming and involves complex decision-making processes (Wiersma, Reference Wiersma2017). Potential slack may be too inert in dynamic environments, where quick responses to emerging opportunities are crucial for innovation investments. Additionally, mobilizing potential slack, especially through debt financing, presents challenges and hesitancy due to the additional costs and risks associated with interest expenses. Firms may be cautious in allocating potential slack for uncertain R&D projects to balance the benefits against the costs.
Theoretical contributions
This study challenges and expands upon the conventional understanding of dynamic capabilities at the firm level (e.g., Eisenhardt & Martin, Reference Eisenhardt and Martin2000; Teece, Reference Teece2007; Teece, Pisano, & Shuen, Reference Teece, Pisano and Shuen1997) by adopting a microlevel lens on organizational change and firm performance (Adner & Helfat, Reference Adner and Helfat2003). It builds upon existing research on DMCs (e.g., Heubeck & Meckl, Reference Heubeck and Meckl2022c; Korherr et al., Reference Korherr, Kanbach, Kraus and Mikalef2022; Matarazzo, Penco, Profumo, & Quaglia, Reference Matarazzo, Penco, Profumo and Quaglia2021) but goes further by providing new evidence for the integral role of a manager’s entire portfolio of DMCs in driving firm performance through innovative strategies.
The findings highlight the significant differences in managerial capabilities in sensing and seizing opportunities, and reconfiguring the firm’s asset portfolio. These differences emphasize the crucial role of managers in organizational success, particularly in the current era of digital and global competition. Thus, this study transfers Teece’s (Reference Teece2007) notion of the microfoundations of dynamic capabilities to the context of DMCs, validating the idea that managerial capabilities have become increasingly vital for organizational success (Aguinis et al., Reference Aguinis, Audretsch, Flammer, Meyer, Peng and Teece2022; Wallin, Pihlajamaa, & Malmelin, Reference Wallin, Pihlajamaa and Malmelin2022; Weill & Woerner, Reference Weill and Woerner2015).
This study reinforces the notion that innovation has become the bedrock for firm performance (e.g., Appio, Frattini, Petruzzelli, & Neirotti, Reference Appio, Frattini, Petruzzelli and Neirotti2021; Martín-Peña, Díaz-Garrido, & Sánchez-López, Reference Martín-Peña, Díaz-Garrido and Sánchez-López2018; Verhoef et al., Reference Verhoef, Broekhuizen, Bart, Bhattacharya, Qi Dong, Fabian and Haenlein2021). As organizations face tough environments, innovating and adapting becomes paramount for sustaining competitive advantage and achieving superior performance. The findings underscore the significance of DMCs in fostering innovation and driving firm success, underscoring the importance of prioritizing and developing these capabilities in organizations.
This study contributes to DMC theory by empirically demonstrating that the performance benefits of strong DMCs are not direct but are mediated by their impact on R&D investments. This intermediate effect of DMCs reinforces the two-staged rationale of DMC theory, stating that managers influence firm performance by effectively allocating resources, and this ability is contingent upon the heterogeneity of DMCs (Adner & Helfat, Reference Adner and Helfat2003; Helfat & Martin, Reference Helfat and Martin2015b).
Further, this study highlights that strong DMCs enable firms to adapt strategically to changing market dynamics. It demonstrates that the benefits of DMCs are realized indirectly through their impact on R&D investments, which are crucial for innovation and long-term performance. These findings align with recent research on DMCs (Heubeck & Meckl, Reference Heubeck and Meckl2022a, Reference Heubeck and Meckl2022b) yet advance this research stream by robustly supporting the two-staged nature of DMC theory. This study consequently emphasizes the importance of considering the intermediate effect of DMCs on performance outcomes.
This study empirically demonstrates that the linkage between DMCs and firm performance is significantly influenced by a firm’s resource configuration, as proposed in DMC theory (e.g., Adner & Helfat, Reference Adner and Helfat2003; Beck & Wiersema, Reference Beck and Wiersema2013). Specifically, the findings highlight the role of organizational slack as a critical component of a firm’s resource portfolio for innovation. While previous research has touched upon this idea (e.g., Sirmon, Hitt, & Ireland, Reference Sirmon, Hitt and Ireland2007; Sirmon et al., Reference Sirmon, Hitt, Ireland and Gilbert2011), it has not been explicitly incorporated into the study of DMCs until now. Therefore, this study advances the understanding of DMCs by considering the configuration of a firm’s resource portfolio as a crucial factor in the DMCs–firm performance relationship. The findings emphasize the importance of top managers in effectively deploying slack resources for innovation (Ruiz‐Moreno, García‐Morales, & Llorens‐Montes, Reference Ruiz‐Moreno, García‐Morales and Llorens‐Montes2008; Sirmon et al., Reference Sirmon, Hitt and Ireland2007; Wiersma, Reference Wiersma2017) and the role of DMCs in efficiently utilizing these resources as catalysts for innovation (Adner & Helfat, Reference Adner and Helfat2003; Beck & Wiersema, Reference Beck and Wiersema2013).
This study expands the research on organizational slack by adopting a multidimensional perspective and considering the effects of different types of slack resources. Unlike previous studies focusing primarily on highly discretionary slack (e.g., Ashwin et al., Reference Ashwin, Krishnan and George2016; Bentley & Kehoe, Reference Bentley and Kehoe2020; Tabesh, Vera, & Keller, Reference Tabesh, Vera and Keller2019), this research takes a comprehensive approach to understanding slack resources. The findings highlight that internal slack resources (available and recoverable slack) play a crucial role in strengthening the indirect effect of DMCs on firm performance by amplifying the relationship between DMCs and R&D intensity. In contrast, external slack resources (potential slack) do not significantly influence these relationships. Therefore, the study underscores the multifaceted nature of slack resources and their varying effects at the firm level.
This study significantly contributes to the microfoundational strategic management literature by empirically examining the relationship between DMCs and firm performance (e.g., Aguinis et al., Reference Aguinis, Audretsch, Flammer, Meyer, Peng and Teece2022; Felin, Foss, Heimeriks, & Madsen, Reference Felin, Foss, Heimeriks and Madsen2012; Helfat & Martin, Reference Helfat and Martin2015b). The findings support the role of DMCs in enhancing performance but reveal that the impact is indirect, mediated by increased R&D spending. This study provides empirical evidence for the two-staged conceptualization of DMC theory (e.g., Helfat & Martin, Reference Helfat and Martin2015b; Heubeck, Reference Heubeck2023b; Tasheva & Nielsen, Reference Tasheva and Nielsen2022) and emphasizes the crucial role of CEOs in resource allocation for innovation. These findings reinforce the fundamental propositions of DMCs (e.g., Adner & Helfat, Reference Adner and Helfat2003; Martin, Reference Martin2011; Sirmon et al., Reference Sirmon, Hitt and Ireland2007), the importance of CEOs in sustaining competitive advantages and driving strategic change while acknowledging variations in their capabilities.
Managerial implications
This study has significant implications for managerial practice. First, it emphasizes the importance of CEOs with strong DMCs in driving organizational renewal and survival. Firms should carefully select highly skilled CEOs or consider replacing their current CEO with a more competent successor. Organizations must conduct comprehensive assessments and actively develop the DMCs of their CEOs.
Second, it highlights how DMCs influence organizational performance, emphasizing that strong DMCs do not directly lead to superior firm performance. Instead, they facilitate financial success by increasing R&D spending. This finding implies that DMCs are essential for organizational survival in dynamic environments, emphasizing the need to cultivate and leverage these capabilities. Further, this demonstrates that firms should recognize the importance of allocating resources toward R&D initiatives as part of their strategic efforts.
Third, this study emphasizes that firms should provide appropriate resources to highly skilled CEOs to enhance their innovative capacities and performance. Firms should grant skilled CEOs discretion over internal slack resources, enabling them to allocate more resources toward R&D activities. Examples from innovative companies like Alphabet and 3M (Chireka & Fakoya, Reference Chireka and Fakoya2017; age & Brin, Reference Page and Brin2004) demonstrate the accumulation of available resources and the integration of excess capacities, inspiring firms to adopt similar resource allocation strategies.
Fourth, while external slack does not directly impact R&D spending or firm performance, it is still vital for firms to have the ability to secure external funds when necessary. This may be particularly important for other investment types that do not face immediate time pressures, such as acquisitions.
In conclusion, this study offers practical insights for managerial practice. Firms should prioritize the appointment or development of CEOs with strong DMCs, comprehend the role of DMCs in driving performance through increased R&D spending, provide appropriate resources to amplify the benefits of DMCs, and ensure the capacity to secure external funds when required. By implementing these recommendations, organizations can enhance their competitive advantage, foster innovation, and improve performance in dynamic business environments.
Research limitations and recommendations
While this study has made several notable contributions to the existing literature and has important implications for managerial practice, certain limitations present opportunities for future research. To begin with, the scope of this study was limited to publicly listed firms. Subsequent investigations could expand upon the research model by applying it to datasets from privately held enterprises, particularly focusing on small- and medium-sized entities. This approach would yield insights into potential differences in the mechanisms and the moderating role of slack resources between larger and smaller firms.
Another limitation pertains to the sample of firms analyzed, which were exclusively German. This geographical constraint may restrict the generalizability of the findings. To enhance the external validity of the results, future research endeavors could replicate this study across diverse countries, including those classified as emerging markets.
Moreover, this study employed quantitative research methods. However, this approach may not have entirely captured the intricate interplay between DMCs, R&D intensity, organizational slack, and firm performance. Subsequent studies could employ qualitative techniques like surveys or case studies to gather subjective data. This qualitative exploration would enable a deeper comprehension of the intricate relationships at play.
Furthermore, this study relied on an innovation input-oriented metric, specifically R&D intensity. While consistent with a significant portion of the innovation literature (Adams, Bessant, & Phelps, Reference Adams, Bessant and Phelps2006) and grounded in the rationale of capturing CEOs’ proclivity for innovation (Hill & Snell, Reference Hill and Snell1988; Kor, Reference Kor2006), future research avenues could involve exploring output-oriented innovation measures. Examples include patents or new product development, which would provide insights into the tangible commercial outcomes of R&D investments.
Another aspect worth considering is that this study centered around the dynamic capabilities of individual managers without delving into the mechanisms through which these dynamic capabilities aggregate. Consequently, prospective research could delve into the aggregation of DMCs within top management teams and how this aggregation shapes strategic decision-making and overall firm performance.
Additionally, an aspect that remained unaddressed in this study is the potential influence of DMCs at lower management tiers. As middle-level management continues to be pivotal in strategy execution and organizational change (e.g., Greven et al., Reference Greven, Kruse, Vos, Strese and Brettel2022; King, Fowler, & Zeithaml, Reference King, Fowler and Zeithaml2001; Wilden, Lin, Hohberger, & Randhawa, Reference Wilden, Lin, Hohberger and Randhawa2022), a promising avenue for future investigation would be to explore whether middle managers’ DMCs also contribute to organizational success.
Lastly, considering the ever-evolving nature of today’s dynamic economic landscape, it would be advantageous to replicate this study at a later juncture to ascertain the robustness and consistency of the findings over time.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/jmo.2023.57.
Data Availability Statement
The datasets generated during and/or analyzed during the current study are available from the author upon reasonable request.
Financial Support
This research project did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conflicts of Interest
The author declares that he has no relevant financial or nonfinancial conflicts of interest to disclose. He certifies that he has no affiliations with or involvement in any organization or entity with any financial or nonfinancial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interest in any material discussed in this article.
Tim Heubeck is a researcher at the Chair of International Management, University of Bayreuth, Germany. He received his PhD from the University of Bayreuth. His main research areas include dynamic capabilities, corporate governance, and organizational change, mainly focusing on the managerial and organizational antecedents to innovation in the digital economy. He has published his work in several international high-impact journals, including the Review of Managerial Science, European Journal of Innovation Management, and Managerial and Decision Economics. Tim Heubeck can be contacted at: [email protected].
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