How strategic leadership can impact sustainable innovation? Exploring the mediating effect of risk mitigation and resource orchestration Open Access

Strategic leadership is increasingly getting recognized as a core driver of sustainable innovation, particularly in disruptive environments where public-funded organizations are mandated to simultaneously pursue competitive advantage and societal impact. Sustainable innovation enables such organizations to strategically differentiate themselves and command a premium by embedding environmental implications and social impact while deploying their innovative products, processes, and business models (Singh and Jha, 2024; van Zijl and Koster, 2024). For instance, Tata Motors' multi-billion-dollar investments in electric vehicles and charging infrastructure reflect their strategic commitment to clean mobility and long-horizon market positioning in India's passenger vehicle sector (Jaiswal et al., 2022; Talebian et al., 2025). In the long run, such capabilities to consistently commit, create and integrate sustainable innovations in existing products, processes or business models can ensure long-term competitive advantage (Esau et al., 2025; Motloung and Lew, 2023). However, building and sustaining these capabilities in public-funded organizations operating in resource-constrained ecosystems such as India can be challenging due to funding volatility, infrastructural gaps, and institutional constraints (Abdul Basit et al., 2022; Tikas, 2023a). Notwithstanding these constraints, the emergence of several technology-intensive unicorns from India underscores their strategic leadership to develop and leverage sustainable innovation capabilities for rapid scaling and business growth (Bocken and Ritala, 2022; Jin and Wang, 2023). However, scaling innovation-driven growth within disruptive environments requires strategic leadership that continuously balances opportunity exploration and “sustainable exploitation” to ensure consistent transformation of value-driven innovation into societal impact (Arraya, 2022; Giang and Dung, 2021). Hence, strategic leadership becomes critical for sustainable innovation ventures in disruptive environments.

Strategic leadership capabilities—such as articulating a compelling long-term vision, orchestrating critical resources, and aligning stakeholders around shared priorities—are central to sustainable innovation within disruptive environments (Cortes and Herrmann, 2021; Esau et al., 2025). Prior research highlights several attributes of strategic leadership that drive sustainable innovation performance. Some studies emphasize leaders' roles in envisioning the sustainable future, anticipating technological and market shifts, and formulating strategies that unlock new value-creation opportunities (Jia et al., 2022; Tikas, 2024a). Others underscore how leadership styles that promote strategic flexibility and adaptive resource deployment enable organizations to reconfigure their internal structures and organizational processes to capture emerging opportunities in dynamic environments (Kurniawanti et al., 2023). Some even highlight the importance of leaders in fostering organizational cultures that support continuous experimentation, creativity, and innovation as drivers of competitive advantage (Abdelwahed et al., 2025; Dahlin and Sumsion, 2023). Despite these insights, the role of strategic leadership in sustaining innovation performance—particularly in complex, deep-tech and publicly funded R&D settings—remains underexplored and deserves deeper investigation within emerging ecosystems.

Sustainable innovation continues to be understudied with respect to the latent actors, mechanisms, and boundary conditions that shape how strategic leadership translates into innovation performance within dynamic environments (Singh and Jha, 2024). The knowledge gaps are particularly pronounced for government-funded R&D organizations in emerging economies such as India that aspire to pursue deep-tech innovation. For such organizations, commercialization of early-stage research from lab to market entails navigating technical uncertainty, long development cycles, and multi-stakeholder coordination under stringent resource constraints (Marx and Hsu, 2022; Tikas, 2024b). Existing literature offers limited insight into how such teams mitigate business risks associated with technological failure, regulatory hurdles, and market adoption during early commercialization phases, or how they orchestrate innovation-critical resources that are expensive, difficult to mobilize, and demand specialized expertise (Liao and Tsai, 2019; Sinha et al., 2023). For instance, government-funded R&D teams working on developing life-saving therapeutics must secure and operate high-end scientific equipment, interpret complex data patterns, and partner with pharmaceutical manufacturers to scale production and distribution (Fini et al., 2020). Universities in emerging economies, however, often lack the necessary financial and human capital to support such deep-tech innovation projects (Siegel and Guerrero, 2021). Compounding these challenges, deep-tech innovations typically face uncertain technological trajectories, ambiguous market readiness, and unpredictable customer willingness to adopt novel solutions (Marx and Hsu, 2022) increasing the number of “unknowns” that teams must manage before any returns materialize.

These commercialization complexities, technological uncertainties, and market ambiguities can reduce the attractiveness of deep-tech projects and dissuade potential entrepreneurs from pursuing them (Ding et al., 2023). Nevertheless, some government-funded R&D teams continue to pursue deep-tech innovations, motivated by ambitions to advance scientific frontiers, create societal impact, and contribute to sustainable development goals (Fini et al., 2023). However, prior research does not comprehensively explain why these R&D teams embark on such a demanding journey from academic research to marketable innovation and what enables them to sustain the challenges throughout. Their resolve, emotional resilience, and eclectic resourcefulness reflect a presence of strategic leadership capabilities that might have helped them navigate this transformation (Johnson et al., 2023). To uncover such unknowns and understand their interrelationships, this study raises three fundamental research questions:

This study aims to answer these questions through a theoretically grounded and empirically rigorous investigation of government-funded deep-tech R&D teams in India. In doing so, it advances the innovation literature by theorizing and testing the mediating roles of risk mitigation and resource orchestration in the relationship between strategic leadership capabilities and sustainable innovation performance, thereby extending the attention-based view of the firm to a context characterized by high uncertainty and public funding constraints. Eventually, the findings explicate how strategic leadership enable teams to routinely balance risk mitigation with resource orchestration to deliver sustainable innovation performance.

Furthermore, the study also contributes to the literature on strategic leadership, the attention-based view, and sustainable innovation, while offering actionable insights for policy makers, top management teams, and R&D leaders in government-funded academic settings. The remainder of the paper is structured as follows. The next section develops the theoretical background by underpinning the attention-based view (ABV) to integrate constructs like strategic leadership, risk mitigation, and resource orchestration, and formulating the hypotheses that link them. The subsequent section details the research design and methodology, including the empirical context, sampling, measures, and analytical approach adopted to get the empirical results. After presenting the empirical results, the article highlights the theoretical and managerial implications of those findings. The final section outlines the study's limitations and proposes avenues for future research.

Strategic leadership plays a critical role in directing organizational attention towards innovation and sustaining it over time to gain a competitive advantage. This demands a multi-faceted approach towards innovation, which combines a shared vision, dedicated resource allocation and empowering support from organizational stakeholders (Abdelwahed et al., 2025; Cortes and Herrmann, 2021). An integrated approach towards the adoption of such innovation-friendly practices helps organizational leaders align their managerial attention towards long-term innovation and leverage it to drive future growth of the organization. This approach towards driving long-term growth of organizations through sustained attention towards innovation finds its underpinnings in the attention-based view (ABV) of the firm (Quansah et al., 2022; Tikas, 2024a). The ABV provides theoretical insights into how organizations structure their attentional mechanisms towards their operating environments and shape their decision-making processes through unique stimulus-response combinations. It explains these managerial attentional mechanisms into three distributed sub-systems: attentional perspective, attentional selection and attentional engagement (Ocasio, 2011; Ocasio and Joseph, 2018). Attentional perspective provides a top-down cognitive approach to gaining awareness about environmental stimulus-response combinations, whereas attentional engagement helps in sense-making, resolving conflicts and proceeding towards decision-making (Joseph et al., 2024; Rerup, 2009). Attentional selection provides a mechanism to generate strategic alternatives which can help leaders make informed decisions under uncertainty (Kudesia and Lang, 2023). Collectively, the attention-based view helps organizational decision-makers interpret the ambient stimulus-response combinations and make informed decisions about their strategic alternatives.

However, the attention-based view fails to incorporate innovation contextualization and does not comprehensively explain innovation-oriented strategic decision-making (Pan Fagerlin and Wang, 2021). It does not sufficiently explain how decision-makers can perceive the long-term implications of envisioning innovation outcomes and aligning their short-term decisions accordingly (Xu et al., 2020). It also does not incorporate the attentional mechanisms that can help managers balance their internal and external awareness about innovation-centric developments. Lastly, it falls short of incorporating mechanisms that can help innovation teams collectively focus their attention on understanding stakeholder problems and resolving them through innovative approaches for organizational benefit (Bocken and Ritala, 2022). Moreover, it does not inform organizational leaders about building the necessary attentional capabilities that can help them in their endeavours to continuously identify new opportunities for innovation and develop innovations to gain a competitive advantage (Tikas, 2023b). Overall, the attention-based view emerges as an important theoretical lens to analyze innovation-oriented decision-making and deserves further investigation.

The attention-based view is cognitively connected with strategic leadership through the pivotal role played by the allocation of managerial attention in influencing organizational decision-making and strategic choice formulation (Pan Fagerlin and Wang, 2021). The ABV essentially suggests that the locus of managerial attention can shape an organization's strategic priorities and responses to stimuli from its operating environment. It emphasizes the cognitive boundaries of decision-makers and accentuates the importance of identifying leadership focal points (or anchors) while determining the organization's strategic direction (Ocasio et al., 2018). In doing so, it highlights the role of “managerial attention” as a critical resource that can drive organizational growth and superior performance through their decision-making authority (Joseph and Wilson, 2018). By incorporating innovation in their attentional mechanisms, leaders can create ecosystems that can inspire innovation within their organizations and navigate them through dynamic environments (Zhang et al., 2022). To sustain such attention, strategic leadership can create a compelling vision for the collective future of the organization and seek buy-in from all the major stakeholders. Such a shared vision can help them create a culture that encourages innovative thinking and risk-taking while neutralizing the threats inherent in the ambient environment (Liu et al., 2023). Such an innovation-inspiring culture can positively influence the leaders to allocate more resources for pursuing advanced research and deep-tech innovation that can empower the organization to enter new markets or uncharted territories. Additionally, such an innovation-positive culture backed by resource allocation helps organizations to sense latent opportunities, seize them through innovations and reconfigure themselves to capture value from those innovations (Lee, 2021). Such sensing, seizing and reconfiguring requires dedicated attention from strategic leaders throughout the innovation process. However, the extant literature on ABV does not provide a comprehensive understanding of how strategic leadership can influence sustainable innovation in dynamic environments which are marked with extreme uncertainty and unpredictability (Ocasio et al., 2018). Hence, it becomes important to investigate the following hypothesis:

Managerial attention to innovation can also help organizations mitigate potential risks that may impact the future growth of the organization. By paying attention to their proximal environments, participation in industry exhibitions and continuous engagement with target customers, organizations can gain awareness about the potential threats to their existing value propositions, shifts in consumer preferences and potential conflicts with collaborators that can create bottlenecks within the innovation value chain (Giaccone and Magnusson, 2022). By continuously monitoring these linkages between the focal firms and their key stakeholders, organizations can improve their capabilities to identify potential risks, prioritize the most appropriate, and allocate resources to mitigate them accordingly (da Silva Etges and Cortimiglia, 2019). Managerial attention plays a key role in identifying various risks that can scuttle the innovation process and, hence, deserves to be continuously monitored through dedicated attentional mechanisms (Lorentz et al., 2021). Subsequently, additional attentional structures are required to categorize and prioritize high-probability risks, which might create huge negative impacts and hence, deserve urgent attention from top decision-makers (Hock-Doepgen et al., 2021). Lastly, attentional engagement involving several stakeholders can help organizations in mitigating such high-impact risks, which demand a collective commitment of attentional resources along with dynamic capabilities to sense, seize and transform their innovation risks into rewards for organizational growth. Through engaging multiple internal stakeholders like employees, managers and executives with external stakeholders like investors, customers and regulators, innovation organizations can improve the quality of their innovations by neutralizing their high-impact risks along with maintaining trust and transparency. Such collective attention to innovation can make it easier for ventures to raise funding and aggregate critical resources from a diversified pool of stakeholders, leading to improved innovation performance (Shaikh and Randhawa, 2022). Thus, the attention-based view can help innovation organizations mitigate their innovation-oriented risks through the identification, prioritization and allocation of critical resources. This helps in proposing the following hypothesis:

Resource orchestration can help innovation leaders gain superior organizational performance through structuring, bundling and leveraging innovation-critical resources. It can help them in optimizing their innovation-critical resources to gain superior innovation performance and competitive advantage (Jia et al., 2022). However, mere possession of such innovation-critical resources is not sufficient to derive a competitive advantage; innovation organizations need to develop technical expertise in efficiently utilizing these resources to sustain innovation superiority (Cui et al., 2022). Developing such technical expertise in resource utilization demands a dedicated allocation of attentional resources along with structuring, bundling and leveraging of physical resources to provide a differential advantage. Hence, integrating resource orchestration with the attention-based view can provide a comprehensive understanding of how organizations can effectively leverage their resource combinations and sustain their attentional focus over time to achieve their strategic goals and objectives (Andersén and Ljungkvist, 2021). The ABV underscores the importance of attentional structuring to guide managerial decisions regarding the adaptation of resource combinations as per the changes in the dynamic world. It further guides the leaders in prioritizing their cognitive resources to a limited number of innovation tasks and prepares them to dynamically switch their attention among a finite set of tasks based on internal demands and external pressures (Jia et al., 2022). Such dynamic allocation of attention requires absolute clarity and conviction about which resources are to be prioritized for achieving strategic goals and which ones are to be allocated to achieve tactical ones. Managerial attention plays a critical role in executing such attentional shifts, executing them with precision and aligning them with the strategic objectives (Sirmon et al., 2011; Wang et al., 2020). Thus, ABV plays a critical role in guiding managerial attention and resource allocation towards innovation under uncertainty. This narrative also helps in proposing the third hypothesis:

Collectively, these three hypotheses extend the literature by conceptually and empirically integrating the attention-based view with strategic leadership to explain how managerial attention is channelled toward sustainable innovation in government-funded, deep-tech R&D teams. By modelling risk mitigation and resource orchestration as dual mediating mechanisms, the study moves beyond prior research that has typically treated these constructs in isolation, thereby offering a more fine-grained account of how leadership-driven attentional processes are translated into innovation outcomes. Moreover, by situating the analysis in government-funded Indian R&D teams operating in deep-tech domains, the study introduces a novel empirical context that contrasts with the predominantly private-sector or generic innovation settings examined in earlier work, enriching the external validity and robustness of the attention-based view and strategic leadership theories (See Table 1).

Literature review, along with expert opinion, helped in developing the conceptual framework (Figure 1), which depicts the direct effects of strategic leadership capabilities on team innovation performance, along with indirect effects flowing through two mediators – risk mitigation and resource orchestration. To test this conceptual framework within empirical settings, pre-existing scales for all four constructs were iteratively deployed in this multi-study research design (Figure 2). The entire data collection process was structured in three phases, where the first phase included pilot testing these scales with small (N1 = 120) respondents and subsequently moving to the validation phase through a large sample (N2 = 360); followed by the replication phase involving a large sample (N3 = 324) respondents. Within each phase, which roughly lasted for 2–3 months, a week-long time gap was introduced after circulating a scale-based questionnaire. Sequentially, the strategic leadership scale was circulated among respondents to seek their input, followed by the scales on mediating variables – risk mitigation and resource orchestration. To avoid common method bias, the fourth scale on team innovation performance was only circulated among the team leaders of those teams who participated in the previous three scale-based data collection (Podsakoff et al., 2012). This ensured that the data collected for the dependent variable did not overlap with the data collected for the antecedents, to further avoid common method bias.

This study was empirically contextualized within the government-funded academic R&D organizations in India, which were pursuing cutting-edge research and commercialization through academic entrepreneurship. Such government-funded R&D teams were mandated by their institutions to develop deep-tech innovations and commercialize them with the intent to create societal impact. Since such teams work on early-stage research, which may take longer to progress through the design, development and deployment stages, they have to make strategic decisions under extreme uncertainty within their internal as well as external environment. Hence, it becomes interesting to investigate how such teams develop strategic leadership capabilities to improve their team innovation performance and translate their scientific discoveries into societal impact. Furthermore, government-funded R&D teams which were pursuing only deep-tech research were included so that best practices could be learnt and shared among the ones which were not currently pursuing advanced research. Teams pursuing low- or medium-tech were excluded as they would not have been experiencing the pressure to innovate under a high risk of failure and long gestation time. This study could not include similar teams pursuing high-tech innovation within the private sector, as their orientation towards innovation might be biased towards profit appropriation rather than impact maximization. Additionally, privately funded labs might be constrained from sharing information about sensitive technologies under research due to non-disclosure agreements and legal constraints. Contrarily, government-funded academic labs may not be bound by any such strict rules and, in fact, are quite open to speaking about their scientific and innovation accomplishments. Hence, government-funded R&D teams operating in government-funded R&D organizations were included in the study.

Existing scales on strategic leadership, risk mitigation and resource orchestration were deployed to collect relevant data from government-funded R&D teams. Each team generally consisted of five to ten members who had shared responsibilities for scientific research, market analysis and product development. Accordingly, care was taken that at least three members from each team participated in the first three phases of the survey, where their inputs were most relevant. The last phase of the survey was reserved for participation from the team leaders who were responsible for the overall performance of these teams and their venture growth. It was ensured that none of the team leaders participated in the first three phases of the survey. All the respondents were requested to provide their collective perspectives about their teams on aspects related to strategic leadership, risk mitigation and resource orchestration for innovation. These responses were recorded on a five-point Likert Scale ranging from 0 to 5, where 0 indicated “strongly disagree”, and 5 indicated “strongly agree” to the respective item. Responses collected through these scales were checked for missing values, corrupted entries and non-linearity before being subjected to statistical analysis using SPSS and AMOS. Thus, a multi-method research design was followed in this study to ensure that the data collected from relevant respondents is free from missing values, assumption violations and common method bias.

All the respondents throughout this study belonged to government-funded academic R&D organizations mandated to pursue advanced research and encouraged to commercialize their findings through technology licencing or venturing within the Indian landscape. Accordingly, innovation R&D teams within government-funded academic organizations were requested to participate in this study by providing their responses to questionnaires, which checked their perception of their teams on various aspects related to team innovation performance. Responses were collected from three different samples – pilot sample (N1 = 120 respondents), analysis sample (N2 = 360 respondents) and validation sample (N3 = 324 respondents) without any changes in the above-mentioned inclusion-exclusion criteria (Table 2). It can be observed that the pilot sample was composed of 78 (65.00%) male respondents, whereas 42 (35.00%) were female respondents. Similarly, the large sample used for the main analysis consisted of 224 (62.22%) male respondents and 136 (37.77%) female respondents, whereas the validation sample consisted of 204 (62.97%) male respondents and 120 (37.03%) female respondents.

In terms of the age of the respondents in the pilot sample, 66 (55.00%) were below 30 years of age, whereas 32 (26.60%) belonged to the age group of 30–49 years, and 22 (18.03%) were above 50 years of age. Analogously, the analysis sample consisted of 128 (35.55%) respondents below 30 years, 188 (52.22%) belonging to the age group of 30–49 years and 44 (12.22%) were above 50 years of age. Lastly, the validation sample consisted of respondents, where 96 (29.62%) were less than 30 years old, followed by 182 (56.17%) were between 30–49 years, and 46 (14.20%) were above 50 years. In terms of work experience, 62 (51.60%) of the pilot respondents, 116 (32.22%) of the analysis sample, and 110 (33.95%) of the validation sample had work experience between five and nine years. Moreover, 35 (29.10%) respondents from the pilot sample, 196 (54.44%) respondents from the analysis sample, and 176 (54.32%) respondents from the validation sample had work experience between 10–19 years. Finally, 23 (19.10%) respondents from the pilot sample, 48 (13.33%) respondents from the analysis sample and 36 (11.11%) respondents from the validation sample had work experience above 20 years, indicating the diversified nature of the participant teams.

The respondent data was collected through well-established scales on strategic leadership capabilities (15 items), risk mitigation capabilities (12 items) and resource orchestration capabilities (12 items) for innovation (Table 3). Each of the scales required responses to be marked on a scale of 1–5, where 1 indicated “strongly disagree” and 5 indicated “strongly agree”. The strategic leadership capabilities scale was a three-dimensional scale with sub-dimensions like empowering vision, resource allocation and support for innovation. An indicative item for visionary leadership reads as “My team creates a compelling vision for innovation”, which can be marked on a five-point Likert scale (Cortes and Herrmann, 2021; Tikas, 2024a). The Cronbach alpha measure for this scale was 0.88, indicating acceptable levels of internal consistency and reliability (Table 4). Similarly, the innovation risk mitigation capability scale consisted of 6 items reflected through three sub-dimensions: risk assessment, prioritization and experimentation. An indicative item for risk mitigation capability reads as “My team judiciously prepares an appropriate attitude toward risky projects”, which can be marked on a five-point Likert scale (Table 3). The Cronbach alpha measure for this scale was 0.89, indicating acceptable levels of internal consistency and reliability (Table 4). Analogously, the innovation resource orchestration capability scale consisted of 6 items, which reflected three sub-dimensions: adaptive structuring, synergistic leveraging and decentralized decision-making (Tikas, 2023a). A sample item from this scale read as “My team provides autonomy for completing innovative tasks”, had to be marked on a five-point Likert scale (Table 3). The Cronbach alpha measure for this scale was 0.82, indicating acceptable levels of internal consistency and reliability (Table 4). Subsequently, all the items were subjected to exploratory factor analysis for item reduction to identify the most relevant ones, which could be used to represent their respective constructs.

Table 3 exhibits the central tendency of the various datasets used in this multi-study research. It primarily indicates the centrality and dispersion of the respondent's perception towards the key constructs and their indicator items. For instance, the perception of respondents on the item “My team develops a set of values for pursuing innovation” averages around 3.72 with a standard deviation (SD = 1.07) for the pilot sample, 3.82 (SD = 1.03) for the analysis sample and 3.78 (SD = 1.07) for the validation sample indicating the overall agreement of the respondents for that indicative item. This implies that the majority of the respondents are in agreement with the statement that their teams develop a set of values for pursuing their innovation goals. Subsequently, correlations among these items will be computed to identify the underlying factors and the nature of association among them, along with the strength of their significance.

The standardized scales were used to collect responses in this multi-study research design consisting of a pilot sample (N1 = 120 respondents), analysis sample (N2 = 360 respondents) and validation sample (N3 = 324 respondents). For every study, data were collected in a three-phased manner from government-funded R&D teams operating in government-funded organizations in India. In the first phase, all the team members were requested to provide their responses for the strategic leadership scale, followed by a week-long break. In the second phase, the team members were requested to mark their responses for the risk mitigation and resource orchestration scales, followed by a week-long break. In the third phase, the team leaders were requested to provide their responses for the team innovation performance scale. Subsequently, the same process was repeated for the analysis sample (N2 = 360 respondents) and validation sample (N3 = 324 respondents). Exploratory factor analysis was used to identify the factors underlying the items and understand their grouping patterns based on their factor loadings (above >0.6) as recommended by experts. Accordingly, the first factor of strategic leadership, consisting of 7 items out of the total 14 items, emerged to be significant. Similarly, the second factor of risk management and the third factor of resource orchestration consisted of 6 items each (Table 5). This also implied that 7 items from the first factor, X items from the second factor and Y items from the third factor were eliminated due to poor factor loadings, low communality scores or heavy cross-loading values. Thus, based on the retained items, strategic leadership (7 items), risk mitigation (6 items) and resource orchestration (6 items) were subjected to confirmatory factor analysis, mediation and path analysis.

The optimized scales for strategic leadership (7 items), risk mitigation (6 items) and resource orchestration (6 items) were deployed among similar respondents without changing the inclusion-exclusion criterion and repeating those who had previously participated. These scales were circulated among the government-funded R&D teams operating within government-funded R&D organizations throughout India. This study was able to collect 360 responses from 120 teams, where three respondents (excluding the team leader) from every innovation team responded to the survey. This ensured that every team was sufficiently represented by those representatives who had at least spent 2 years with the team. A week-long time gap between the scales ensured that there were no prior biases that would affect the incumbent scale or lead to common method bias (Podsakoff et al., 2003). Furthermore, team leaders were requested to participate in a separate survey, which exclusively involved items related to the dependent variable – sustainable innovation performance. This ensured that the data collected for dependent variables was isolated from the o sustainable innovation ne for independent variables to methodologically avoid common method bias (Podsakoff et al., 2012). Confirmatory factor analysis of the above-collected data validated the factor composition of the independent and mediating constructs, along with ensuring the model fit (Table 5). Critical indices such as the comparative fit index (CFI, 0.94), Goodness-of-fit index (GFI, 0.88), and the Tucker–Lewis index (TLI, 0.92) exhibited an acceptable fit (Bentler and Hu, 1998). Root Mean Square Error of Approximation (RMSEA, 0.06), the standardized root mean residual (SRMR, 0.04), and the normed chi-square statistic (χ²/df, 2.31) also emerged to be within an acceptable range (Kline, 2016).

The reliability of these scales was assessed through the Cronbach alpha values, which were within acceptable levels for strategic leadership (α = 0.88), risk mitigation (α = 0.89) and resource orchestration (α = 0.83) as guided by the experts (Bentler and Hu, 1998; Cronbach, 1951). All values of the factor loadings were found to be higher than the minimum recommended thresholds (above 0.6) along with their significance levels (p < 0.05), which helped in firmly establishing the convergent validity of all three constructs. Similarly, discriminant validity for them was established (Table 6) through the Fornell and Larcker (1981) criterion, which specified that “the average variance extracted (AVE) should be greater than the square of respective inter-factor correlations” (Fornell and Larcker, 1981). Thus, the discriminant validity for all three constructs – strategic leadership, risk mitigation and resource orchestration was established as per expert recommendations.

To gain additional validation for the results from the confirmatory factor analysis, a similar study was replicated on an independent sample without changing the data collection protocol or the sample inclusion-exclusion criterion. The replication study involved 324 participants belonging to 108 government-funded R&D teams operating within government-funded R&D organizations in India. The findings (Table 4) revealed that the critical indices like the comparative fit index (CFI, 0.91), the goodness-of-fit index (GFI, 0.85), and the Tucker–Lewis index (TLI, 0.90) were found to be within acceptable thresholds (Bentler and Hu, 1998). Normed chi-square statistic (χ²/df, 2.86), the root mean square error of approximation (RMSEA, 0.06), and the standardized root mean residual (SRMR, 0.04) were also observed to be within acceptable thresholds (Bentler and Hu, 1998; Rappaport et al., 2019). The reliability estimates for all three constructs – strategic leadership (α = 0.86), risk mitigation (α = 0.85) and resource orchestration (α = 0.84) were within the permissible limits (Wang and Kim, 2017). Factor loadings for the items belonging to these constructs were significant at the 0.001 level and were observed to be above the recommended threshold of 0.6 (Kline, 2016). The estimated values of average variance extracted (AVE) were found to be above the minimum threshold value of 0.5 along with sufficiently high factor loadings, which helped establish convergent validity (Hair et al., 2014; Kline, 2016). Additionally, the discriminant validity (Table 6) for all three constructs was established using the Fornell and Larcker (1981) criterion, which required the AVE values to be greater than the squared inter-factor correlations (Fornell and Larcker, 1981). Thus, the validity and reliability of all three constructs have been established in the analysis sample as well as the validation sample, indicating their appropriateness for estimating the mediation effects between strategic leadership and innovation performance of government-funded R&D teams.

A combination of SPSS and AMOS 23.0 was used to develop the measurement model and analyze the mediating effects through the structural equation model (Figure 3) due to their superior functionality over conventional regression models (Iacobucci et al., 2007). Accordingly, the direct and indirect effects of strategic leadership on team innovation performance were computed for both samples and summarized in Table 7. It was observed that the direct and indirect effects were significant at the 0.01 level (two-tailed). The direct effect of strategic leadership on team innovation performance was positive, with standardized regression weights of 0.57 and significant (at p < 0.001). However, with the mediators between them, the direct effect reduces to 0.22, yet significant (at p < 0.01), and the indirect effect is positive for both the mediating paths. Furthermore, the indirect effect of risk mitigation was positive (β = 0.20, p < 0.001), and that of resource orchestration was also positive (β = 0.17, p < 0.01). This confirms the partial mediation effect of both the mediating variables – risk mitigation and resource orchestration on the relationship between strategic leadership and team innovation performance in the analysis sample. A similar effect was observed in the validation sample (N3 = 324), where the direct effect of leadership on team performance reduced from positive (0.43, p < 0.001) to positive (0.12, p < 0.001) after the inclusion of the two mediators. The indirect effect of risk mitigation was positive (β = 0.13, p < 0.01), and that of resource orchestration was also positive (β = 0.18, p < 0.001). This confirms the partial mediating effect of risk mitigation and resource orchestration on the relationship between strategic leadership and team innovation performance, which has been tested across two different large samples (Figure 4).

The findings of this study highlighted the importance of the attentional mechanisms that help strategic leadership influence the sustainable innovation performance of R&D teams operating within government-funded R&D organizations in India. While doing so, it contributed to the extant literature on managerial attention towards innovation in three ways: the exploration of two critical mediators, establishing their statistical significance and estimation of their partial mediating influence, as elaborated below.

First, the study reveals that strategic leadership in government-funded R&D teams impacts sustainable innovation performance both directly and indirectly through two specific attentional mechanisms: innovation-oriented risk mitigation and innovation-oriented resource orchestration. This finding extends the pre-existing generic understanding that “more attention is better” to explain “how attention is converted into capabilities” that accelerate innovation and commercialization. Second, it extends the attention-based view (ABV) by situating attention within innovation-centric decision processes and demonstrating that strategic leadership capabilities—such as envisioning, empowering support, and ambidextrous resource allocation—embed managerial attention into everyday routines that foster experimentation, risk-taking, and psychological safety. This finding shifts the extant conversations on ABV from “where attention goes” to “what attention does for innovation” in public R&D organizations. Third, it deepens our understanding of resource orchestration and risk-mitigation theories by identifying attentional sub-capabilities (adaptive structuring, synergistic leveraging, decentralized decision-making, data-driven risk assessment, evidence-based prioritization, and collaborative experimentation) that partially mediate the leadership–innovation relationship, thereby specifying the mechanisms through which strategic leaders can transform environmental turbulence into sustained innovation performance in government-funded deep-tech environments.

Strategic leadership capabilities can help an organization identify novel approaches to innovation and integrate them into daily routines to enhance efficiency and effectiveness. In highly competitive environments, such novel approaches to innovation can drive future growth and ensure organizational survival through new value propositions, market disruptions and unique business models (Sinha et al., 2023). Extant literature on the attention-based view (ABV) highlights the importance of attentional mechanisms in shaping strategic decisions and responses to environmental challenges (Ocasio et al., 2018). However, it fails to contextualize these managerial attention mechanisms within innovation-centric decision-making scenarios. This creates a knowledge gap that impedes scholars and practitioners from comprehensively understanding the impact of strategic leadership capabilities on organizational structures and underlying cognitive processes that influence innovation performance (Shaikh and Randhawa, 2022). Ignorance about such attentional mechanisms governing innovation impedes leaders from blending cognitive processes with organizational practices to build an innovation-friendly environment (Cortes and Herrmann, 2021). The study addresses these gaps by empirically estimating both the direct and indirect effects of risk mitigation and resource orchestration in the leadership–innovation relationship. This shows that strategic leadership capabilities—manifested as dynamic envisioning, empowering support, and ambidextrous resource utilization—foster an environment that encourages visionary thinking, facilitates experimentation, and supports informed risk-taking in innovation activities. Such an environment enhances psychological safety and, in turn, improves innovation speed, shortens development cycles, and helps optimize productivity (Jia et al., 2022). In such innovation-friendly settings, managerial attention can be more sharply directed toward identifying untapped market segments, understanding unmet needs, and developing innovations that address those needs (Laszczuk and Mayer, 2020). Thus, a capability-based approach to managing this environment helps routinize these processes so that organizations can consistently innovate products, processes, and business models (Tikas, 2024a). Accordingly, strategic leadership influence sustainable innovation performance both directly and indirectly through the creation of innovation-supportive contexts that enable more effective risk mitigation and resource orchestration.

Strategic leadership primarily shapes how organizations acquire, adopt, and allocate innovation-critical resources for novel solution development, thereby directing the way resource combinations are leveraged to gain and sustain competitive advantage (Jia et al., 2022; Wang et al., 2020). However, existing work on resource orchestration explains little about how organizations actually recombine their idiosyncratic resources to improve innovation performance or how strategic leaders exploit synergies across resource configurations (Andersén and Ljungkvist, 2021). It also does not sufficiently inform about how strategic leaders can identify synergies among their resource recombination strategies that are available within their organizational settings (Cui et al., 2022; Sirmon et al., 2011). This study addresses these gaps by examining how strategic leadership activates resource orchestration capabilities—specifically adaptive structuring, synergistic leveraging, and decentralized decision-making—in government-funded R&D teams through an attention-based view (Tikas, 2023a; Wang et al., 2020). The findings suggest that leaders with strong strategic capabilities can use adaptive structures to reallocate attention rapidly in response to sudden environmental shifts, which R&D teams may interpret as threats or opportunities for future innovation-driven growth. Strategic leaders can also help R&D teams leverage latent opportunities by identifying and integrating them into existing innovation processes, generating synergistic alignment and non-linear returns that can enhance future innovation performance (Andersén and Ljungkvist, 2021). Finally, decentralizing decisions about resource acquisition and allocation to R&D teams can accelerate innovation and improve decision quality by harnessing collective problem-solving and brainstorming (Cui et al., 2022). Collectively, these insights extend the attention-based view by incorporating resource-orchestration capabilities into the explanation of how strategic leadership improves sustainable innovation performance.

Strategic leadership also shapes innovation risk mitigation by combining data-driven risk assessment, evidence-based prioritization, and collaboration-based experimentation into a coherent set of practices. This combination enables leaders to navigate uncertainty in high-velocity innovation environments by proactively identifying emerging threats and reframing them as opportunities for growth, rather than treating risk as a purely defencive concern (Giaccone and Magnusson, 2022). When such practices become routinized, they crystallize into risk-mitigation capabilities that systematically direct managerial attention toward issues that matter most for the success of innovation initiatives, thereby supporting sustained competitive advantage (da Silva Etges and Cortimiglia, 2019). The broader literature offers convergent illustrations of this mechanism: firms that continuously scan market and technological trends, anticipate shifts in customer preferences, and institutionalize experimentation are better able to transform potential vulnerabilities in products, processes, or business models into new growth trajectories (Hock-Doepgen et al., 2021). These patterns reinforce the argument that it is not merely the presence of risk mitigation processes, but their seamless integration into the leadership's attentional architecture, that enhances innovation performance (Jia et al., 2022). For instance, prior work highlights how Tesla's market trend analysis and hype creation around novel innovations helped it navigate competitive and regulatory pressures, while Netflix used similar anticipatory sensing of consumer preferences to pivot its business model into new markets (Shaikh and Randhawa, 2022). Likewise, 3 M's culture of continuous exploration and creative exploitation has enabled rapid experimentation and evolution of its product portfolio to sustain competitiveness (Laszczuk and Mayer, 2020). Together, these examples underscore the role of strategic leadership in embedding risk-mitigation routines that enhance innovation performance and extend the attention-based view by foregrounding risk-related mechanisms as a key mechanism through which managerial attention shapes innovation outcomes.

However, these results can be generalized primarily to innovation-intensive organizational contexts that pursue deep-tech R&D projects in emerging economies where technology development teams operate under high technological and market uncertainty, resource constraints, and mandates for societal impact. Within such environments, the identified directional mechanisms —strategic leadership shaping sustainable innovation performance through risk mitigation and resource orchestration—are likely to hold, consistent with broader evidence on innovation-driven leadership in public and quasi-public organizations. Beyond these, these directional relationships may extend to other public-sector and mission-driven R&D organizations with caution. Broad generalizations to private-sector firms or low-tech organizations pursuing incremental innovation settings may be difficult due to the differences in the incentive structures, governance logics, and risk–resource profiles that can alter the salience and strength of the mediating mechanisms. Accordingly, these findings should be viewed as context-bound but theoretically portable propositions that call for further external validation through cross-country, cross-sector, and cross-technology studies that explicitly test how certain institutional contexts and innovation types can moderate the proposed relationships.

Collectively, this study extends prior empirical work that links strategic leadership, attention, and innovation by moving beyond simple main-effect models and specifying the dual mediating roles of innovation-oriented risk mitigation and resource orchestration in government-funded, deep-tech R&D teams. Typically, prior studies have shown that visionary or innovation-supportive leadership is positively associated with innovative outcomes. However, this study adds a more granular mechanism-based explanation by showing that adaptive structuring, synergistic leveraging, decentralized decision-making, data-driven risk assessment, evidence-based prioritization, and collaboration-based experimentation jointly explain how leadership attention is translated into superior sustainable innovation performance in public R&D settings. Theoretically, these findings extend the attention-based view from a predominantly structural-cognitive account of “where attention goes” to a more capability-based account of “what attention does” for innovation performance, and enrich resource orchestration and risk-mitigation theories by identifying specific attentional sub-capabilities through which strategic leaders can convert environmental turbulence into sustainable innovation performance.

This study offers targeted implications for senior leaders, R&D managers, and incubation directors in government-funded R&D and academic innovation settings. The evidence indicates that strategic leadership, risk mitigation, and resource orchestration form a mutually reinforcing “capability bundle” through which organizations can consistently enhance their sustainable innovation performance. Accordingly, leadership development towards sustainable innovation should be treated as a core strategic investment by creating leaders who articulate a compelling long-term innovation-centric vision, foreground sustainability, and facilitate experimentation that supports calculated risk-taking, learning, and collaboration. By fostering an innovation-centric culture, these capabilities can be embedded in the recruitment, development, and promotion systems to align with long-term innovation goals. Risk mitigation should be integrated into innovation management as a continuous, strategy-driven process, supported by routines for ongoing risk assessment, portfolio-based prioritization, and disciplined experimentation that balance failure protection with scaling-up potential.

Concurrently, resource orchestration should also be strengthened through agile structures, cross-functional teams, flexible budgeting, and boundary-spanning roles that enable teams to mobilize, bundle, and reconfigure resources in response to evolving technological and sustainability demands. The results underscore that it is the integration of strategic leadership, risk mitigation, and resource orchestration—rather than isolated improvements in any single domain—that underpins stronger sustainable innovation performance and provides a practical diagnostic lens for identifying capability gaps in ventures that stagnate or fail to scale. While especially salient for government-funded deep-tech R&D teams operating under high accountability and resource constraints, these mechanisms are likely relevant to other mission-driven and hybrid organizations seeking to design innovation ecosystems that support sustainable innovation performance.

Methodological limitations could have constrained the researcher's ability to collect data from all the government-funded R&D teams operating within government-funded R&D organizations in India. The current study was largely restricted to innovation teams pursuing deep-tech innovation, which is resource-intensive and can only be pursued by well-funded academic organizations. Hence, it could not include those organizations that might be pursuing low-tech or non-tech innovations, which may not require huge funding and critical equipment during the initial stages. The inclusion of such innovation teams in future research can help provide a comparative analysis among deep-tech and low-tech entrepreneurship that can contribute towards extending the attention-based view of the firm and innovation strategy analysis. Furthermore, this study could not involve private-sector government-funded R&D teams, which might be facing a different set of challenges concerning survival and scalability. Incorporating such innovation teams can add a new dimension to the extant literature on the attention-based view by comparing the challenges faced by innovation teams incubated within public-funded and private-funded academic organizations. Lastly, the current study was restricted to Indian organizations and could not include similar teams outside India to provide a cross-cultural perspective on the challenges faced by government-funded R&D teams. Future researchers can extend this study beyond India to add novel contextual factors or constraints that can contribute towards enriching the attention-based view of the firm and strategic leadership for driving sustainable innovation performance.

This study set out to explain how strategic leadership shapes the sustainable innovation performance of government-funded R&D teams embedded in Indian public-sector organizations. Drawing on the attention-based view and dynamic capabilities perspectives, the theoretical model and empirical analysis jointly demonstrate that strategic leadership drives sustainable innovation performance indirectly through two central mechanisms: risk mitigation and resource orchestration. The findings imply that, in government-funded R&D settings characterized by high uncertainty and multiple institutional demands, strategic leadership can simultaneously mitigate critical risks and orchestrate resources for innovation, thereby enabling R&D teams to deliver stronger and more sustainable innovation performance. This simultaneous emphasis on risk mitigation and resource orchestration underscores the importance of strategic leadership for sustaining innovation in mission-oriented public organizations. Theoretically, the study advances the attention-based view by specifying how leadership attention and sustainability-oriented priorities are translated into sustainable innovation performance via risk mitigation and resource orchestration pathways, rather than exerting only direct effects. It also extends dynamic capabilities research by showing that the orchestration of resources for sustainable innovation is contingent on leaders' capability to recognize, frame, and manage risks inherent in the long-horizon, public R&D initiatives. Together, these insights illuminate a more nuanced micro–foundation of how strategic leadership activates and channelizes capabilities in sustainability-oriented innovation contexts.​

From a practical standpoint, the results suggest that policy makers in government-funded R&D organizations should view strategic leadership development, risk management architectures, and resource orchestration routines as mutually reinforcing levers for improving sustainable innovation performance. Interventions such as targeted leadership development programs, formalized risk–innovation portfolios, and cross-functional resource orchestration mechanisms can help R&D teams better navigate competing demands while preserving a sustainability focus. These contributions are subject to important boundary conditions within resource-constrained ecosystems like Indian government-funded R&D organizations. Accordingly, the institutional, cultural, or sectoral features may differ in how strategic leadership, risk mitigation, and resource orchestration interact in other settings. Future research can extend this work by examining alternative institutional contexts, testing additional mediating and moderating mechanisms (e.g. organizational climate, policy volatility, stakeholder pressures), and employing multi-level or longitudinal designs to unpack the temporal dynamics through which strategic leadership capabilities drive sustainable innovation performance.​