Leveraging Big Data Analytics Capability for Firm Innovativeness: The Role of Sustained Innovation and Organizational Slack

Abstract

In the era of digital transformation and data-driven decision-making, big data analytics capability (BDAC) is crucial for firms to enhance innovation and sustainable competitive advantage in highly dynamic markets. Grounded in dynamic capability theory, this study used a moderated mediation model to explore the impact of BDAC on innovativeness. Empirical analysis was conducted by using survey data from 270 enterprises to test the hypotheses. The results reveal that BDAC significantly and positively influences innovativeness, and sustained innovation mediates this relationship. Moreover, organizational slack positively moderates the effect of BDAC on innovativeness, both the direct effect and indirect effect. These findings provide theoretical support and practical implications for understanding how BDAC enhances firm innovativeness.

1. Introduction

As advanced technologies continue to penetrate all industries in today’s dynamic business environment, human civilization is rapidly transitioning to the data era, with data becoming the key element of modern firms [1]. To fully unleash the value of big data, organizations should build big data analytics capability (BDAC) to coordinate diverse data resources [2]. In the age of digital transformation and data-driven decision-making, BDAC has emerged as the next frontier of innovation, competition, and productivity [3]. It is served as a critical tool for organizations to survive, innovate, and sustain competitive advantage in highly dynamic markets [4,5]. BDAC encompasses personnel expertise, collaboration and knowledge exchange processes, and accessible infrastructure and data, along with well-established collecting and processing methods [3]. It empowers firms to manage, process, and analyze datasets across various business areas, thereby generating valuable insights into customer needs and emerging market opportunities. These insights improve the quality of strategic decision-making, foster innovations that are more responsive to market demands, and ultimately enhance firm performance and competitive advantage [2,6,7,8].

Meanwhile, in today’s turbulent business environment, the importance of innovation has become more prominent than ever before [9]. Innovation is a primary strategic objective for organizations and a core driver for gaining substantial competitive advantage and achieving long-term success [10,11]. Innovativeness, as the propensity to innovate or adopt innovations, is a necessary factor for firms to obtain a high level of novelty to survive successfully in an increasingly changing and volatile environment [12]. It is manifested as the novelty degree of innovation in products, services, processes, technological updates and business models. Furthermore, innovativeness is widely regarded as a critical source for firms to create new markets [12], increase performance and maintain competitive advantages [13].

Existing research on the relationship between BDAC and innovation primarily centers on three perspectives. First, prior research has suggested that BDAC can improve overall innovation performance [7,14]. Second, studies have examined the impact of BDAC on different types of innovation, showing that BDAC can promote business model innovation [5], process innovation [15], supply chain innovation [16], and service innovation [17]. Third, the influence of BDAC on innovation capability is explored, concluding that it can improve the quality and speed of innovation, thereby enhancing innovation capability [18,19]. However, the relationship between BDAC and innovativeness, which is an important concept reflecting a firm’s innovation tendency and ability, has not yet been investigated. Therefore, the following question is posed:

RQ1. Does BDAC significantly affect a firm’s innovativeness?

Moreover, previous research has demonstrated that data resources promote sustained innovation [20,21], and that sustained innovation contributes to innovativeness [22]. However, it remains unclear whether sustained innovation can serve as a bridge between BDAC and innovativeness. Additionally, achieving innovativeness may not only requires BDAC, it also depends on the coordinated action of other internal factors [23,24]. In particular, organizational slack serves as a critical internal resource for firms and is essential to facilitating innovation driven by technological progress [25]. Nonetheless, current research remains inconsistent on the role of organizational slack in innovation [26,27,28]. Therefore, the following questions are posed:

RQ2. Does sustained innovation serve as mediators in the relationship between BDAC and innovativeness?

RQ3. Does organizational slack effect the relationship between BDAC and innovativeness?

Accordingly, this study aims to investigate the impact of BDAC on firm innovativeness, with attention to the mediating role of sustained innovation and the effect of organizational slack. Drawing on the dynamic capability theory, we develop a moderated mediation model to explore the mechanism through which BDAC affects innovativeness via sustained innovation, while also examining how organizational slack moderates both the direct and indirect relationships in this framework.

This study addresses a research gap in understanding the role of BDAC in firm innovativeness. Prior research has primarily focused on the effects of BDAC on innovation performance, specific types of innovation, and innovation capability, with limited attention to its influence on innovativeness. By examining how BDAC impacts innovativeness, this study helps fill this gap and extends the literature on BDAC’s role in driving innovation. Furthermore, it contributes to the research on the mechanisms through which BDAC promotes innovativeness by introducing sustained innovation as a mediating variable. In addition, it advances research on the boundary conditions of BDAC’s impact on innovativeness by exploring the moderating effect of organizational slack. Overall, the study offers theoretical and practical insights for organizations seeking to leverage BDAC to enhance firm innovativeness in dynamic environments.

The structure of this paper is organized as follows: first, the existing literature is reviewed to explain the relationships among the variables in the proposed research model and then corresponding research hypotheses are developed. Second, the research methodology is described. Third, the research model is evaluated and the hypotheses are tested. Finally, the empirical findings are discussed, followed by conclusions, theoretical contributions, practical implications, limitations, and suggestions for future research.

2. Literature Review and Hypotheses

2.1. BDAC and Innovativeness

In recent years, an increasing number of firms have accelerated the deployment of BDAC to gain critical insights and enhance their competitive advantages [3]. BDAC can be defined as the ability to capture, store and analyze big data to generate valuable insights by effectively deploying resources through firm-wide processes, roles and structures [3,29]. BDAC is commonly conceptualized in three dimensions: tangible capabilities, intangible capabilities, and human skills, which together provide a clear framework for firms in effectively developing their competitive advantages [29]. Tangible capabilities include abilities to integrate, store, process, analyse and visualise internal and external data, adequate physical and technological infrastructure, and financial resources. Intangible capabilities are represented by a data-driven decision-making culture and a high orientation to collect, share, stock and apply big data-based knowledge. Human skills consist of the technical, managerial, and relational capabilities of professional personnel such as data scientists and big data analysts [5,18]. The combined effect of these resources enables firms to develop BDAC to achieve business value enhancement, particularly in driving innovativeness [30].

Innovativeness is essential to the success of firms [31]. It refers to a firm’s ability and tendency to create or adopt new products and services, enter new markets, and support new ideas, novelty, experimentation, and creative processes [32]. Beyond openness to new ideas, innovativeness also captures the extent to which it deviates from established practices. Inherently, it reflects a firm’s willingness to support creativity in developing products and services, as well as the novelty of innovation practices [33,34]. Firms with high innovativeness are more inclined to utilize innovative technologies to generate new ideas, processes, products, and systems, which is crucial for sustaining their competitiveness and long-term success [35].

Previous studies, based on the perspective of dynamic capability theory, have regarded BDAC as a dynamic capability, believing that it can better perceive and explore new opportunities, reallocating resources and regenerate capabilities, promote enterprises to achieve reconstruction and transformation, and thereby facilitate innovation activities [29]. Therefore, this study takes the dynamic capability theory as an appropriate perspective to understand how BDAC promotes innovativeness. Specifically, BDAC contributes to quickly collect and analyze large amounts of data from both internal and external sources, enabling firms to extract unique, untapped, and valuable insights and knowledge from large-scale, diverse, and up-to-date data [5]. This is conducive to expanding the existing knowledge resources of the firm [29] and providing decision-makers with wise support [36], thereby encouraging both managers and employees to pursue innovative affairs and driving the enterprise to make more innovative attempts. Moreover, BDAC can enhance operational capabilities such as marketing and technological capabilities by generating valuable and actionable insights to strengthen the competitiveness of firms [3]. It enables firms to adopt large volumes of structured data for reporting and forecasting purposes in operations to promptly detect issues, monitor supplier behavior and assess customer satisfaction, and make operational processes more predictive and responsive [37]. This facilitates the effective execution of corporate activities, enabling firms to proactively adopt novel solutions on a quasi-real-time basis to quickly adapt to changes and challenges. It also stimulates firms to innovate their products, processes, business models and improve their innovation capability [15,16,19]. As a result, technological innovations based on R&D outcomes become more readily recognizable and acceptable. Furthermore, BDAC helps establish a monitoring mechanism to grasp customer needs and competitor actions in the dynamic social and business environment [29]. This allows firms to rapidly adapt their products and services to better match consumer preferences, and identify, anticipate, and learn from competitor behaviors in order to provide support for them to outperform competitors [29]. This is conducive to improving the organizational environment for innovativeness and providing support for continuous improvement and firm innovativeness [38]. Therefore, we hypothesize that:

H1. 

BDAC positively affects firm innovativeness.

2.2. BDAC and Sustained Innovation

To survive and thrive in today’s increasingly dynamic and disruptive business world, firms need to go beyond occasional innovations and engage in sustained innovation that adapts to environmental changes [39]. Sustained innovation encompasses a series of dynamic and sustained processes and capabilities, including the accumulation of knowledge and experience through innovation, the elimination of outdated mindsets and technologies, the reconfiguration of innovation resources and capabilities, and the continued reliance on previously successful innovation paths [40]. It reflects the sustainable connection between current and past innovation activities, emphasizing the continuity and cumulative nature of innovation over time [39,41]. Sustained innovation is characterized by continuous accumulation, relying on past innovation trajectories and the accumulation of innovation capabilities. It is crucial for firms to maintain sustainable competitive advantages and development in an increasingly turbulent and uncertain competitive environment [41].

Appropriate decisions based on BDAC can enhance a firm’s performance and value. Two important ways are beneficial from the function of BDAC. One is the personalization. This provides a customized product or service information. Another is the real-time analytics. This is the real-time analysis of large volumes of data. BDAC enhances firms’ customer interactivity capability and customer retention capability to improve strategic sales performance, achieving sustained and rapid growth in sales revenue and profits [42]. And then, these achievements can provide the financial support needed for sustained innovation. BDAC enables firms to deeply explore information critical for R&D process, such as customer usage behaviors, and customer needs, and identify common technological components across different products or services. It is BDAC that allows firms to extract structural patterns and optimization paths from vast amounts of data related to product design and usage [43]. This encourages firms to develop general technology platforms and product architecture systems, facilitate modularity and standardization at both the technological and product levels, and ultimately improve innovation efficiency and sustainability. Additionally, BDAC can optimize supply chain planning and enhance supply chain operational efficiency and innovation capability by optimizing real-time routes, reducing transportation costs and lowering financial risks [44]. Eventually, firms can better optimize resource allocation and value chain coordination, more effectively explore economies of scale, foster continuous technological accumulation. These improvements boost innovation sustainable growth. Therefore, we hypothesize that:

H2. 

BDAC positively affects sustained innovation.

2.3. Sustained Innovation and Innovativeness

At the organizational level, the innovation accumulation derived from continuous learning and practice within sustained innovation provides firms with knowledge and experience for proactively pursuing innovation [40,45]. Indeed, sustained innovation enables firms to gain a competitive edge over their rivals and improve both innovation performance and financial performance, providing the material resources and strategic flexibility needed for firm innovativeness [46]. In addition, engaging in sustained innovation over the long term means that firms gain a deeper understanding of the business environment and helps them identify key opportunities and threats to formulate appropriate strategies for environmental adaptation in innovativeness [45]. Consequently, firms with sustained innovative behaviors can enhance innovation efficiency, reduce innovation risks, and be more inclined to decide to pursue innovativeness. At the employee level, a culture of sustained innovation over the long term can enhance employees’ confidence in innovation and the formation of their creative role identity, thereby boosting intrinsic motivation and encouraging employees to actively pursue innovativeness efforts [47]. Therefore, we hypothesize that:

H3. 

Sustained innovation positively affects firm innovativeness.

2.4. Mediating Role of Sustained Innovation

Studies widely recognizes digital technologies such as BDAC as a critical enabler of corporate innovation, particularly in supporting the continuity of innovation over time [48]. BDAC empowers firms to identify and extract valuable knowledge and resources from massive datasets, restructure that knowledge, and enhance knowledge diversity, thereby facilitating the transformation of data into knowledge, and knowledge into actionable intelligence [49]. Such capabilities enable knowledge management to operate accurately and efficiently, motivates firms to take the initiative in knowledge reconstruction and innovation, allows firms to acquire and allocate more resources and invest them in innovation activities, and ultimately promotes sustained innovation [50]. Moreover, through long-term and continuous investment in innovation, firms are better positioned to continuously initiate and implement new innovation projects, leading to improved economic performance and innovativeness [50]. During the sustained innovation process, the cumulative feedback generated by ongoing innovation efforts can strengthen firms’ ability to refine their innovation strategies, enhancing both their propensity for innovation and the novelty of outcomes [51]. Therefore, we hypothesize that:

H4. 

Sustained innovation mediates the relationship between BDAC and firm innovativeness.

2.5. Moderating Role of Organizational Slack

Organizational slack refers to the actual or potential resources retained by firms beyond their immediate operational needs, in order to cope with environmental changes and pursue new opportunities [52,53]. Typical forms of slack include excess cash, unallocated capital expenditures, surplus employees, and underutilized operational capacities at the technical level [52]. These resources exceed the minimum required to maintain a specific level of organizational output, in order to serve as a buffer during times of turbulence and uncertainty, helping firms absorb shocks and seize new opportunities that arise from such conditions [52]. Organizational slack is widely regarded as a critical factor influencing firm innovation [53]. It is essential for overcoming external shocks, creating new value, supporting organizational transformation, thus then benefiting the pursuit of innovative opportunities [54].

Organizational slack can provide sufficient slack resources for innovation activities, enabling firms without resource constraints and inducing them to engage in more creative initiatives [55]. This allows firms not only to optimize existing processes through BDAC, but also to allocate sufficient resources to transform BDAC-generated insights into substantive innovativeness outcomes [32]. Meanwhile, organizational slack can enhance returns on innovativeness driven by BDAC by providing the necessary resources to support innovation projects across different technological domains [25]. Organizational slack also enhances firms’ ability to absorb shocks and recover rapidly from shocks [54], which can create room for experimentation and error to encourages BDAC-driven innovativeness efforts. Additionally, organizational slack fosters a culture that values experimentation and learning from failure, shifting managerial decision-making from efficiency-centered logic to risk-oriented entrepreneurship, and promoting corporate entrepreneurship [55]. It enhances firms’ willingness, flexibility, and confidence to explore and implement novel opportunities and innovation [56], ultimately strengthening the positive impact of BDAC on innovativeness. Therefore, we hypothesize that:

H5. 

Organizational slack positively moderates the relationship between BDAC and firm innovativeness.

Sustained innovation is characterized by long timeframes, and substantial financial demands, consistent, long-term investment in both costs and resources [51]. As an accumulated excess of organizational resources, organizational slack can alleviate short-term performance pressures and support sustained innovation efforts under uncertain market conditions. Beyond directly supplying necessary resources for innovation projects, organizational slack also acts as a critical buffer during environmental shifts or innovation failures, helping firms maintain stable operations [54]. By easing resource constraints and improving firms’ tolerance for failure, organizational slack enables firms to engage in long-term, high-risk, and breakthrough innovativeness, thereby enhancing both the intensity and novelty of sustained innovation activities. Therefore, we hypothesize that:

H6. 

Organizational slack positively moderates the relationship between sustained innovation and firm innovativeness.

Organizational slack facilitates innovation decisions based on BDAC-driven valuable insights by providing flexibility in strategic planning and supporting decision-makers in actively pursuing innovation strategies [57]. The accumulation of resources within firms is a key driver of sustained innovation behavior [57]. Organizational slack can provide sufficient resources and service as an important buffer in the sustained innovation process. It also enhances a firm’s ability to cope with potential innovation risks and threats, offer greater room for trial and error, thereby supporting sustained innovation-driven innovativeness activities [57]. At the same time, organizational slack allows firms to use abundant resources during sustained innovation, adjust resource allocation flexibly, and better withstand the risks of failure. This enable firms are more likely to explore diverse and novel innovation paths, unlocking their research and development potential and increasing their enthusiasm and initiative toward innovation [49]. Accordingly, organizational slack amplifies the effect of sustained innovation on innovativeness. Therefore, we hypothesize that:

H7. 

Organizational slack strengthens the mediating effect of sustained innovation on the relationship between BDAC and firm innovativeness.

The conceptual model is illustrated in Figure 1. To better clarify the concepts of innovativeness and sustained innovation, we provide a conceptual comparison table in Appendix A.

3. Methods

3.1. Sample

This study employed a questionnaire survey method to collect data from firms in China. The respondents’ distribution characteristics are presented in

Table 1. Profile of the respondents.

Characteristics	Range	Frequency	Percent (%)
Gender	Male	145	53.70
	Female	125	46.30
Age	18–25 years	5	1.85
	26–33 years	131	48.52
	34–41 years	101	37.41
	42–49 years	28	10.37
	50 years and above	5	1.85
Jobtitle	R&D or Data Analyst	62	22.96
	Middle Management	103	38.15
	Senior Management	105	38.89
Firmage	Less than 1 year	0	0
	1–3 years	1	0.37
	3–5 years	26	9.63
	5–10 years	99	36.67
	Over 10 years	144	53.33
Employee	Fewer than 100	22	8.15
	100–300	63	23.33
	300–500	66	24.44
	500–1000	60	22.22
	Over 1000	59	21.85
Industry	Electronic Information Technology	108	40.00
	Finance & Insurance	15	5.56
	Manufacturing	80	29.63
	Service Industry	17	6.30
	New Energy & Environmental Protection	22	8.15
	Biotechnology & Pharmaceuticals	20	7.40
	Others	8	2.96
Datadepartment	Independent Data Center	103	38.15
	Department Responsible for Data Management	167	61.85

.

The survey targeted three kinds of staff. They are R&D or Data Analyst, middle management and senior management. These respondents were highly relevant to the research topic and sufficiently familiar with the questionnaire content. The formal survey data were collected through the Wenjuanxing online platform, with a total of 400 questionnaires distributed randomly. After several rounds of screening, valid responses were used for the empirical analysis of the impact of big data analysis capabilities on firm innovativeness. First, 32 incomplete questionnaires were excluded, including incomplete scoring of measurement items or the absence of basic firm background information. Second, 27 responses that did not meet the sample selection requirements were removed, including those from nonprofit institutions, colleges, universities and research institutions, as well as those without data management departments. Third, 43 responses that failed the reversal item test were eliminated. A reversal item is designed to assess whether respondents are answering attentively by presenting two options with opposite meanings. Lastly, 28 responses with consistent scores for multiple consecutive items suggesting strong response regularity were eliminated. After screening, 270 valid samples remained, and the effective recovery rate was 67.5%.

3.2. Measures

All measurement items in this study were adapted from the existing mature scales in prior literatures. The items have been translated into Chinese and improved through back translation. The measurements of questionnaire variables were all presented as multi-item scales, and the response format of each item was a Likert-type scale ranging from 1 to 7, in which 1 represents strongly disagree and 7 is strongly agree.

Dependent variable: Innovativeness (INNO) was adapted from Lin et al. (2025) [58] and Hurley & Hult (1998) [59], comprising 4 items. It is related to technical innovation, management team activity, the degree of innovation adoption, and encouragement for employees to propose new ideas.

Independent variables: BDAC was operationalized based on the measurement framework proposed by Lin et al. (2025) [58] and Akter et al. (2016) [60], incorporating 4 items. It is associated with aspects such as advanced data mining tools, relationship discovery capabilities, predictive analytics capabilities, and the ability to uncover market trends from big data.

Mediating variable: Sustained innovation (SUSINN) was adapted from Latan et al. (2020) [46] and Dougherty & Hardy (1996) [61], consisting of 4 items. It reflects sustained growth in revenues and profits, common technology platforms, product architecture, and the opportunities for exploring scale economies of innovation.

Moderating variable: Organizational slack (OS) was measured adapted from Jiao et al. (2021) [62] and Atuahene-Gima (2005) [63], containing 4 items. It is associated with uncommitted resources, short-term resource availability, rapid resource access, and managerial resource discretion.

Control Variables: The study incorporates big data resources, the five strengths (including ease of entry, threat of substitutes, bargaining power of buyers, bargaining power of suppliers, and rivalry among existing players), firm age, and employee as control variables. Specifically, big data resources (BDR) was adapted from Suoniemi et al. (2020) [64] and Akter et al. (2016) [60], while The five strengths was adopted from Guo et al. (2022) [45]. All measurement items are provided in Appendix B.

3.3. Common Method Bias Test

Common Method Bias (CMB) refers to the artificial and systematic errors in the correlation between variables caused by the common influence of measurement tools, data collection methods, or participants’ response patterns when using the same data collection method or source. Therefore, in this study, Harman’s single-factor method was adopted to conduct the common method deviation test by conducting exploratory factor analysis and principal component analysis on all measurement items. The test results showed that when the eigenvalue is greater than 1, the cumulative explanatory variance percentage of the first factor is 36.805% (less than 40%), indicating that no specific factor emerged, and the first principal factor does not explain most of the variance, suggesting no severe common method bias exists in this study. In addition, the multicollinearity test was conducted using Smart PLS, and all variance inflation factor (VIF) values were well within acceptable limits, suggesting that multicollinearity is not a concern in the research model.

4. Results

4.1. Assessment of the Measurement Model

This study evaluated the measurement model by using Smart PLS to ensure the reliability and validity of research constructs. The factor loadings for each item, Cronbach’s α coefficients, and composite reliability values all exceeded the recommended threshold of 0.7 (see

Table 2. Reliability and validity analysis.

Constructs	Items	Cronbach’s Alpha	VIF	Loadings	Cross Loadings	AVE	Composite Reliability
BDAC	BDAC1	0.736	1.365	0.729	0.729	0.558	0.835
	BDAC2		1.312	0.717	0.717
	BDAC3		1.590	0.781	0.781
	BDAC4		1.463	0.760	0.760
SUSINN	SUSINN1	0.738	1.275	0.702	0.697	0.562	0.836
	SUSINN2		1.679	0.792	0.794
	SUSINN3		1.734	0.794	0.792
	SUSINN4		1.312	0.704	0.710
OS	OS1	0.832	1.516	0.741	0.741	0.665	0.888
	OS2		2.080	0.860	0.860
	OS3		1.847	0.843	0.843
	OS4		1.918	0.814	0.814
INNO	INNO1	0.753	1.357	0.724	0.736	0.574	0.843
	INNO2		1.487	0.792	0.788
	INNO3		1.461	0.744	0.755
	INNO4		1.473	0.768	0.751
BDR	BDR1	0.747	1.517	0.772	0.773	0.569	0.841
	BDR2		1.323	0.722	0.721
	BDR3		1.542	0.789	0.789
	BDR4		1.379	0.733	0.733

), confirming the reliability and internal consistency of the measurement scales. The AVE values of the indicators in the model are all exceeded 0.5 (see Table 2), indicating satisfactory convergent validity of the measurement items. The cross-loadings test of each item shows that the load of each item on the index it represents is greater than the load on other indicators (see Table 2), indicating that each index has good discriminant validity.

In addition, discriminant validity was assessed using the Fornell–Larcker criterion. This approach compares the square root of the AVE for each construct with the correlations between that construct and all other constructs in the model. As shown in

Table 3. Fornell Larker criterion.

	BDAC	BDR	INNO	OS	SUSINN
BDAC	0.747
BDR	0.690 **	0.755
INNO	0.430 **	0.367 **	0.758
OS	0.528 **	0.580 **	0.352 **	0.816
SUSINN	0.606 **	0.667 **	0.441 **	0.614 **	0.749

Notes: The square roots of AVE appear on the diagonal. Non-diagonal elements (non-bold) are the correlations among constructs. ** indicates the significance level p < 0.01.

, the square root of the AVE value for each construct exceeds the corresponding inter-construct correlations in the respective columns. This result indicates that each construct shares more variance with its own indicators than with other constructs, providing strong evidence of discriminant validity.

Given that high factor loadings may bias the Fornell–Larcker criterion and potentially obscure true discriminant validity, this study further assessed discriminant validity using the heterotrait–monotrait (HTMT) ratio of correlations [65]. The HTMT ratio has been recognized as a more reliable approach in the context of PLS-SEM, with Monte Carlo simulation studies demonstrating its superior performance in detecting discriminant validity issues compared to traditional methods.

As shown in

Table 4. HTMT ratio.

	BDAC	BDR	INNO	OS
BDAC
BDR	0.931
INNO	0.576	0.490
OS	0.672	0.736	0.445
SUSINN	0.825	0.898	0.593	0.781

, the HTMT values among the core constructs (BDAC, sustained innovation, organizational slack, and firm innovativeness) were all below the recommended threshold of 0.90, indicating acceptable discriminant validity [65]. However, the HTMT value between BDAC and the control variable big data resources (BDR) was 0.931, slightly exceeding the conventional cut-off. While this warranted attention, it did not indicate construct redundancy. BDR and BDAC, although related, were conceptually distinct: BDR reflects the availability and breadth of big data assets, whereas BDAC captures a firm’s ability to effectively process and apply these resources. Moreover, as BDR was included in the model as a control variable rather than a focal construct, this elevated correlation did not undermine the discriminant validity of the main constructs or the validity of the hypothesized model. Therefore, the measurement model assessment results demonstrate that the research model has good reliability and validity, rendering it suitable for hypothesis testing.

4.2. Hypothesis Testing

The hypotheses were tested using the SPSS Process macro (v. 4.1) with the bootstrapping method to assess direct, indirect, and moderating effects. The results are shown in

Table 5. Results of the mediating and moderating.

Hypotheses	Path			Coeff	t	p	(LLCI; ULCI)	Result
Mediating effect:
H1	BDAC	→	INNO	0.220	2.864	0.005	(0.069; 0.373)	Supported
H2	BDAC	→	SUSINN	0.289	4.404	0.000	(0.160; 0.418)	Supported
H3	SUSINN	→	INNO	0.233	3.314	0.001	(0.095; 0.371)	Supported
H4	BDAC	→	SUSINN → INNO	0.067	-	-	(0.017; 0.133)	Supported
Moderating effect:
H5	OS × BDAC	→	INNO	0.165	3.750	0.000	(0.079; 0.252)	Supported
H6	OS × SUSINN	→	INNO	0.140	3.905	0.000	(0. 069; 0.210)	Supported

. According to Table 5, BDAC has a significant positive impact on innovativeness (β = 0.220, p < 0.01), sustained innovation (β = 0.289, p < 0.01), and sustained innovation has a significant positive impact on innovativeness (β = 0.233, p < 0.01), thereby supporting H1, H2 and H3. For the mediation effect test, the bootstrapping analysis results show that the confidence interval of the indirect effect of sustained innovation in the influence path of BDAC on innovativeness does not include 0 (BootLLCI = 0.017, BootULCI = 0.133), indicating that the mediating effect is significant, and hypothesis 4 is supported.

In terms of the moderating effect test, as shown in Table 5, the confidence interval for the moderating effect of organizational slack on the relationship between BDAC and innovativeness does not include 0 (BootLLCI = 0.079, BootULCI = 0.252), indicating that organizational slack exerted a significant moderating effect. Furthermore, as illustrated in Figure 2, the regression line slope of BDAC impacts on innovativeness is steeper under higher levels of organizational slack. It suggests that the influence of BDAC on innovativeness strengthens as the level of organizational slack increases. Thus, Hypothesis 5 is supported.

The test of the moderating effect of organizational slack on the relationship between sustained innovation and innovativeness reveals that the confidence interval does not include zero (BootLLCI = 0.070, BootULCI = 0.210) (see Table 5), indicating a significant moderating effect. Furthermore, as illustrated in Figure 3, the slope of the line representing the impact of sustained innovation on innovativeness is steeper under conditions of higher organizational slack. This suggests that the effect of sustained innovation on innovativeness is stronger when organizational slack is higher, thereby supporting Hypothesis 6.

Finally, as shown in

Table 6. Results of the moderated mediating effect.

	Moderator: Organizational Slack
		Effect	Boot SE	95% Boot CI (LLCI, ULCI)
BDAC→SUSINN→ INNO	Mean − 1 SD	0.042	0.033	[−0.012, 0.118]
	Mean	0.077	0.032	[0.023, 0.149]
	Mean + 1 SD	0.112	0.045	[0.029, 0.204]

, the test of the moderating effect of organizational slack on the mediating effect of sustained innovation showed that the 95% bootstrap confidence interval of the index of moderated mediation included 0 (BootLLCI = −0.012, BootULCI = 0.118), indicating that the overall moderating effect was not statistically significant. However, this did not indicate that organizational slack has no influence on the mediating process. Further analysis revealed that when organizational slack was at the mean level (BootLLCI = 0.023, BootULCI = 0.149) and above the mean level (BootLLCI = 0.029, BootULCI = 0.204) (see Table 6), the confidence intervals did not include 0, indicating that the mediating effect of sustained innovation strengthens as organizational slack increases. The result suggests that under specific conditions, organizational slack can significantly enhance the mediating role of sustainable innovation in the path of BDAC influence on firm innovativeness, thus supporting Hypothesis 7.

5. Discussion

BDAC exerts a positive influence on firm innovativeness. The research indicates that BDAC enhances firm innovativeness by supporting informed decision-making, strengthening operational capabilities and enabling firms to monitor customer needs and competitor actions in the dynamic business environment. These findings support prior evidence of the positive impact of BDAC on innovation [7] and enrich the research on how BDAC contributes to firm innovation outcomes by specifically focusing on innovativeness.

Sustained innovation mediates the relationship between BDAC and firm innovativeness. As the analysis shows, BDAC enables firms to transform data into knowledge and further into actionable insights. This encourages them to proactively reconstruct knowledge, allocate resources toward innovation, gradually accumulate long-term innovation capacity and drive sustained innovation. Sustained innovation, in turn, facilitates the continuous refinement of products and processes, fosters organizational learning and innovation routines, and ultimately enhances firm innovativeness. Prior research has primarily examined the effects of data resources on sustained innovation [21,22,66], the factors influencing sustained innovation [67], and the impact of sustained innovation on firm innovation [25], but have largely overlooked its mediating role in the relationship between BDAC and innovativeness. The results of this study complement the research on the mediating role of sustained innovation.

Organizational slack strengthens the direct effect of BDAC on innovativeness, the impact of sustained innovation on innovativeness, and the mediating effect of sustained innovation between BDAC and innovativeness. Specifically, organizational slack provides the flexibility required to acquire, allocate, and deploy resources, enabling firms to more effectively translate data-driven insights into innovative actions, sustained innovation efforts, and boost overall innovativeness. Thus, when firms possess strong big data capabilities, maintaining an appropriate level of organizational slack enhances the likelihood that these data capabilities will be smoothly converted into innovation outcomes. This finding confirms the positive role of organizational slack in the relationship between technology and innovation, aligning with prior research in the literature [68,69].

6. Conclusions

Drawing upon dynamic capability theory, this study investigates the role of BDAC in enhancing innovativeness, the mediating role of sustained innovation, and the moderating effect of organizational slack. Empirical findings confirm that BDAC significantly enhances innovativeness, with sustained innovation acting as a mediator in this relationship. Moreover, organizational slack positively moderates the direct effect of BDAC on innovativeness, the effect of sustained innovation on innovativeness, as well as the mediating pathway of sustained innovation.

6.1. Theoretical Implications

First, this study enriches the literature on the impact of BDAC on firm innovation. Regarding the impact of BDAC on firm innovation, prior research has mainly examined its effects on innovation performance [7,70,71], business model innovation [5], process innovation [15], supply chain innovation [16], and innovation capability [19], while largely overlooking its influence on innovativeness. Grounded in dynamic capabilities theory, this study examines how BDAC enhances a firm’s innovativeness, addressing the gap in the existing literature. In doing so, it also expands the research on the role of BDAC in driving innovation and broadens the theoretical scope of the dynamic capability literature.

Second, this research advances the understanding of the mechanisms through which BDAC fosters innovativeness. Existing empirical studies have shown that BDAC enhances firm innovation indirectly through mediators such as business model innovation, learning capability, market offering flexibility, dynamic capability, and organizational agility [23]. However, the mediating role of sustained innovation has not been sufficiently addressed. By introducing sustained innovation as a mediating variable, this study emphasizes that BDAC not only drives innovativeness through its technological advantages but also enhances it by supporting sustained innovation efforts and outcomes. Thus, this study broadens the understanding of the pathways by which BDAC drives innovativeness.

Third, this study extends the research on the boundary conditions under which BDAC influences innovativeness. Existing studies on the boundary conditions influencing the relationship between BDAC and firm innovation has primarily focused on factors such as market orientation [72,73], digital platform capabilities, environmental factors [74], and enterprise architecture [24], while the role of organizational slack has received limited attention. Moreover, the extant literatures present conflicting views regarding the role of organizational slack on innovation [75]. This research incorporates organizational slack as a moderating variable to examine its role in the relationship between BDAC and firm innovativeness. The results demonstrate that organizational slack plays a significant positive moderating role in three key relationships: between BDAC and innovativeness, between sustained innovation and innovativeness, and in the mediating effect of sustained innovation between BDAC and innovativeness. These findings broaden the research on the boundary conditions of BDAC’s impact on firm innovativeness.

6.2. Practical Implications

First, BDAC plays a critical role in enhancing firm innovativeness. Managers should therefore treat the development of BDAC as a strategic priority for fostering innovation. To achieve this, firms should invest in advanced analytics technologies, establish robust and efficient data pipelines, and cultivate a skilled workforce with expertise in data science and analytics [76]. Given that big data analytics is an iterative process, firms should also enhance their data governance frameworks and decision-making systems to ensure that data insights are consistently captured, interpreted, and applied. This includes developing feedback loops between data analysis and execution, improving data quality management, and establishing cross-functional teams to convert insights into concrete innovation initiatives. Additionally, fostering a data-driven culture is essential to ensure the continuous generation of innovation. Firms should embed analytics into both strategic planning and daily operations, align performance evaluation with data utilization, and encourage all levels to incorporate data into their decision-making processes.

Second, the findings demonstrate that sustained innovation mediates the relationship between BDAC and innovativeness. This highlights the need for firms to develop and reinforce their capacities for sustained innovation in order to unlock the full innovation potential of BDAC. Specifically, firms should integrate BDAC into long-term innovation strategies, emphasizing iterative experimentation and feedback-driven learning to gradually build innovation advantages. To facilitate the stable transformation of insights into innovative outcomes, firms should embed data insights into innovation processes by establishing cross-functional collaboration mechanisms and robust knowledge management systems. Moreover, a supportive internal environment is essential. Firms should shape an internal environment that supports innovation continuity and dynamic adaptability through effective resource allocation, innovation-oriented culture, and employee incentives, ultimately enhancing innovativeness. For example, offering recognition, career development opportunities, or performance-based bonuses tied to long-term innovation outcomes can motivate employees to engage consistently in innovation efforts.

Lastly, the research results show that organizational slack positively moderates the effects of BDAC and sustained innovation on innovativeness, as well as the mediating role of sustained innovation. From the resource management aspect, this suggests that firms should avoid adopting overly rigid, technology-centric resource allocation strategies. Instead, they should maintain a certain level of slack to allow flexibility in the face of market and technological uncertainty, such as financial buffers, human capital reserves, and excess production capacity. To enhance the promoting effect of big data resources on enterprise innovation, firms should focus on constructing a synergetic system integrating big data capabilities and resource slack. For instance, when big data analytics identifies an emerging trend or customer need, available slack resources can enable swift and effective responses, accelerating innovation implementation. Additionally, managers should also assess the optimal level of organizational slack from a strategic perspective, regarding it as an invisible safeguard for enterprises to tackle dynamic market and technological uncertainties, rather than mere resource waste.

6.3. Limitations and Future Recommendation

While this study makes significant contributions to the literature, there are three limitations. First, the use of cross-sectional survey data limits the ability to infer causal relationships, as it does not capture the temporal sequencing of variables. Despite the inclusion of control variables to mitigate potential endogeneity concerns, the absence of longitudinal observations restricts the robustness of causal interpretations. Future studies should adopt longitudinal designs or natural experiments to strengthen the robustness of causal inference. Moreover, this study conceptualizes sustained innovation as a mediator in the relationship between BDAC and innovativeness. Future research could expand on this by investigating alternative mediating factors such as strategic flexibility and strategic planning [23], improvisational capability [17], and organizational ambidexterity [70], to better capture the dynamic processes through which BDAC enhances innovation outcomes. Additionally, the sample was drawn from Chinese firms, which may limit the generalizability of the findings. Future research could examine how Chinese cultural effects may affect the relationship between BDAC and innovativeness.