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Organizations must employ big data analytics to maintain sustained innovation in the highly dynamic and evolving business landscape. Even though BDA has a transformative power to revolutionize how businesses do things and engage with their customers’ adopting BDA has faced significant challenges, especially in developing countries. This research aims to create a theoretical framework to understand how organizational readiness for BDA can influence sustainable innovation performance. Sampling errors were mitigated through a time-lagged study design, and the data was collected in three phases. The test results using Partial Least Squares Structural Equation Modeling show that organizational readiness is a critical mediator, establishing a robust chain between BDA skills and sustainable innovation performance. The results of this study imply the need for organizational foundation and alignment, which are critical to the compelling strategic deployment of BDA for sustainability innovation performance. Thus, this study can offer a valuable contribution to this topic in the future and a profound implication of the phenomenon at receptive stages.
Introduction
Big Data Analytics Capabilities (BDAC) are essential in the digital age to foster innovation and sustain a competitive edge in modern business. With advanced technologies permeating various industries, data has emerged as the lifeblood of companies. Firms that fail to embrace this trend risk falling behind. BDAC is now a critical focus for managers worldwide, as numerous studies highlight its importance in decision-making.
Understanding the nexus between BDAC and sustainable innovation is complex, intersecting economic prosperity with environmental preservation and societal well-being. Developing economies face unique challenges in leveraging BDAC for sustainable innovation (Chan et al. 2022; Wamba et al. 2020). “This study explores how organizational readiness influences BDAC’s strategic and innovative use. We investigate the factors affecting an organization’s readiness, including managerial culture, creative capacities, technological underpinnings, and human capital, and how these factors enable BDAC to drive sustainable innovation.
In this context, “sustainable innovation performance” refers to economically worthwhile, socially responsible, and environmentally sound breakthroughs. These innovations provide long-term solutions aligned with performance measurement metrics. While significant strides have been made in using BDAC across various industries, its potential remains, especially for companies in emerging economies on the brink of economic development (Egwuonwu et al. 2023).
Our study trials the fundamental assumption that organizations are inherently ready and fully capable of utilizing BDAC. The necessary conditions for successful integration should be addressed. Our research aims to bridge this gap by providing a theoretical framework around organizational readiness and its influence on sustainable innovation. The combination of the potential and technical domains with the operational realities faced by today’s organizations (Arshad et al. 2022; Muhammad et al. 2022).
The Questions Raised by this invention are in the gap left by accumulated studies.
How does organizational readiness affect sustainable innovation in developing economies?
Are management adaptability, technological foundations, and personnel capabilities capable of driving the effective use of BDAC for sustainable innovation?
The Objectives of the study are as follows.
To quantify the effect of organizational readiness on the effectiveness of sustainable innovation in developing economies.
To investigate the connection between management adaptability and the utilization of BDAC for promoting sustainable innovation.
To examine the role of technological infrastructure and competence in organizational readiness for BDAC.
To assess the impact of human resources management policies on organizational readiness to use BDAC for sustainable innovation.
Our study makes two key contributions:
From a scholarly perspective, it fills a void in the existing literature by examining the mediating role of organizational readiness between BDAC and sustainable innovation performance.
In practice, it illuminates the challenges of implementing BDAC in developing countries and identifies opportunities for addressing these challenges.
By addressing these gaps, our research offers practical advice to firms and policymakers in emerging economies on using BDAC sustainably, focusing on management methods, technological foundations, and human resources endowment.
The evolution of theories and hypotheses
Theoretical underpinnings
BDAC’s resource-based view
RBV posits that firms contain inventories of resources, which can be either tangible or intangible. These resources are crucial for a company to attain a competitive edge and improve its performance. As a result, Melville et al. (2004) attributed technology to performance as impacting a firm’s tangible resources and assets. Furthermore, Barney (1991) argued that the firm obtained personnel competencies, an intangible term. Amit and Zott (2017) and Arshad et al. (2022) explained that such factors could be nurtured more productively into productive organizational capabilities and competencies, resulting in value creation.
This inquiry uses the RBV framework to investigate the phenomenon of BDAC administration, technology, and talent as a critical organizational capability in determining an entity’s readiness and its bias toward continuous innovative output. A mediating construct, organizational readiness, amplifies all the effects of BDA assimilation on sustainable innovation performance. Organizational readiness can influence how human and financial resources and technology are allocated for business process analysis. Moreover, increased excitement would correspond to better resource allocation and analytical proficiency.
Theory of information processing and BDAC
The Information Processing Theory implies that, much like how people use the brain to process information, businesses use the concept to help in decision-making and make the world less ambiguous. The implementation of the theory to BDAC shows that the company can manage its data, make better decisions, and improve initiative. Tushman and Nadler (1978) propose that the amount of information processing a firm can effectively perform to its ability to adapt meaningfully should be matched with environmental pressure. Recent findings by Mikalef et al. (2021) prove and expand the notion of the harmony present in BDAC. The data processing skills, combined with an organization’s readiness, can lead to a higher ability to respond to the needs and innovate.
Organizational change readiness theory and BDAC
The psychological and behavioral characteristics of an organization’s change readiness are reflected regarding leadership support and resource allocation. The insights provided by the theoretical framework developed by Tushman and Nadler (1978) help distinguish the link between sustainable innovation performance, organizational readiness, and big data analytics. By the dominant logic of organizational readiness for change, which includes cultural characteristics, leadership support, and employee involvement, the company’s chances of implementing and passing change initiatives are more successful when the company is ready.
In evaluating organizational change readiness, Weiner emphasized the role of behavioral and psychological approaches (Weiner, 2009). Later, Holt et al. (2017) made this perspective more precise in modern digital transformation initiatives, where transforming and accelerating business only sometimes correspond to a technical change as commonly perceived and explained. In the digital world, businesses should be ready to implement big data analytics and cloud computing in technical and cultural respects.
BDA’s management capability and sustainable innovation performance
Big Data Analytics is a big enabler and has established itself as the foundation for any organization to survive, transform, or innovate in this digital transformation age, which changes quickly. This area highlights the need for appropriate and relevant BDA usage as a means of managing and enhancing the scope of an organization’s adherence to sustainable innovation performance, a complex approach involving growth, environmental sensitivity, social responsibility, and financial stability.
A density narrative emerging from current academic dialogues underscores how BDA can catalyze innovation and an integral foundation for shaping the boundaries of a digitally sustainable organization. Instantly, core competencies in BDA embrace a robust data governance framework, more vital analytical capabilities, advanced strategic decision-making skills, and an increased deployment of analytics to support innovative activities Mikalef et al. (2019). Similarly, Schiederig et al. (2012) argued that BDA is established by the conceptualization of as it supports sustainable innovation, which focuses on radical innovations, including new products, services, and processes that achieve an organizational purpose besides conserving the environment and natural resources.
Recent findings confirm that the effect of BDA-shaped sustainable innovation performance paths is becoming stronger and more evident. In this regard, Mushtaq et al. (2022) argue that an establishment immersed in data literacy via a data-centric culture and empowered analytical tools stimulates product innovation according to mature consumers’ integrated expectations and market drives. Dubey et al. (2019) study supply chain optimization trends from the perspective of BDA-powered processes and point out the benefits of process innovation and environmental and financial sustainability. Guo and Chen (2023) also support the integration of BDA competencies by claiming that it fosters the creation of a favorable ground for a knowledge-based organizational culture that is innovative and creative.
Nevertheless, Fosso Wamba et al. (2024) stressed that the way to capitalize on BDA for sustainable business is to assimilate BDA into sustainability-driven strategic achievements, emerging as a critical vector of innovative progress. The potential for BDA combined with the imperative of sustainability is exciting, and it is exciting to see companies leverage their way out of ancient social dilemmas and into a brave new world. Ren et al. (2022) provide useful insights about the challenges suggested by our case studies of innovation. Addressing data guardianship issues, the need for BDA specialization skills, and how to neatly integrate BDA into all existing organization-wide schemas: An inquiry Fighting back against these nemeses warrants a posture that includes an unyielding commitment to lifelong learning, trans-disciplinary collaboration and believing that a data-first organization can be won. The bottom line depicts an image of what it means to be a smart business decision - from conception through analysis and visioning by looking into the future, as well as smart investments strategically protected with wise stewardship. The lights of business rewards and potential profit beckon the way for strategic growth in BDA farms Barton (2016).
Consequently, if a firm has already erected the ramparts of extensive data infrastructure, it stands to benefit significantly in terms of competitive advantage and economic strength. Coordination with BDA, one of the characteristic aspects of the big data ecosystem, shows how organizational functions combine and debate issues of resource organization (Muhammad et al. 2022; Arshad et al. 2022). Good data stewardship nurtures resilient collaboration networks with stakeholders, generating value in enterprise pursuits (Lombardi et al. 2022). Li et al. (2018) have given the recognition that the potential for a firm’s innovation lies in synergistic collaborations, not confined by organizational silos but going beyond them to encompass multiple stakeholders’ interactions Fig. 1.
Fig. 1 [Images not available. See PDF.]
Conceptual framework of the study.
This conceptual framework represents the hypothesized relationships among five key constructs.
Therefore, we anticipated that H1: There is an association between BDA management and enhanced sustainable innovation performance.
BDA technology capability and sustainable innovation performance
In this digital age, technology is an enabling force that contributes the necessary technology components to Big Data Analytics and facilitates sustainable innovation. More precisely, BDA consists of multiple transactions (collecting data and storing it properly to support a required processing analysis) facilitated by IT systems and infrastructure supported by analytical tools or innovative software solutions (Mikalef et al. 2018). From an HR perspective, firms’ ability to leverage BDAT must filter into how they make decisions strategically and spur innovation that is not limited to economic value, targeting essential environmental and social problems (Wamba et al. 2017).
Firm competence is classified as sustainable innovation performance based on reducing carbon footprint, enhancing energy efficiency, and improving society’s well-being (Dong et al. 2024). Firms will offer valuable data regarding the market, customer behavior, business best practices, and processes, which will support intelligent decision-making and direct sustainable innovation trends using BDAT’s elevated predictive analytics application (Jabbar et al. 2020).
The use of BDAT crucially contributes to the efficient utilization of resources and minimizing adverse environmental outcomes. Ren et al. (2021) state that it helps to progress processes and items that fit the standards of ecological sustainability more closely. Chen et al. (2022) also argue that BDAT contributes to aligning new product and service development, noting sustainability for stakeholders involved in the process, such as customers, employees, or society. And yet, while BDAT capabilities could be very useful to innovation, many organizations require assistance embedding those capabilities into their innovation flows. Challenges in Data Security, Skilled Workforce for the Industry, and Big Data Analytics and Technologies must align with Sustainability Management (Talwar et al. 2021). Resolving these issues requires tracking ethical data practices, constant professional development, and promotion of the culture of organizational sustainability.
Another way big data technology can boost innovation is through the empowerment of data analysts in developing blueprints. Generally, this is achieved by making data more accessible and interoperable and making models more readily available. Supported by Court and Barton, the three fundamental principles for creating big data technology infrastructure are connectivity, compatibility, and modularity. According to Barton and Court (2012), applying connectivity and compatibility will empower companies to master technology and handle vast amounts of data quickly exchanged between their disciplines. The presence of compatibility is supported by Davenport et al. (2007). They will help all decision-making activities while maintaining a high-quality information flow.
Based on the extensive body of evidence presented, we can formulate the following proposition:
H2: The impact of technological competencies positively related to big data on an organization’s sustainable innovation performance.
BDA talent capability and sustainable innovation performance
The Big Data Analytics Talent Capabilities (BDATL) framework includes several key professional competencies, from data stewardship, analytical capabilities, and critical thinking to business acumen. As per Davenport (2014b), these skills allow you to bridge the gap between data insight and strategic tasks needed for continuous innovation. Shollo and Galliers (2024) studied the BDATL capabilities of firms that drive innovation on sustainability. Their results indicate highly innovative capabilities from chance case detection to exploitation opportunities. Adam and Kevin (2024) state that sustainable innovation performance is key to company success in the long term, as well as ecological stewardship and fair socioeconomic development.
It covers developing new products, services, and processes that support economic growth and advance environmental sustainability and social equity. These can be turned into profit-making services in a business’s operation. They rely on data analytics to inspect the damage they caused and look for ways to limit it using an enormous BDATL framework.
As a result, answers have been found to simultaneously enhance data efficiency and improve organization and environment sustainability (Opoku et al. 2022). Once BDATL aspects have developed the skills to use predictive analytics and data mining techniques effectively, they can be crucial in predicting sustainability trends by giving valuable insights into an organization’s innovation strategy (Wiengarten et al. 2021). According to Hartmann et al. (2023) and Dubey et al. (2019), BDATL helps create a corporate culture emphasizing social responsibility and environmental stewardship. This approach demolishes organizational barriers and promotes the development of comprehensive, cross-functional cooperation.
BDA talents are hard to come by, and the rapid pace of technological change only makes them more challenging, so a strategy is required for constant learning and adaptation. Ranjan and Foropon (2021) commented that integrating BDA insights with sustainable innovation imperatives is now a necessity of Business Culture, pushing every business to operate on its merit.
The innovation life cycle includes various aspects like creation, collaboration, and continuity in expanding corporate innovation. All three sectors need to be connected seamlessly through an integrated mode of communication (Lu et al. 2020). According to Del Vecchio et al. (2018), the ambitious skills and capacity of individuals proficient in big data may significantly affect an organization’s capability for innovation and sustainable competitive advantage. So, van den Broek and van Veenstra (2018) stress the importance of fostering a collaborative culture to enhance innovation performance. Big data-innovated businesses could receive input from various sources to help them develop and refine new concept ideas. In this context, they can now identify investment opportunities worth their investment (Arshad et al. 2022; Lozada et al. 2023).
Considering the above analysis, we propose the following hypothesis for empirical investigation:
H3: A positive and direct correlation exists between the proficiency of considerable data talent and an organization’s sustainable innovation performance.
BDA management and organizational readiness
Big Data Management Capability (BDMC) is a prerequisite for enterprises’ effectiveness in making well-informed decisions. It indicates the leadership potential of transformation with a nod to (Fosso Wamba et al. 2024). Big Data Analytics and Management of Large Data (BDMC), based on modern technical infrastructure combined with powerful data governance frameworks, processing quality analysis tools, and efficient interoperability so as not only to handle large quantities of data but all the while remain stable. BDMC is vital in helping enterprises adapt to different environments as the economic landscape changes (Akhter et al. 2021).
An organization’s readiness for large-scale data projects has three crucial components: culture, Technology, and strategy. Participation in significant data initiatives is closely linked with the interrelationship of a company’s technological resources, its data-friendly values, and what measures it takes to connect them to broader business goals (Chen et al. 2022). So, combining these three elements is essential in overcoming issues related to large-scale data projects standing in front of considerable benefits obtainable from BDA (Baig et al. 2021).
A technical infrastructure is a prerequisite for supporting BDMC and ensuring organizational readiness. For big data projects to succeed, they need enough storage space and the large-scale analytics tools they need (Gupta et al. 2020). Moreover, a data-driven corporate culture is evidence of readiness and strategic alignment for big data projects and can help them succeed. BDMC is a part of this culture (Davenport, 2014a). Privacy fears, security concerns, rapid technological change, and opposition from the company around you all work against BDMC fortification and big data readiness. Breaking these barriers to foster innovation, improve operational efficiency, and run a competitive advantage is critical.
To address the general problems encountered in great information applications, the study emphasizes the need to reinforce BDMC and organizational readiness. However, as Muhammad et al. (2022) research shows, any business that puts effort into constructing BDMC and the right attitude towards data will only unstintingly find itself shifting towards historically new forms of revenue center. In addition to being the necessary precondition for accessing large data sets, a strong BDAM demonstrates that an organization is willing to take risks that might increase operational value (Kim, 2019; Arshad et al. 2022).
Big data analytics management capability is a measure through which businesses can see what kind of work does well and foresee consumer trends and market opportunities (Sonka, 2016). Indeed, you can even extrapolate this further to say that BDAM is a company’s preparation for competition in the market, be it within space itself or trying one’s luck at countless other things that are equally difficult to imagine.
We derive the following hypothesis from these insights and propose it for empirical validation:
H4: A highly prepared firm is more likely to have a robust big data analytics management capability, which creates an atmosphere favorable to sustainable innovation performance.
BDA technical capability and organizational readiness
Big data readiness, or an organization’s ability to accept and use big data technologies and methods, is critical for today’s corporations. Leadership endorsement clearly stated company goals and a data-centered corporate culture where the whole business has moved towards transparency all contribute to big data proficiency in the workforce (Al Hakim et al. 2021). In addition, organizational readiness is critical when preparing for large-scale data use. This is because of the challenges associated with big data adoption, such as change aversion, inadequate resources or data compartmentalization, and the failure to integrate big data activities into organizational processes snugly (Chen et al. 2018).
BDAT, therefore, provides organizations with the technological infrastructure and tools necessary for big data projects. The technical skills of competent enough people are the basis for rapid storage, processing, and analysis of large volumes of data, of which an environment conducive to data-driven decision-making can grow (Gupta et al. 2021). These competencies enable firms to pioneer a data-driven culture and align big data activities with an organization’s strategic goals, demonstrating readiness (Davenport, 2014a).
On a different front is BDAT ‘s role aims to prepare organizations for big data engagement by imparting skills oriented toward their needs. Now, companies can send their staff out for training in big data work with the newest data analytics tools and technologies. Consequently, the adoption and use of big data are inextricably connected with BDAT and the broader concept of organizational readiness. In this setting of its significance, BDAT appraised against organization readiness has become a method of enlarging the frontier (Onukwugha et al. 2017). Rehman’s et al. (2022) perspective on producing BDAT success stories emphasizes the smooth integration of new technologies with other existing systems. Some tensor data can be successful for big data because of its value addition (Peng et al. 2022).
Moore (2020) indicated that data maturity models and the big picture from roadmaps will provide depth. These complement the large data analysis skills and a more formal definition of where another artificial intelligence technology could be starting strategically. Additionally, they allow for scaling out more broadly across data analytics, which is considered an enterprise orientation (Halper and Krishnan, 2013). Identifying hotspots for big data capacity assessment will help guide that behavior away from those traps and toward growth (Rafferty et al. 2018). Queiroz et al. 2021 emphasizes factors such as infrastructure, data management methods, research capabilities, governance standards, and technology abilities when assessing an organization’s readiness.
Combining results from these attributes, we propose the following hypothesis:
H5: Organizational readiness positively relates to the Big Data Technical Capability (BDAT).
BDA talent capability and organizational readiness
Big Data Talent Capability is a collection of skills and abilities that inform how good an organization is at producing big data people (George et al. 2016). This includes the skills to analyze and manipulate data; one must know how to create statistical models; one needs programming knowledge and should be a field expert. BDATL thus becomes significant in strategic decisions, innovation of technological advancements, or competitive advantage (Mikalef et al. 2018). Terminologies such as organizational readiness are related to big data, a different dimension of scoring or measurement where an organization can take and utilize effective insights from big data. An organization’s preparedness leans so much on healthy leadership, consistency with the organizational culture, availability of infrastructure, and policy implementation through effective governance, as well as routine staff training (Al Hakim et al. 2021; Chen et al. 2022). BDATL is instrumental in preparing businesses to work within the big data domain. This brings the far more important purpose of accrediting new technologies and specialist staff while developing a big data proficient workforce by preparing all tools (Mikalef et al. 2018). An organization’s desire to make data-driven reactions is a sign of maturity. The constitution of an analytics-driven culture creates technical innovation and deepens big data applications within companies (Davenport, 2014a).
In addition, BDATL personnel are also aware that their task includes such matters as helping the organization to get ready, beefing up the leadership efforts and strategic objectives of senior management, and ensuring that big data is woven into the texture of the organization itself (Kiron, 2013). Furthermore, BDATL is responsible for building the necessary platform and governance processes to manage data quality, privacy, security, and compliance; doing so requires a skilled design and execution force for implementation (Gupta et al. 2020).
Modern data is an analytical raw material critical for coping with masses of information and needs to be turned into a refined product like any other material. It requires various talents, including technical and technological management, business acumen, and relationship management. This set of competencies is critical because it can foster technical staff capabilities and the use of advanced tools by individual data analysts in strategic resources assessment (Clark, 2020).
Similarly, big data expertise is critical to strategic long-term planning and technological skill empowerment for commercial opportunities. The leadership is committed to creating a culture that adopts and absorbs new technologies to help bring innovative ideas faster (Lombardi et al. 2022; Chen et al. 2022). According to the Resource-Based View (RBV), a firm’s talent and specialized skills are essential in boosting ability and cultivating an innovative cultural atmosphere.
Given the above discussion, we formulate the following hypothesis for empirical examination:
H6: Big Data Analytical Talent Capability (BDATL) improves organizational readiness, creating an atmosphere conducive to sustainable innovation.
Organizational readiness for sustainable innovation performance
Organizational capacity refers to a company’s aptitude to handle and capitalize on changes effectively. It is like leadership support; its resources are accordingly allocated, and worker loyalty towards management directions and culture within the organization forms this category and influences the direction of company development (Gamache et al. 2018). This coupled multimode adapt opportunity is crucial for embracing new methodologies, fusing technology in the process of emerging out from its cocoon, and an ability to speed up innovation (Abouei and Ghasemaghaei, 2020). Organizational readiness is also essential for making long-term strides in innovation. This kind of innovation does not just start being practiced today; a Poor environment can touch the whole society and everyone with it (Dong et al. 2024).
In the long course of gaining its long-term benefit, sustainable innovation performance is subject to an organizational capacity, dependent on organization readiness. This calls for robust capabilities in the social and environmental spheres as well. Long-term sustainable development is implemented in an organizational context. Due to the importance of corporate leadership as shaped by institutional role models, this codex has never been questioned by the authors or refuted by empirical research (Walker et al. 2014). Organizational readiness for sustainable innovation comprises company infrastructure, technological resources, and human capital needed to develop, research, and others (Kramer and Porter, 2011). Only when a company is richly endowed with assets can it solve problems and create new sustainable production methods. A workplace culture that encourages worker involvement and empowerment typifies organizational readiness so that innovative products are born of shared ownership and a creative climate (Jabbour et al. 2013). Organizational readiness is one lynchpin of dual demands from within an organization: continuous innovation and sustainable improvement. Hence, firms with an inherent inclination towards learning are highly inclined to embrace sustainable innovation, driven by the desire to investigate and gain insights from successes and failures (Dodgson et al. 2008).
Organizational readiness is an ever-shifting feature that helps firms respond quickly to new sustainability issues and take advantage of opportunities to be more innovative. Baker et al. (2016) states it is volatility in the ability or willingness to change, development, and entrepreneurial spirit. Aboelmaged (2014) defines organizational readiness in the context of change management as the will and tools needed; Zhu et al. (2018) say an organization’s readiness can be judged by its ability to absorb new technologies into existing human capital and infrastructure. According to Chwelos et al. (2001), readiness also predicts the degree to which an organization will derive benefits from technologies in practical terms. Structural and psychological readiness are regarded as crucial qualities of an organization (Shahrasbi and Paré, 2014), with structural readiness to adopt new technologies critically (Lee et al. 2015)
To use technology effectively and be competitive, innovative, and sustainable in performance, the organization must clearly understand what it is ready for and how it translates. Product- and service-led industries (Srinivasan, 2020) require dynamism, which is at the core of the essence of these industries, such as hospitality. Based on information processing theory, firms with an appropriate receptive environment should be able to most effectively analyze and capitalize off the BDAC big data streams provided. And it leads to enhanced sustainable innovation performance. Therefore, BDAC’s implementation success and benefits rely on a robust technological infrastructure and organizational cultural framework based on the theory of organizational readiness for change. The following hypothesis is developed from the discussion above:
H7: A significant direct relationship exists between an organization’s readiness and sustainable innovation performance.
BDA, organizational readiness, and sustainable innovation performance
As we have seen in this context, an organization’s readiness is the general capacity to go out and adapt, create, or even survive. The type of readiness described here corresponds to an organization’s ability to implement change programs and sustain innovation - a core economic, social, and environmental sustainability activity. This examines how organizational preparation affects sustainable innovation performance. Leadership support, resource availability, staff dedication, and organizational culture are all critical components of organizational readiness since they contribute to a company’s resilience and adaptability in the face of change (Gamache et al. 2018). Readiness within a company serves as a barrier to resistance to change, allowing for the acceptance of new processes and technologies while encouraging innovation initiatives (Armenakis and Harris, 2002). Sustainable innovation performance, which focuses on developing and implementing creative initiatives that increase economic prosperity while solving critical social and environmental concerns, depends on an organization’s readiness (Schiederig et al. 2012). Long-term organizational growth, competitive posture, and reduced socio-environmental consequences depend on achieving sustainable innovation performance. Such success is contingent on organizational preparation, in which leadership support and vision play critical roles. Sustainable innovation, driven by leadership that encourages experimentation, risk-taking, and creative thinking, ensures progress toward sustainable development (Sarkis, 2020). Investment in sustainable innovation, from research to product and process improvements, requires financial, human, and technological resources (Porter et al. 2020). The combination of substantial resources and an innovative culture emphasizes an organization’s ability to overcome obstacles and provide sustainability-focused solutions. Organizational readiness, employee empowerment, and invigoration also strengthen ownership of collective and sustainable innovation efforts (Jabbour et al. 2020). Sustainable innovation performance can be expanded with inclusive decision-making and opportunities for professional development, recognition, and reward systems consistent with innovation. By continuously refining one’s practices, observing newly developing situations around oneself, and learning all the time, organization readiness becomes critical to such output. Organizations with a learning orientation, which embraces experimentation and draws lessons from their successes and failures, have a higher tendency for sustainable innovation performance (Dodgson and Gann, 2023).
Individual and social preparation is critical for significant change (Reynolds, 2021). Change readiness is multidimensional and can be measured at several levels within the company. This detailed approach is crucial for understanding and assessing readiness (Rafferty et al. 2013). The development of big data roadmaps and maturity models, which serve as critical instruments for successful BDA deployment, emphasizes big data analytics (‘s significance in encouraging innovation (Moore, 2020). Maturity models are frameworks for advancing BDA efforts, offering benchmarks, and assisting with risk reduction (Greene et al. 2016). Braun and Esswein (2007) evaluated numerous data maturity models, concluding that the best frameworks include organizational structure, technology architecture, data management, analytics, and governance. These elements are essential for measuring an organization’s BDA readiness. Effective organizational change management aligns people, processes, culture, and strategy to help organizations move to new technological paradigms (Arshad et al. 2022). Before successful innovation, there must be a shift in cultural ethos, accountability frameworks, resource distribution, and the development of necessary skill sets.
Drawing from an information processing perspective in addition to the theory of organizational readiness for change, this integrative framework posits that BDAC can contribute toward improved decision-making. This is especially true when more innovative ideas are translated into specific policies. This partnership only benefits when the organization is willing to know and engage its IT department with the learnings of BDA and its technical capability concerning infrastructure (Maroufkhani et al. 2020; Gangwar, 2018).
From this synthesis, we propose the following hypotheses.
H8: Organizational readiness mediates the positive relationship between BDA management capabilities and sustainable innovation performance.
H9: Organizational readiness mediates the positive relationship between BDA technical skills and sustainable innovation performance.
H10: Organizational readiness mediates the positive relationship between BDA talent abilities and sustainable innovation performance.
Methodology and process
This study obtained data from Pakistan’s National Database Registration Authority (NADRA), the primary government organization responsible for data registration, and National ID. NADRA’s official investigative structure is highly rigorous, so adhering to ethical principles throughout the process was crucial. The ethics committee of Yunnan University of Finance and Economics granted formal consent, ensuring all study techniques adhered to the highest ethical standards.
The approach employed for this study was a wide range of questionnaires, which were devised to gather data not only on the readiness of the companies but also on whether they changed anything and what led to their computing technology. People working as administrative staff in finance and data processing for NADRA were the primary target audience for this study. Potential participants were further required to express consent. To guard the anonymity of respondents and thereby alleviate perceived biases, informed consent was appended to the questionnaire in recognition of the possible biases brought out by Podsakoff et al. (2003), the desire for social confirmation, and nervousness.
With gratitude for its vast network of 18,000 employees and 800-plus data collection sites, NADRA was founded to coordinate the overseas identity of Pakistani people and bring national data onto a single line. The sample included managers, monitors, checkers, and data center leaders. Although constructing a complete frame was nearly impossible, we drew purposeful sampling to ongoing ON-SITE activities for recruiting researchers. By contrast, recent developments suggest that researchers should conduct power research to calculate enough observations. Power analysis helps us determine the smallest sample size we can use by always referring to the part of a model with many predictors (Hair et al. 2019).
Before the software calculates the required sample size for a research study, the researchers must provide details regarding the effect size, significance level, and potency. Different statistical software is available to calculate sample size through power techniques, such as G* Power, SPSS Sample Power, or SAS Power, of these, but also the most recommended ones by scholars in business and social sciences for their scientific research credibly (Hair et al. 2019; Sarstedt et al. 2016). Through G* Power, we calculated 129 to be the smallest number of study subjects, but data was collected from 255 respondents.
Measurement of variables
This study measured the Likert scale using a 5-point scale ranging from (1 = strongly disagree) to (5 = strongly agree). Table 1 presents data on the measurement of variables, sources, the quantity of items, and sample items utilized in this investigation. When addressing issues related to age, gender, education, and employment experience, it is important to consider the following control factors. This survey also assessed the social standing of the individuals who participated (Wamba et al. 2017).
Table 1. Measures of the study variable.
Variable | Adopted from/year | Number of items | Example item |
|---|---|---|---|
BDA Management | Datta and Malhotra’s (2015) | 16 | Does your business continuously seek new chances to strategically apply Big Data Analytics (BDA)? |
BDA Technical | Vidgen et al. (2017) | 12 | The software applications in your organization can be effortlessly integrated with numerous analytics platforms. |
BDA Talent | Byrd and Turner (2000), Gupta and George (2016) | 16 | The data management capabilities of the analytics personnel at your company are noteworthy. |
Organizational Readiness | Yen et al. (2012) | 07 | Our organization has the necessary technological infrastructure to support BDA. |
Sustainable Innovation Performance | Chen et al. (2006) | 08 | Our processes and operations have become more sustainable due to recent innovations. |
Process
A longitudinal design utilizing data gathered over three waves was adopted to test our model of first authorizations and respondent identifications. This allows us to detect temporal trends and corroborate the credibility of any research findings by being consistent over time, which varied in replication at different points across research. The independent variable (T1: BDAC) concerns companies, the mediator variable (T2: Organizational Readiness) is a social cognitive factor, and the penultimate dependent variable (T3: Sustainable Innovation Performance) concerns companies.
A phase delay of 4 weeks was maintained throughout to ensure temporal clarity and consistency. To ensure the continuity of participant traceability throughout all rounds, data collection was initiated in December 2021 with the first authorizations and respondent identifications, all of which had been carefully synchronized. Participants were given a unique ID associated with their data center and employee number. This ensures the consistency of the collected data set (Rafique et al. 2021).
Five hundred self-administered questionnaires were handed out to senior executives during the study’s first phase: these posed queries explicitly targeted big data analysis and demographic variables. Of these, only 385 respondents (77%) were received. However, the interweave sent out during the first wave also managed to reach about as many people as were gathered, out of which 314 underwent a strict usage test successfully for second phase, 81%. The final phase, in which sustainable innovation outputs were evaluated, achieved a response rate of 81%. With 255 valid returns, we can now say that the response rate over this period amounted to 51%. Table 2 displays the demographic characteristics of the participants included in the study.
Table 2. Demographic information.
Characteristics | Frequency | Percentage | |
|---|---|---|---|
Gender | Male | 162 | 64% |
Female | 93 | 36% | |
Age | Less than 25 | 46 | 18% |
26–30 | 98 | 38.4% | |
30–40 | 79 | 31% | |
41 and above | 32 | 12.6% | |
Education | Undergraduate | 59 | 23% |
Graduate | 148 | 58% | |
Postgraduate | 48 | 19% | |
Experience | 1–4 years | 91 | 36% |
5–10 years | 136 | 53% | |
11 years and above | 28 | 11% | |
Working position | Managerial level | 103 | 40.3% |
IT | 87 | 34.2% | |
Data processing | 65 | 25.5% |
Note: The values in bold are the highest percentage
Our study aimed to describe and quantify the intricate traits of BDA skills. These covered three dimensions (managers, technologists, and talents). Four fundamental parts have been opened to actualize the concept of BDA management ability: strategic planning, investment, coordination, and control. The elements, which cover a range of other than administrative dimensions, were affected by influential literature in this sub-field (Arshad et al. 2022; Ravichandran and Keikhosrokiani, 2023; Muhammad et al. 2022). Technological capability within Big Data Analytics (BDA) was conceptualized as interrogated, compatible, and modular. The talent assessment in Big Data Analytics (BDA) was around four areas: technology management and application, technical competence, commercial acumen, and customer relations. Some of these capabilities were supported by recent empirical studies of scholars like Wamba et al. (2017), Ravichandran and Keikhosrokiani (2023), and Arshad et al. (2022).
The complex models composed of reflective and formative constructs were calculated using the well-established model-building technique SEM-PLS. Our exploratory study is well suited to this approach because it allows for our complex model and data collection sample size limitations. As evidenced by the work of Hair et al. (2017), SEM-PLS shows some empirical validation through its use in potent exploratory research methods like exploratory factor analysis (Henseler et al. 2012).
Confirmatory factor analysis and measurement model validation
CFA was used to verify the measurement model, following the method of McAfee et al. (2012); Average Variance Extracted (AVE) and Composite Reliability (CR) are two measures of how well a concept is valid. AVE analyses whether a concept explains more variance than its measurement error. At the same time, CR investigates the degree to which an item’s loadings don’t differ from each other and makes sense of those differences. All model items had loadings that surpassed the critical 0.001 level (p < 0.001), indicating that the relationships among themselves are very strong and the model is stable. Standardized loadings over 0.70 indicate great strength and consistency (Carmeli, 2005). Moreover, all observable variables collectively explained over 60% of the variation in their corresponding constructs, outperforming earlier outcomes (McAfee et al. 2012).
Table 3 presents the estimation model, highlighting its strength. AVE qualities above 0.50 and CR surpassing 0.80 affirm the model’s dependability. Convergent legitimacy was supported through factor loadings that were more noteworthy than 0.70, and discriminant legitimacy was built up with no construct connections that surpassed earlier limits.
Table 3. Reliability and convert validity.
Construct | Items | FL Range | CR | AVE | α |
|---|---|---|---|---|---|
BDA Management Capability | 1- 16 | 0.78–0.92 | 0.94 | 0.81 | 0.91 |
BDA Technical Capability | 1- 12 | 0.79–0.92 | 0.96 | 0.79 | 0.90 |
BDA Talent Capability | 1–16 | 0.87–0.92 | 0.93 | 0.79 | 0.87 |
Organizational Readiness | 1–7 | 0.84–0.95 | 0.89 | 0.82 | 0.88 |
Sustainable Innovation Performance | 1–8 | 0.80–0.94 | 0.92 | 0.80 | 0.90 |
AVE Average Variance Extracted, FL Range factor loading range, α Cronbach alpha, CR composite reliability
The statistical findings detailed in Table 3 confirm the resilience of the conceptual framework underpinning our evaluation model. The Composite Reliability scores, ranging from 0.89 to 0.96, emphasize the high dependability of the explored constructs. Additionally, the Average Variance Extracted values all surpassed 0.79, further substantiating the soundness of the constructs.
Moreover, the critical item loadings, with most exceeding the 0.80 benchmark, coupled with Cronbach’s Alpha values uniformly transcending 0.87, reinforce the uniformity and trustworthiness of our evaluation model. These metrics span a spectrum of elements, such as BDA management prowess, BDA technical skillset, BDA talent aptitude, organizational preparedness, and sustainable innovation execution. Collectively, they validate the model’s internal consistency and substantiate the constructs.
Model fit summary
Structural Equation Modeling (SEM) fit lists demonstrated an adequate model fit, with an SRMR of 0.046 proposing near coordination between anticipated and watched connections. Despite the NFI of 0.884 being marginally beneath the perfect standard, it also indicates an adequate model fit. Consistency between saturated and assessed models across all fit lists affirms the model’s capacity to catch characteristic connections inside the information set, as appeared in Table 4.
Table 4. Model Fit Summary.
Saturated Model | Estimated Model | |
|---|---|---|
SRMR | 0.046 | 0.046 |
d_ULS | 0.851 | 0.851 |
d_G | 0.705 | 0.705 |
Chi-Square | 1526.132 | 1526.132 |
NFI | 0.884 | 0.884 |
SRMR Standardized Root Mean Squared Residual, NFI Normed Fit Index
According to Fig. 2, big data analytics competence and organizational readiness substantially impact sustainable innovation. A firm’s ability and willingness to adopt big data practices correlate strongly with ongoing improvements. Readiness varies by 0.55%, yet the factors forecast three-quarters of an organization’s innovativeness over time. While competence influences outcomes significantly, readiness remains crucial, and a company must fashion an environment hospitable to analytic change. The figure provides compelling evidence that cultivating expertise and culture uplifts performance measurably.
Fig. 2 [Images not available. See PDF.]
Variation of independent and mediating variables towards dependent variable.
Common method bias
We conducted various analyses to detect potential standard method bias in our research. Through a procedural assessment, two factors were found to have eigenvalues exceeding one before rotation. Initially, the primary factor accounted for 31.14% of the variance, decreasing to 26.32% after adjustment. Both values are well below the frequently cited threshold of 40% for indicating notable common method bias. Therefore, we can confidently state that prejudice did not influence our study. These conclusions corroborate the methodological soundness of our work and its reliability. Drawing on the framework of Podsakoff et al. (2003), our approach to diagnosing and mitigating common method bias has been systematic and comprehensive, further validating the authentic findings.
Discriminant validity
The Fornell-Larcker criterion outlined in Table 5 provides convincing support for the discriminant validity of our measurement framework. Namely, the variation accounted for by a construct, and its markers significantly surpass the variation shared with other constructs. The correlation matrix clearly illustrates that the diagonal elements (square roots of the AVEs) are statistically more significant than the off-diagonal elements (inter-construct relationships). This indicates that each construct independently captures a substantial amount of the nuances in its indicators. As the discrepancies between shared variances become more accentuated across all constructs, it underscores the model’s robust discriminant validity.
Table 5. Discriminant validity using the Fornell-Larcker criterion.
BDAMC | BDATC | BDATLC | OR | SIP | |
|---|---|---|---|---|---|
BDA(Management) Capability | 0.901 | ||||
BDA(Technological) Capability | 0.609 | 0.903 | |||
BDA (Talent) Capability | 0.414 | 0.508 | 0.889 | ||
Organizational Readiness | 0.572 | 0.675 | 0.574 | 0.909 | |
Sustainable Innovation Performance | 0.435 | 0.629 | 0.695 | 0.791 | 0.896 |
The value in bold is discriminant validity
The Heterotrait-Monotrait Ratio assessment epitomizes the delineation of credibility amid constructs. As depicted in Table 6, all HTMT analyses fall beneath 0.85, substantiating each concept’s discernible nature. These notions incorporate BDA Administration conduct, talent reservoirs, technological prowess, sustainable innovation performance, and organizational preparedness for BDA. The findings confirmed that every idea is uniquely characterized and precisely represents the intended multidimensional qualities explored. Moreover, the results validated the Heterotrait-Monotrait Ratio as a robust approach for evaluating whether a given construct measures something other than it purports to measure.
Table 6. Heterotrait monotrait ratio (HTMT).
BDAMC | BDATLC | BDATC | SIP | OR | |
|---|---|---|---|---|---|
BDA Management capability | |||||
BDA Talent capability | 0.637 | ||||
BDA Technology capability | 0.422 | 0.487 | |||
sustainable Innovation Performance | 0.460 | 0.648 | 0.673 | ||
Organizational Readiness | 0.592 | 0.697 | 0.555 | 0.815 |
The value in bold is HTMT
Hypotheses testing
To examine the direct and mediated relationships in our research framework, we conducted regression analyses assessing the connections between BDA capabilities encompassing management, technical, and talent dimensions and sustainable innovation performance within organizations. Moreover, we investigated the mediating effect of organizational readiness in these associations. Figure 3 visually shows the structural relationships.
Fig. 3 [Images not available. See PDF.]
Relationship among variables.
The interactions of independent towards mediating and dependent variables with beta and t values.
The direct association between BDA management capability and sustainable innovation performance within organizations (Hypothesis H1) was significant. A positive correlation (β = 0.119, t = 3.004, p = 0.001) substantiated the predicted linkage. Similarly, technical capabilities in BDA exhibited a robust association with sustainable innovation performance (β = 0.348, t = 7.729, p < 0.001), confirming Hypothesis H2.
More specifically, the analysis pointed out that BDA talent capability had a significant effect on sustainable innovation performance (β = 0.146, t = 2.962, p < 0.001), affirming Hypothesis H3 and additionally, examining the effect of BDA management capacity on organizational readiness also found a positive and significant impact (β = 0.209, t = 3.661, p < 0.001), which supported Hypothesis H4 as well.
Consistent with Hypothesis H5, BDA technical competence was significantly correlated with organizational readiness (β = 0.282, t = 5.365, p < 0.001). Moreover, a significant positive correlation was confirmed between BDA talent competency and organizational readiness (β = 0.404, t = 6.311, p < 0.001), affirming Hypothesis H6.
Regarding the direct influence of organizational readiness on sustainable innovation performance, the data yielded a substantial positive effect (β = 0.56, t = 10.3, p < 0.001), thus providing empirical support for Hypothesis H7.
Analysis of organizational readiness as a mediator
The study aims to determine whether the combination of (BDAC) impacts sustainable innovation performance through organizational readiness as a mediator. Table 7 shows that organizational readiness mediates BDAC’s three aspects (managerial, technical, and talent capabilities) and success in sustainable innovation. Empirical results show that the relationship between BDAC and sustained innovation performance is positively affected by organizational readiness. The data support hypotheses H8, H9, and H10. The attribution effect across all coefficients is significant, suggesting that organizational readiness is critical in maximizing the effectiveness of BDAC to improve sustainable innovation results.
Table 7. The mediating effects of organizational Readiness.
Relationship path | Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T Statistics (|O/STDEV|) | P values |
|---|---|---|---|---|---|
BDAMC - > OR- > SIP | 0.117 | 0.119 | 0.034 | 3.483 | 0.001 |
BDATC - > OR - > SIP | 0.158 | 0.160 | 0.032 | 4.928 | 0.000 |
BDATLC - > OR - > SIP | 0.226 | 0.227 | 0.046 | 4.911 | 0.000 |
p < 0.001
Discussion
We aim to conduct an empirical investigation to measure the extent to which organizational readiness significantly improves the capabilities of big data analytics (BDAC) in promoting sustainable innovation performance. Table 6 summarizes the mediation study, which shows the intricate link between BDAC and sustained innovation. These results underscore the role of organizational infrastructure in this process of creativity. Organizational readiness is critical to turning BDA managerial, technical, and talent capabilities into strategies for generating sustained innovation. Having these capabilities alone will not suffice. This new information raises fundamental questions about how a firm may develop and make these capabilities serve its innovative goals.
Table 7 shows that, in support of Hypothesis H8, H9, and H10, organizational readiness mediates the effect of BDAC on sustainable innovation performance. This finding supports previous research indicating that BDA’s technical and managerial capabilities significantly improve sustainable innovation performance. It demonstrates that the three aspects of sustainable development- economy, society, and environment- all contribute to a better company’s results. This corroborates our findings and is supported by the current literature. A strong, statistically significant positive correlation exists between BDA talent capability and sustainable innovation performance. The key to a successful deployment of big data analytics is organizational readiness. Big data analytics is essential to this study but not the most critical aspect. Creativity and development go hand in hand, transforming technology into a continuous source for longer-term generations.
How well a company is prepared for big data depends on its managerial agility, technological perception, and human resources strength, all essential in making this trend happen. Given the importance of senior management support for IT project success and the findings of Kalema et al. (2017), organizations are advised to promote an environment that facilitates collaboration across disciplines (Arshad et al. 2022). The need for this paper to address organizational readiness as a moderator of sustainable innovation results from our findings. It also marks an earlier shift in emphasis from the technology itself to the organizational readiness, cultural context, and flexibility of that technology. The companies accomplish this by disclosing organizational construction links to the significance of technology innovation.
Businesses need big data analytics to achieve better sustainable innovation performance. This is especially critical in urgent global challenges such as Climate Change (Silva et al. 2019). By employing big data analytics, businesses can continue to innovate while enabling them to meet new consumer demands for sustainable products and services without incurring additional costs.
This has significant implications in practice for all industrial company leaders. The findings show that when combined with high levels of organizational readiness that are high, strategic BDAC implementation improves innovation processes. When the elements are ready, an organization can succeed in its sustainable innovation.
In addition to contributing new knowledge on the subject, this study explains how BDAC affects organizational readiness and innovation performance levels. A recent empirical study, such as that of Egwuonwu et al. (2023), supports BDAC as a strategic commercial property; it promotes creative performance. To investigate the operation mechanism, our study looks at how organizational readiness mediates the influence of BDAC on sustainable innovation capability.
The study shows that BDAC prepares organizations to innovate and enrich their sustainable innovation performance, which is the power to make new things people want. Consumer demand for new research directions is thus enabled by our findings, which open the cozy and interconnected relations between sustainable innovation performance. Still, given the narrow focus of the industrial sector, it is worth looking at how generalizable such findings may be to other fields (Arshad et al. 2022). Research on BDA impacts on innovation and creativity should take up the variety of outcomes seen in different organizational, cultural, and economic contexts.
However, based on cross-sectional evidence, the present research makes it impossible to establish causal relationships between Business Data Analytics Capacity (BDAC), organizational readiness, and sustainable innovation performance. Future researchers might resort to longitudinal methods to determine the strengths of big data architecture and how company size and cultural support impact its benefits.
Theoretical implications and advancements
The study has significantly enhanced and expanded the academic field of Big Data analysis (BDA) by thoroughly considering the technical and managerial skills necessary to carry out a big data project within an organization. Our work further develops the Foundations by Kiron (2013) to deepen our understanding of BDA. Their research intensity reconstructed a company’s capacity to analyze data into three clear vessels: management output, technology hardware, and talent effort. Our empirical study goes a step further, clarifying again how these three pieces fit together and supplement each other so that the findings are easier to understand and more penetrating. The research yields a new insight into big data analytics (BDA) as a business. Companies’ power of analysis can be untied by Phillips (2017) into three essential parts: management, technology, and talent. Once again, our empirical research echoed this design. These three parts interlock seamlessly to make up the primary professional capacity in big data analysis skills.
Our study uses a different angle to probe, Does organizational readiness mediate the relationship between affordable business process architecture (BDA) and the success of sustainable innovation? In other words, how well-prepared an organization property equipped with big data supports both sustainable product development and readiness has been widely researched in Western literature (Necejauskaite, 2021; Nayak and Walton, 2024), but up until now, a mystery remains unsolved that has not been proposed before (Kushwaha et al. 2016). Although past studies have indicated a connection between BDA and firms’ innovative performance, the specific mechanism by which organizational readiness influences this link has yet to be investigated. To address this issue, we adopt a multidimensional and comprehensive theoretical framework for research on young professional BDA groups. This study posits that BDA is a higher-order construct that significantly affects the extent of doing well in sustainable business innovation. Its purpose is to answer those requests for more empirical modeling methods within this area of big data literature. We agree with Kim (2019) that a significant information gap can be filled using the third-order construct approach method to investigate organizational performance differentiation.
Moreover, our study spreads over a lacuna, prior work relays that the studies on BDA’s sustainable innovation performance and organizational readiness are primarily based in developed economies like the United States of America, Europe, and Latin America, regions with less well-established industrial bases. However, scholars have neglected developing economies such as Pakistan. Not only does our ebullient report focus on this point almost exclusively, but it will also enable Pakistan’s data sector to inject some sustainable innovation and adopt practical BDA strategies (Arshad et al. 2022).
In addition to its theoretical contributions, the study employs a robust time-lagged research design, collecting data across three distinct waves from diversified sources. This methodological approach mitigates the risk of single-method bias and lends credibility to examining higher-order constructs. By extending beyond conventional research methodologies, our study enriches the BDA literature with novel insights and methodological rigor.
Implications for management practice
The findings from this study hold substantial import for professionals in big data analytics and sustainable innovation, including managers, policymakers, and consultants. The research charts a trajectory for enterprises aiming to leverage big data analytics to bolster sustainable innovation performance, necessitating a strategic reassessment of BDA’s role within the broader innovation ecosystem. Practitioners are encouraged to adopt a forward-thinking, adaptable innovation strategy that ensures BDA capabilities are tuned to evolving sustainability objectives and market expectations.
Our research underscores the imperative of creating environments conducive to big data analytics. This entails establishing robust technological infrastructures and nurturing a culture where data utilization underpins strategic decision-making. As demonstrated by Egwuonwu et al. (2023), notable successes in Nigeria’s manufacturing and service sectors exemplify the transformative impact of BDAC when synergized with strategic foresight and a thorough understanding of organizational capabilities.
This proposed multi-dimensional model of big data analytics capacity helps managers address how these attributes interact with one another and thus influence them. The report notes that organizational planning sets the path for utilizing big data analytics as a carrier for competitive advantage prolongation. Leveraging big data analytics is the cornerstone of enhancing sustainable innovation performance. With the corporate landscape changing, enhancing organizational readiness to safeguard big data analytics as a competitive differentiator is more important than ever. Additionally, we offer a predictor tool to innovators that predict the success of their innovations by getting them ready for Big-data analytics.
It investigates the role of organization readiness and capabilities in sustainable, innovative outcomes. It will also suggest how sustainable innovation could be made more effective and durable in providing social, economic, and environmental benefits, along with policy recommendations. Such collaboration between industry insiders and incorporating sustainable innovation strategies can significantly affect the preservation of the environment and economic development.
The main utility of big data analytics in businesses is that it helps organizations gain a competitive edge over their competitors and the required brains for generating innovative thoughts. Nonetheless, it has enough practical applications and obstacles to be worth serious consideration. Suppose firms aspire to maximize the effectiveness of their partnership in big data analytics. In that case, they must strategically plan well ahead, allotting adequate monetary support and addressing all possible threats before they arise. Today, with a data-driven business context, the focus is on creating that Data & Strategic Thinking culture that thrives. The possibility of introducing more diverse and value-added big data efforts in developing countries depends on the challenge of strategic planning and adaptability businesses face. The legislative bottlenecks emanate from issues like data privacy, talent creation, investment in infrastructure expansion, and the inevitable necessity to strengthen Public-Private Partnerships models amidst capital shortfalls. The key to sustainability relies on building a culture that endorses the theory of data-driven decision-making and new techniques in using ‘big data’ effectively within these complex environments.
Conclusions
There exists a fragmented and incomplete understanding of the relationship between conceptual underpinnings and empirical aspects related to BDAC to foster sustainable innovation. Drawing upon more modern data and validating our results against relevant empirical literature from the past three years not only gives this research continued relevance in today’s digital economy but also provides direction for organizations looking to leverage a big BDAC optimally. The implications of this research spawn across both theoretical domains and the practical sphere, paving the way for future studies in the dynamic landscape that stands tall on big data analytics at large, affecting everyday corporate dynamism.
The insights derived from this study equip professionals and policymakers with an enriched understanding of the complex interplay between capacity, strategic planning, and innovation outcomes within the big data milieu. Furthermore, this research paves the way for future scholarly inquiries and policy development, presenting innovative avenues for continued academic and practical discourse.
Limitations and future directions
Despite this study’s value, its limitations open prospects for further research. The study’s initial focus on Pakistan’s National Database and Registration Authority (NADRA) limits the generalizability of the findings to other sectors. Scholars are encouraged to apply the methodologies across various industries and nations to validate and extend our findings.
This study used a predominantly quantitative approach to explore BDAC within a specific framework. Future research could benefit from adopting mixed methods or purely qualitative research designs to further substantiate and diversify the understanding of BDAC’s implications.
Additionally, while this study underscores the influence of BDAC on innovation, future inquiries could broaden the sample size and scope to enhance the applicability of these findings across different industries and regions. Such expansion would lend valuable perspectives on how the dynamics of big data analytics interact with sustainable innovation performance across various marketplaces and cultural landscapes.
In conclusion, the capacity of a firm to initiate sustainable product or service innovations is intricately tied to its fiscal health and strategic orientation. Forthcoming studies may delve into more expansive and heterogeneous samples, thereby enriching the generalizability of the results. The exploration of BDAC across a spectrum of industrial and sectoral milieus promises to reveal nuances in how big data analytics influences innovation practices in diverse operational settings.
Author contributions
MA: Conceptualization; methodology; data curation; formal analysis; original draft; writing review & editing. AQ: Data curation, visualization, review, and editing. WA: investigation; Formal analysis; review & editing. MR: investigation; methodology.
Data availability
The data obtained and examined in this work are documented in the paper and provided in the supplemental Data file. However, certain limitations apply to sharing the data publicly. If requested, the corresponding author can offer additional information.
Ethical approval
Ethical considerations are of utmost importance in this research, which is conducted under the auspices of Pakistan’s National Database and Registration Authority (NADRA). Throughout this investigation, strict respect for ethical norms was maintained, as confirmed by the authorization from the Ethics Committee of Yunnan University of Finance and Economics on 2021/6/25; the letter number is 2021-P-0034. The methodology entailed deploying a survey exploring big data analytics, organizational readiness, and potential impacts on innovation. The survey population included individuals in management and those in roles within the human resources, information technology, and data processing sectors. Consent forms were duly provided alongside the survey, with informed consent secured from all participants, who were fully apprised of the research aims before data collection. The data procured was strictly for academic purposes and safeguarded with strict confidentiality.
Informed consent
All participants had to read and agree to an informed consent statement containing information regarding the researchers and their affiliated institutions, the study’s objectives, potential dangers, and the possible utilization of their data in further studies and publications.
Competing interests
The authors declare no competing interests.
Supplementary information
The online version contains supplementary material available at https://doi.org/10.1057/s41599-024-03424-4.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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