Big data adoption and performance: mediating mechanisms of innovation, supply chain integration and resilience

Abstract

Purpose

This study aims to examine how big data adoption (BA) helps to improve innovation capability, supply chain integration, resilience and organizational performance through direct and mediating mechanisms.

Design/methodology/approach

This study uses a combination of meta-analytic approaches (meta-structural equation modeling and meta-regression) using 205 effect sizes from 76 prior empirical studies. It leverages the organization information processing theory as a theoretical lens to analyze the proposed relationships. This study estimates heterogeneity in the relationship between BA and innovation capability based on the meta-regression by considering different types of moderators: digital competitiveness score (DCS), national culture, type of economies and gross domestic product (GDP) per capita.

Findings

The findings indicate that BA improves the innovation capability of the organization, supply chain integration and resilience, which consequently drives organizational performance. The results show that the innovation capability mediating effect is higher between BA and supply chain integration than between BA and supply chain resilience link. However, supply chain resilience and integration are equally effective in translating innovation capability influence to organizational performance. The authors find that developing countries reap more benefits from BA in driving innovation, and country culture plays a vital role in driving innovations.

Research limitations/implications

This study offers multiple theoretical implications. First, deriving from organization information processing theory, the authors recognized that BA and innovation capability complement each other, which improves the information processing capacity of the organizations, enabling supply chain integration, resilience and organizational performance (Bahrami et al., 2022; Gupta et al., 2020; Chatterjee et al., 2022). This study is one of few that analyzed how BA and innovation capability work together to drive supply chain integration, resilience and organizational performance, which was not collectively studied in existing studies, meta-analyses or reviews to ascertain the direct and mediating mechanisms (Aryal et al., 2020; Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Bag and Rahman, 2023; Alvarenga et al., 2023). Second, our study offers integrated and more definitive results regarding identified relationships. More precisely, the study provides statistically significant direct effects with the help of meta-analysis and meta-structural equation modeling to remove the ambiguity in the literature. Third, apart from the above definitive relationships, mediation analysis contributes to academia in identifying significant mediating mechanisms related to innovation capability, supply chain integration and resilience. Innovation capability partially and significantly mediates between BA and supply chain integration/resilience. Fourth, meta-regression provides valuable insights related to DCS, national culture and type of economies in the supply chain context. In fact, this study is the first one to examine the effects of DCS and all dimensions of national culture on the BA−INV relationship and overcome certain limitations that exist in the literature (Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Nakandala et al., 2023).

Practical implications

Big data is captured through evolving digital technologies such as intelligent sensors, radio frequency identification tags, global positioning system (GPS) locations and social media, which generate large data sets. Thus, managers must extract value from such a large data set and transition from big data to BA. This transition encompasses retrieving unknown patterns and insights from big data, its interpretations and extracting meaningful actions (Gupta et al., 2020; Hallikas et al., 2021). This study confirms that organizational capabilities in terms of BA and innovation enable supply chain integration and resilience. Managers must concentrate on BA and innovation capability simultaneously rather than making a trade-off between capabilities (Morita and Machuca, 2018) to drive supply chain integration, resilience and performance. For example, Morita and Machuca (2018) study revealed that many companies are doing trade-offs between capabilities and innovation. Hence, the findings clarified confusion among practitioners and confirmed that BA improves innovation capability, consequently enabling higher supply chain integration and resilience. Thus, managers investing in innovation capability will be more confident about integration, resilience and performance outcomes.

Originality/value

This is one of the early studies that examine the underlying mechanisms of innovation capability, supply chain integration and resilience between BA and organizational performance. Moderation analysis with a DCS, national culture, type of economies and GDP per capita explains the heterogeneity between the BA and innovation capability relationship.

1. Introduction

Big data are paramount in current business scenarios to achieve superior organizational performance (Dubey et al., 2023). According to a Deloitte report, big data analytics is one of the top three investment priorities among all digital technologies (Gurumurthy et al., 2020), which can improve revenue and customer value by 15% and 23%, respectively, during the early adoption stage and 45% and 41%, respectively, during the maturity stage. Thus, big data adoption (BA) is crucial for organizations to improve performance. BA refers to integrating big data analytics into organizational decision-making (Arias-Pérez et al., 2022).

BA differs from other technological innovations in its ability to collect, analyze and interpret voluminous data to generate valuable insights (Cappa et al., 2021; Chatterjee et al., 2022). Unlike radio frequency identification (RFID), electronic data interchange and other traditional information technologies, BA is characterized by its ability to handle massive volumes of data derived from a wide variety of sources and processed at high speeds. These three characteristics − volume, variety and velocity − are unique to BA and fundamentally change how supply chains operate (Cappa et al., 2021). Nevertheless, few studies also extend the distinguishing features of BA to four or even five characteristics, including veracity and value (Joubert et al., 2023; Li et al., 2023). Here, veracity refers to the quality of generated data, and value represents the disclosure of underexploited insights from big data for effectively managing the supply chain operations (Talwar et al., 2021). These unique characteristics of BA allow supply chains to analyze vast amounts of structured and unstructured data in real-time, enabling more informed decision-making and proactive responses to market changes. While technologies such as RFID focus primarily on tracking and identification, BA goes beyond providing predictive and prescriptive analytics (Talwar et al., 2021; Li et al., 2023). BA can predict future trends and optimize supply chain operations based on complex data analysis, providing a unique capacity to the supply chains. For example, by analyzing real-time data from multiple sources, BA can identify potential disruptions in the supply chain and suggest alternative strategies to mitigate risks. This level of insight and agility is not achievable with technologies like RFID, which are more focused on operational efficiency rather than strategic adaptability. Hence, BA differs from other technologies in its ability to analyze current situations, anticipate future scenarios and suggest preemptive actions.

BA is important for organizations because it supports coordination and collaboration with internal and external stakeholders considering suppliers or customers to generate valued understandings (Yu et al., 2021; Legenvre and Hameri, 2023). This coordination and collaboration among supply chain members helps to improve supply chain integration, which is important for organizations to achieve better performance (Erboz et al., 2022). According to organization information processing theory (OIPT), BA helps organizations to assess diverse data sets and share valuable information across the supply chain, thereby facilitating supply chain integration (Srinivasan and Swink, 2018; Yu et al., 2021). A high level of integration improves an organization’s overall information processing capacity, leading to more efficient operations and better alignment with strategic goals. Big data-enabled supply chain integration facilitates timely material delivery, production plan adjustment, maintaining inventory levels and eliminating potential ad hoc conflicts, resulting in improved organizational performance (Yu et al., 2021; Erboz et al., 2022). Thus, supply chain integration is an important factor in establishing BA’s impact on organizational performance.

However, only supply chain integration may not be sufficient to achieve better performance in uncertain business environments, and thus, organizations need to have resiliency in their supply chain (Cui et al., 2023; Iftikhar et al., 2023; Jiang et al., 2024). OIPT suggests that an organization’s capacity to process information about potential threats and opportunities can make the supply chain resilient during disruptions (Dubey et al., 2021; Yu et al., 2022). By efficiently gathering and analyzing information, organizations can anticipate challenges, develop contingency plans and respond flexibly to changes. Robust information processing systems enhance organizational resilience by enabling timely and informed decision-making during crises (Cui et al., 2023). Realizing the benefits of BA for the supply chains, organizations continue to invest in various data accessing and controlling systems, which improve the information flow and facilitate resiliency in the supply chain (Dubey et al., 2021; Jiang et al., 2024). In reality, Walmart has improved its resiliency by leveraging BA across the supply chain, considering automatic purchasing, inventory tracking and customer fulfillment (Zhan and Tan, 2020).

Along with supply chain integration and resilience, few scholars argue that innovation capability is important for organizations to improve their performance (Yu et al., 2022; Bahrami and Shokouhyar, 2022). The information-sharing and analytical abilities of BA help to improve the innovation capability of organizations (Ashrafi et al., 2019; Bahrami and Shokouhyar, 2022). Innovation capability involves generating and implementing new ideas, products or processes (Calic and Ghasemaghaei, 2021; Chatterjee et al., 2022). From the perspective of OIPT, innovation capability is a response to the need for new information and knowledge to address business uncertainties and challenges. Organizations must process vast amounts of information, filter relevant insights and apply them creatively to develop innovative solutions. Effective information processing capabilities are critical for fostering innovation capability within organizations. A few scholars acknowledged that innovation capability influences supply chain integration or resilience, consequently enabling higher performance (Yu et al., 2022; Dovbischuk, 2022). Hence, innovation capability, in conjunction with supply chain integration and resilience, is an important factor affecting organizational performance in the big data environment.

In the highly dynamic and fast-changing business environment, supply chains need to be innovative, well-integrated and resilient to remain competitive. Investigating these factors together ensures that organizations can simultaneously drive innovation, maintain efficient operations and manage risks effectively. There is a wide body of research investigating these factors but how BA linked with innovation capability, supply chain integration and resilience in an integrated model and further how these factors are associated with organizational performance are largely unexplored (Bahrami and Shokouhyar, 2022; Li et al., 2023; Bag et al., 2023).

Existing research primarily focuses on the direct effects of BA on innovation capability, supply chain integration and resilience (Yu et al., 2021; Li et al., 2023; Bag et al., 2023), paying less attention to the various mediating mechanisms. Nonetheless, the extent to which innovation capability might indirectly influence supply chain integration and resilience within the BA framework is largely unknown. BA-enabled innovation capability represents a distinct organizational strength that can significantly alter supply chain integration and resilience (Bahrami and Shokouhyar, 2022; Li et al., 2023). While certain studies have begun to explore the connections between innovation, integration and resilience (Bag et al., 2023; Li et al., 2023), the mediating mechanisms that link BA with supply chain integration and resilience through innovation capability remain unexplored. To address this gap, we pose the first research question (RQ):

RQ1. How does innovation capability serve as a mediator between BA and supply chain integration and between BA and supply chain resilience?

It is important to understand the mediating role of innovation capability that can provide organizations with strategic insights into leveraging BA to enhance supply chain integration and resilience.

BA has the potential to significantly enhance organizational performance, but the mechanisms through which this happens are complex and need further exploration. Existing literature in connection with innovation, integration and resilience majorly explored its direct relationship with organizational performance (Gawankar et al., 2020; Liu et al., 2021; Arias-Pérez et al., 2022; Bag et al., 2023). However, apart from the direct relationships, it is also important to investigate the mechanism through which it improves organizational performance. These mechanisms are critical for organizational success, especially in today’s global and complex business environments. Innovation capability can drive these mechanisms, and thus, by identifying how innovation capability can improve these mechanisms, organizations can develop targeted strategies to enhance their supply chain integration and resilience, leading to better organizational performance. However, to the best of our knowledge, no study explores how innovation capability improves organizational performance through mediating mechanisms of supply chain integration and resilience in the context of BA. Thus, our second RQ is:

RQ2. How do supply chain integration and resilience serve as a mediator between innovation capability and organizational performance?

This study seeks to answer the above RQs using meta-analysis and meta-analytical structural equation modeling (meta-SEM). The study offers a robust and comprehensive analysis using meta-analytical methods, including meta-SEM and meta-regression. This approach enables a more nuanced understanding of the relationships among BA, innovation capability, supply chain integration, resilience and organizational performance, providing a more reliable and generalizable set of findings.

The major contribution of this paper is to identify the different pathways through which BA is related to organizational performance. This study contributes to supply chain literature by highlighting the interdependence of innovation capability, supply chain integration and resilience within the theoretical groundings of OIPT. This integrated perspective provides a more holistic understanding of how organizations can simultaneously pursue innovation, integration and resilience in complex business environments. The study advances the theoretical discourse on supply chain integration and resilience by linking it directly to information processing enabled by BA. It argues that supply chain integration and resilience enhance an organization’s ability to process information efficiently across different functions, improving organizational performance. Furthermore, this study contributes to the theoretical understanding of innovation capability by positioning it as an outcome of effective information processing. It suggests that the ability to generate and implement innovative ideas is closely tied to how well an organization processes and analyzes information. This connection deepens our understanding regarding the role of information processing in fostering innovation capability, providing a more nuanced view of how organizations can innovate in response to environmental challenges to facilitate supply chain integration, resilience and organizational performance.

The paper is organized as follows. Section 2 deals with a theoretical background and hypothesis development. Section 3 illustrates the research methodology in detail, and Section 4 includes meta-analysis, meta-SEM and meta-regression results. Section 5 encompasses research and practical implications, whereas Section 6 concludes with limitations and a way forward.

2. Theoretical background and hypothesis development

This study focuses on the underlying mechanisms of innovation capability, supply chain integration and resilience between BA and organizational performance. Existing literature on supply chains has explored various links: between BA and operational performance (Gawankar et al., 2020; Dubey et al., 2023), BA and supply chain integration (Yu et al., 2021; Li et al., 2023), BA and supply chain resilience (Chenger and Pettigrew, 2023) and BA and innovation (Bouncken et al., 2021). In addition, a wide body of research has examined the relationships between supply chain integration and performance (Wong et al., 2020; Li et al., 2022), supply chain resilience and performance (Bahrami and Shokouhyar, 2022; Dovbischuk, 2022) and innovation and performance (Saleem et al., 2020; Bag et al., 2023). However, these studies have typically been conducted in isolation. Past research has often highlighted the need to investigate the mediating mechanisms between BA and performance (Calic and Ghasemaghaei, 2021; Erboz et al., 2022; Iftikhar et al., 2023; Balci and Ali, 2024). A seminal paper on mediation analysis in operations management suggests that in multimediator models, contrasting the relative strength of indirect effects through different mediators can provide richer insights (Malhotra et al., 2014). Despite this recommendation, only a few studies have applied multimediator models in the context of innovation capability, supply chain integration, resilience and organization performance related to BA. To address these gaps, this study uses multimediator models, considering innovation capability, supply chain integration and resilience as mediating variables between BA and organizational performance.

To establish mediating mechanisms, we consider OIPT, which offers a plausible theoretical grounding for organizations to collect, analyze and use information effectively (Srinivasan and Swink, 2018; Dubey et al., 2021). The increasing volume of data across the supply chain amplifies the data processing demands for organizations. These growing demands necessitate that supply chain members to handle vast amounts of data, thereby enhancing the data processing capabilities within the supply chain. BA plays a crucial role in meeting these processing demands and in fostering the development of data processing capacities within supply chains (Srinivasan and Swink, 2018; Shafique et al., 2024). OIPT effectively explains the interactions between data processing requirements and capacities, emphasizing the need for organizations to balance these elements to improve supply chain competencies and overall performance (Srinivasan and Swink, 2018; Yu et al., 2021). The literature suggests that BA can fulfill the data processing needs of the supply chain by enabling the collection, analysis and interpretation of large data sets, generating valuable insights (Yu et al., 2021; Shafique et al., 2024). These insights are essential for supply chain members to enhance their innovation capabilities (Corallo et al., 2023) and facilitate supply chain integration and resilience (Li et al., 2022; Yu et al., 2021). Following the same, Srinivasan and Swink (2018) and Dubey et al. (2021), along with many others, used OIPT to argue that organizations must effectively use and analyze information, especially in complex business environments, to improve their performance. Given this discussion, OIPT provides a solid theoretical foundation for understanding the role of BA in supply chains and for developing related hypotheses.

2.1 Big data adoption

BA provides organizations with the tools and methodologies to process, analyze and interpret large data sets. According to OIPT, the capability to manage and use this information effectively enables organizations to reduce uncertainty and make informed decisions. This enhanced information processing capacity is a fundamental driver of innovation, as it allows firms to identify new opportunities, optimize existing processes and create novel solutions. BA continues to be one of the promising capabilities for firms in the future, driving new business opportunities and helping them to develop innovation capabilities (Ashrafi et al., 2019). Along similar lines, JD.com (one of the largest online retailers in China) shows the positive impact of technologies in achieving innovations during supply chain disruptions (Shen and Sun, 2023). Bouncken et al. (2021) find that digitalized supply chains involve digitalization and innovation in a duality relationship, indicating that digitalization can support innovations. Data related to customers’ expectations and competitors can help firms to predict future innovations (Lehrer et al., 2018). Bag et al.’s (2023) study related to the omnichannel health-care supply chain also disclosed that BA helps organizations generate innovative solutions. This data-driven decision-making approach allows businesses to identify opportunities, mitigate risks and optimize processes, fostering innovations in strategy and operations. BA can help to analyze vast amounts of information to identify trends, patterns and correlations that may not be apparent through traditional analysis methods. This helps organizations to anticipate market changes, customer preferences and emerging technologies, providing a foundation for innovative solutions. Based on the discussion, we argue that BA facilitates innovation capability:

H1a. Big data adoption is associated with innovation capability.

Besides BA’s direct impact on innovation capability, it can also influence supply chain integration and resilience. BA enables timely and accurate information sharing among supply chain functions such as procurement, warehousing, operations, transportation and logistics, facilitating better integration among supply chain members (Yu et al., 2021; Li et al., 2023; Kotzab et al., 2023). Walmart has improved its integration by leveraging big data across the supply chain, such as automatic purchasing, inventory tracking and customer fulfillment (Zhan and Tan, 2020). BA supports advanced coordination mechanisms among supply chain partners by providing insights into each partner’s operations, inventory levels and demand forecasts. This coordination is essential for synchronizing activities across the supply chain, leading to reduced redundancies and improved efficiency. Big data platforms can integrate disparate information systems of supply chain partners, enabling seamless data exchange and collaboration. Extant literature suggests that OIPT can be used to understand the supply chain’s ability to integrate with internal and external processes to respond to dynamic environmental challenges (Srinivasan and Swink, 2018; Yu et al., 2021; Shafique et al., 2024). This integration is critical for developing shared strategies to improve organizational performance, reflecting OIPT’s focus on enhancing information processing capabilities through technology. Hence, we argue that BA is associated with supply chain integration:

H1b. Big data adoption is associated with supply chain integration.

Supply chain resilience refers to the ability of a supply chain to anticipate, prepare for, respond to and recover from unexpected disruptions or changes, returning to its original state or growing stronger after facing adverse situations (Alvarenga et al., 2023; Li et al., 2023). The concept of supply chain resilience is critical in today’s global business environment, where supply chains face various risks, including natural disasters, geopolitical tensions, economic fluctuations and technological failures. By leveraging big data, organizations can gain real-time visibility into their supply chain operations, from raw material sourcing to end-product delivery (Dubey et al., 2021; Edwin Cheng et al., 2022). This visibility helps identify bottlenecks, predict future disruptions and make informed decisions, thereby aligning with OIPT’s emphasis on reducing uncertainty through improved information processing. By leveraging predictive analytics, organizations can anticipate potential disruptions, demand fluctuations and supply chain bottlenecks before they occur. This foresight allows for proactive adjustments to operations, inventory management and logistics planning, ensuring smoother, more efficient supply chain operations and creating resilience during disruption (Al-Khatib, 2022; Chenger and Pettigrew, 2023). Thus, based on these discussions, we posit:

H1c. Big data adoption is associated with supply chain resilience.

2.2 Innovation capability

Innovation capability refers to “a firm’s ability to generate, accept, and implement new ideas, processes, products, or services” that create or improve value for the stakeholders (Wang and Dass, 2017; Bahrami and Shokouhyar, 2022). Innovation capability, fueled by BA, plays a pivotal role in enhancing supply chain integration because various entities in the supply chain, including suppliers, manufacturers, distributors and retailers, can integrated due to benefits derived from innovations. Innovations borne out of BA can lead to the development of new collaboration platforms, improvement in information-sharing protocols and the establishment of more synchronized operations (Corallo et al., 2023). For instance, an innovative data-sharing platform can enable real-time visibility of inventory levels across the supply chain, facilitating better coordination among partners. The mediating role of innovation capability in translating BA into enhanced supply chain integration is a testament to the organization’s ability to apply its enhanced information processing capabilities toward more effective coordination and collaboration. Innovations such as data-sharing platforms and improved information-sharing protocols directly reduce information processing requirements by making the supply chain more predictable and manageable. This aligns the organization’s information processing capabilities better with its needs, as OIPT advocates. Hence, we posit:

H2a. Innovation capability mediates the relationship between BA and supply chain integration.

BA directly improves supply chain resilience; however, the existence of innovative capability can support the BA−supply chain resilience link (Bahrami and Shokouhyar, 2022). Innovation capability can help organizations to realize the actual benefits of BA to achieve supply chain resilience. BA helps supply chain partners to innovate their products and processes to cope with disruptions, enabling supply chain resiliency (Wong et al., 2020; Do et al., 2022). Organizations relying on BA only to drive resilience are less effective than organizations that use innovative capabilities to drive resilience (Bahrami and Shokouhyar, 2022; Shen and Sun, 2023). The mediating role of innovation capability thus lies in its ability to transform the analytical insights provided by BA into practical innovations that directly contribute to supply chain resilience. Without this mediating influence, the raw data and analytical insights might remain underutilized, failing to translate into actionable strategies that enhance resilience. Innovation capability bridges this gap, ensuring that the potential of BA is fully realized in terms of building a more resilient supply chain. Based on discussions, we hypothesize the following:

H2b. Innovation capability mediates the relationship between BA and supply chain resilience.

2.3 Supply chain integration

As discussed above, innovation capability improves organizational performance; however, the presence of supply chain integration can support the innovation capability to organizational performance link. Innovation capability generates new opportunities for supply chain members to innovate their products and services, whereas supply chain integration facilitates the innovation process through better coordination and information sharing among supply chain members (Srinivasan and Swink, 2018). From the OIPT perspective, innovation capability complements supply chain integration by improving the organizational data processing capacity through integrating internal and external resources and knowledge to reduce uncertainty and consequently improve performance. Here, a more integrated supply chain supports knowledge and resource utilization more efficiently than nonintegrated supply chains, thus impacting the innovation−performance link. Supply chain integration results in effective intra- and interorganizational processes, such as information exchange, integrated management system and goal alignment (Wiengarten et al., 2019; Wong et al., 2020), facilitating the innovation capability to performance relationship. Following the discussions, we argue that supply chain integration mediates the relationship between innovation capability and organizational performance:

H3. Supply chain integration mediates the relationship between innovation capability and organizational performance.

2.4 Supply chain resilience

While organizations with superior innovation capabilities frequently introduce product and process innovations, leading to enhanced performance, the resilience of the supply chain plays a crucial role in reinforcing the link between innovation capability and organizational performance. Through the lens of OIPT, supply chain resilience is seen as augmenting the organization’s information processing capacity, enabling better decision-making in the face of disruptions (Yu et al., 2022). This enhancement is essential for maintaining the positive impact of innovation on performance amidst the increasing inevitability of supply chain disruptions. Organizations endowed with a high innovation capability are poised to achieve superior performance levels, provided their supply chains possess the resilience to withstand such disruptions (Forbes, 2022; Yu et al., 2022). Therefore, it is posited that supply chain resilience amplifies the effect of innovation capability on organizational performance.

The mediating influence of supply chain resilience in the nexus between innovation capability and organizational performance is indispensable. It indicates that the advantages brought by innovation capability, crucial for securing competitive advantages and operational enhancements, reach their maximum potential in organizational performance when they significantly bolster supply chain resilience (Forbes, 2022; Yu et al., 2022). According to OIPT, organizations that can dynamically align their information processing capabilities with their needs in changing conditions are more likely to perform better, as they can more effectively coordinate and use resources. Innovation capability often improves resiliency in the supply chain because organizations seek to develop and implement strategies that enhance their adaptability and, consequently, the organizational performance (Holgado and Niess, 2023; Ye et al., 2024). Innovations that fail to contribute to resilience might not deliver the anticipated performance gains in a fluctuating and uncertain market context. On the contrary, resilience-focused innovations ensure that the supply chain serves as a strong foundation, fostering sustained improvements in performance even during disruptions. Thus, we propose:

H4. Supply chain resilience mediates the relationship between innovation capability and organizational performance.

Based on the identified relationships in the above hypothesis, our research model is shown in Figure 1.

3. Research methodology

This study uses meta-analysis to integrate relevant empirical findings related to BA within supply chains. This approach has been widely used in the literature to incorporate different theoretical perspectives and synthesize empirical findings (Cram et al., 2019; Akın Ateş et al., 2022; Chen et al., 2023). Consistent with Cram et al. (2019) and Akın Ateş et al. (2022), we follow a three-stage procedure for the meta-analysis:

sample selection;

coding data from identified studies; and

analyzing data for direct and indirect effects as shown in Figure 2.

Stage 1. Sample selection: We used keywords string as follows to find relevant articles for meta-analysis: (TITLE-ABS-KEY (“big data”) OR TITLE-ABS-KEY (“analytics”)

AND TITLE-ABS-KEY (“supply chain”) OR TITLE-ABS-KEY(“firm”) OR TITLE-ABS-KEY (“organisation”)) AND TITLE-ABS-KEY (“innovation”)) OR TITLE-ABS-KEY (“integration”)) OR TITLE-ABS-KEY (“resilience”)) OR TITLE-ABS-KEY

(“performance”)) OR TITLE-ABS-KEY (“outcome”)) AND (LIMIT-TO (DOCTYPE, “ar”))

AND (LIMIT TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”)).

We found 1,916 documents at this stage. We retained peer-reviewed journal articles and excluded other documents, such as conference papers and book chapters, resulting in 925 articles for further analysis. Next, two authors independently read the abstracts of all 925 articles and removed articles involving analytical modeling, simulations, case studies and literature reviews due to the nonavailability of requisite data for meta-analysis (e.g. Chatterjee et al., 2022), resulting in 215 articles. Finally, we excluded papers having insufficient information to calculate effect size statistics (e.g. sample size, reliability, correlation) for at least one of the relationships in our research model, resulting in 72 articles for meta-analysis. Furthermore, we conducted a manual search with identified keywords from other research outlets, which yielded four articles. Hence, 76 articles were included in our meta-analysis (refer Appendix).

Stage 2. Coding: We use a coding protocol to summarize the information (Cram et al., 2019; Akın Ateş et al., 2022). For each study in our sample, we coded:

study-level data such as the author, and country of data collection;

construct-level data such as the construct names, definitions, reliabilities, means and standard deviations; and

relationship level data such as the sample size and correlation.

We categorized the various constructs coded from studies according to our research model (Cram et al., 2019). Different phrases used to describe similar constructs were categorized under the same name based on their definitions and descriptions of measurement items. For example, big data analytics, data analytics, supply chain analytics and business analytics were coded as big data adoption. Supplier integration, customer integration, internal integration and supply chain collaboration were coded as supply chain integration. Disaster capability, supply chain survivability/resilience and firm resilience were categorized as supply chain resilience. Innovations capability, new product introduction, product, process and service-related innovations were coded as innovation capability. Firm performance, quality performance, financial performance, supply chain performance and business performance were categorized as organizational performance. The details of the five constructs are illustrated in Table 1. To verify our categorization (Gerow et al., 2014), two independent researchers randomly selected 10 papers from the sample and matched the constructs to our categories. Their classification was consistent with ours.

Stage 3. Meta-analysis: Referring to the guidelines of Lipsey and Wilson (2001) and Cram et al. (2019), we calculated a weighted mean effect size by correcting the observed correlation with reliability and transforming them to standard scores. For the primary analysis, we used meta-analysis relative weight techniques (Cram et al., 2019) to gauge the strength of relationships between independent and dependent variables. Furthermore, to evaluate the validity and reliability of the meta-analysis results, we conducted significance (z-test) and homogeneity (Q-test) tests for each relationship and calculated the confidence intervals. To estimate publication bias, we computed the failsafe-N statistic, which estimates the number of studies with nonsignificant results that can change the result of our study (Hunter and Schmidt, 2004; Cram et al., 2019). We use meta-SEM followed by mediation analysis to test the research model.

Contextual factors such as national culture, type of economy or digital competency may influence the impact of BA on innovation capability. Meta-analyses frequently use contextual factors to explain heterogeneity in focal relationships (Eisend, 2019; Iyer et al., 2020; Liu et al., 2021; Kumar et al., 2022; Akın Ateş et al., 2022). Our study is based on a meta-analysis, and the majority of meta-analysis papers consider national culture or type of economy as moderators. This approach is essential because meta-analyses compile papers published across various countries and regions. To effectively analyze contextual factors, it is crucial to include country-level or cultural factors. Table 2 summarizes the papers and the moderators used in various studies, clearly showing that most papers incorporate national culture and type of economy as moderators.

For instance, Akın Ateş et al. (2022) used Hofstede’s cultural dimensions − power distance, uncertainty avoidance, individualism, masculinity and long-term orientation − as moderators to explain heterogeneity between supply chain complexity and firm performance. Liu et al. (2021) examined the moderating effects of individualism and uncertainty avoidance on the relationship between supply chain integration and firm performance. Eisend (2019) used Hofstede’s culture dimensions to explain the heterogeneity related to digital piracy, incorporating gross domestic product (GDP) per capita and internet users per 100 inhabitants to account for economic and technological disparities. In line with these approaches, this study uses Hofstede’s national cultural dimensions as moderators to explain cultural heterogeneity in the focal relationship. National culture draws research attention in operations management due to the growing importance of supply chain globalization (Liu et al., 2021; Akın Ateş et al., 2022). It is crucial because it indicates societal responses to norms, formality and structure (Saldanha et al., 2021; Liu et al., 2021; Kumar et al., 2022), and individuals’ perceptions of digitization and innovation are shaped by societal contexts. In addition, economic and technological competencies vary among countries due to different levels of infrastructure and economies of scale (Eisend, 2019; Rahman et al., 2022; Oesterreich et al., 2022). Economic disparities are captured through the type of economy and GDP per capita, while technological disparities can be measured using the country’s digital competitiveness score (DCS)¹.

This study uses meta-regression with identified moderators − DCS, Hofstede’s national culture dimensions, type of economy and GDP per capita − to understand the impact of digital competitiveness, national culture and economic disparities on the relationship between BA and innovation capability. To operationalize these contextual factors, country-specific data are collected and used for further analysis. The DCS, reflecting a country’s digital competitiveness, is obtained from the International Institute for Management Development World Digital Competitiveness score (WDCR, 2022). Similarly, country-specific Hofstede’s cultural dimensions (Hofstede, 1980)² are used in the meta-regression. Studies with data collected from multiple countries, such as Li et al. (2023), are excluded from the regression analysis due to the unavailability of country-specific data. For the type of economy, developing economies are coded as 0 and developed economies as 1 in the meta-regression. Finally, GDP per capita data are retrieved from the World Bank³ and used as input for the meta-regression.

4. Results

4.1 Meta-analysis results

The corrected effect size (corrected r) for each of the relationships between the five constructs is summarized in Table 3. We use the “metafor” package available in R software for our analysis. We follow the quartile benchmarks as suggested by Lipsey and Wilson (2001) and Cram et al. (2019) to interpret the magnitude of the effect sizes. It states that effect sizes less than 0.3 are “small”, between 0.3 and 0.5 are “medium”, between 0.5 and 0.67 are “large” and greater than 0.67 are “very large” (Lipsey and Wilson, 2001; Cram et al., 2019). We found that the overall effect size across relationships varies between 0.46 and 0.64. Furthermore, three identified relationships in our study are medium; seven relationships are in a large category. This signifies that BA largely drives innovation capability and integration, followed by resilience in supply chains.

For validity, a z-test was conducted to confirm the significance of each relationship effect size and its statistical significance level. Homogeneity tests (Q-value) were also carried out for each relationship to understand the possibility of moderating effects. The confidence intervals were also computed. It indicates that the average effect size should be within the calculated 95% confidence interval (Jiang et al., 2012). From Table 3, we noticed that the prediction interval range of many relationships includes zero, and hence, the possibility of moderators will be examined (Cram et al., 2019). Furthermore, we examined the failsafe-N to confirm the publication bias, which confirms to the requirements (Rosenthal, 1979; Cram et al., 2019).

4.2 Path model estimation (meta-structural equation modeling)

To test the research model, we use structural path analysis. This analysis requires a meta-analytically derived correlation matrix as input. Our research model contains five constructs, meaning 10 off-diagonal cells of a correlation matrix are needed. We calculated meta-analytic correlations (refer to Table 3) among all constructs and used these correlations to create a correlation matrix, as shown in Table 4. The table also includes means and standard deviations used for meta-SEM in Stata 14. Since meta-SEM requires a single sample size, the minimum sample size (1,056) across all relationships was used for analysis (Hooda et al., 2022).

The research model (Figure 1) was first tested using meta-SEM. The results showed a poor fit with χ2df=40.90, P < 0.00, comparative fit index (CFI) = 0.96, Tucker-lewis index (TLI) = 0.82 and root mean square error approximation (RMSEA) = 0.19. However, all hypothesized paths were significant. Here, χ2df, TLI and RMSEA were not acceptable (Hooda et al., 2022; Kumar et al., 2022). The poor model fit suggests that other paths may be added to achieve a better model fit by referring to the modification indices (Hooda et al., 2022).

The path between SCI and SCR was added due to a high modification index (74.54). The emergent model (Figure 3) showed a good fit with χ2df=4.49, CFI = 0.99, TLI = 0.98 and RMSEA = 0.058. All model fit indices were acceptable (Bentler and Bonett, 1980; Hooda et al., 2022; Kumar et al., 2022). All hypothesized paths remained significant, and one added path, SCI → SCR, was also significant (p < 0.00).

The emergent model (Figure 3) shows that BA is significantly related to innovation capability (β = 0.52, p = 0.000), confirming H1a. Similarly, BA is also significantly related to supply chain integration (β = 0.33, p = 0.000) and resilience (β = 0.16, p = 0.000), confirming H1b and H1c. H1 shows that the relative strength of the BA−innovation capability link is highest, followed by the BA−supply chain integration and the BA−supply chain resilience link. Furthermore, INV−OP (β = 0.27, p = 0.000), SCI−OP (β = 0.15, p = 0.000) and SCR−OP (β = 0.45, p = 0.000) are statistically significant. By comparing the coefficients, it is worth mentioning that supply chain resilience is more effective in driving organizational performance than supply chain integration. Interestingly, we found one more relationship (dotted line) in the emergent model (SCI → SCR). This gives us new insights, such as supply chain integration facilitating resilience, which aligns with Li et al. (2022) and Poberschnigg et al. (2020).

4.3 Mediation analysis

Table 5 shows the mediation test statistics. We perform mediation analysis in Stata 14 using “medsem” command. We consider effect sizes (e.g. correlation coefficients), standard deviation and mean of each construct relevant to the paths in the mediation model. Our result suggests that innovation capability partially mediates between BA and SCI and between BA and SCR, confirming H2a and H2b. More precisely, the 40% and 30% effect of BA on SCI and SCR, respectively, is mediated by INV. This shows that innovation capability affects the BA−SCI link more than the BA−SCR link. Similarly, the 19% and 18% effect of INV on OP is mediated by SCI and SCR, respectively, confirming H3 and H4. This shows that both SCI and SCR are almost equally effective in translating the effect of innovation capability on organizational performance.

Apart from the stated hypothesis, we find one more significant partially mediating effect link, as shown in Table 5. Specifically, 46% effect of SCI on OP is mediated through SCR. This shows that even though SCI directly improves OP, it also indirectly impacts OP through SCR. This is because better integration among supply chain members enables higher organizational benefits when the supply chains are resilient.

4.4 Post hoc: meta-regression

In a post hoc analysis, we analyze the effect of contextual factors on the BA−INV relationship. This is because it has sufficient data points for meta-regression. We regressed the effect size of all identified factors (DCS, national culture, type of economy and GDP per capita) on the BA−INV relationship. The final estimated model is as follows: (1) ESij=β0j+rij (2) β0j=γ00+γ01×(DCSj)+γ02×(Individualismj)+γ03×(Uncertanity Avoidancej)+γ04×(Long term orientationj)+γ05×(Indulgencej)+γ06×(Power distancej)+γ07×(Musculinityj)+γ08×(type of economyj)+γ09(GDP per capitaj)+u0jwhere ES_ij is the i^th effect size describing the relationship reported within the j^th sample. Equation (2) describes the effect of all identified moderators that vary between studies. Here, u_j is the term for the study level residual error. A fully nested model (Table 6) illustrates the meta-regression results, which provide additional insights.

Table 6 illustrates that the high DCS of a country has a positive effect on the BA−INV relationship because the estimate is positive and statistically significant. Regarding national culture, we find that estimates of individualism, uncertainty avoidance and long-term orientation are positive and statistically significant, supporting the BA−INV relationship. People in individualistic societies focus more on becoming self-sufficient rather than relying on social groups (Veiga et al., 2001). Hence, individualism supports adopting new technologies to meet their demand and pursue their goals (Taylor and Wilson, 2012; Saldanha et al., 2021). Thus, highly individualistic societies support the BA−INV relationship. For instance, a country such as the USA is highly individualistic and thus can easily adopt new technologies to achieve innovations (Saldanha et al., 2021). Similarly, a culture with high uncertainty avoidance is likely to evade future risks by doing more innovations, and thus, countries with high uncertainty avoidance support the BA−INV relationship. Similarly, a country with a long-term orientation will adopt BA to drive innovations because this will ensure long-term survival and profitability. Interestingly, indulgence is not significant in driving the BA−INV relationship. This is because indulgence is related to controlling society’s desires and impulses, which may not support digitization and innovations.

On the contrary, power distance and masculinity culture significantly but negatively impact the BA−INV relationship. High power-distant societies have a low level of horizontal communications and a high level of rigidity, which is detrimental to creative thinking, new technology adoption and innovations (Shane, 1993; Saldanha et al., 2021). Similarly, a highly masculine culture will be a more rigid society, thus detrimental to digitization and innovation. Finally, the types of economies are also significant, and it shows that developing economies harness BA more than developed economies to drive innovations (Oesterreich et al., 2022).

5. Discussion

This study suggests that BA drives innovation capability, supply chain integration and resilience. BA enhances innovation capability by providing insights from analyzing vast and varied data sets (Bahrami and Shokouhyar, 2022). These insights can lead to developing new products, services and processes that better meet customer needs or create new market opportunities.

BA significantly improves supply chain integration through improved visibility and coordination among supply chain partners. This result collaborates with Yu et al. (2021) and Li et al. (2023). By leveraging big data, organizations can track and analyze real-time information from various sources along the supply chain, including suppliers, manufacturers, distributors and customers (Zhou et al., 2023). This study confirms that BA is significantly associated with supply chain resilience, which collaborates with Al-Khatib (2022). BA is also crucial in enhancing supply chain resilience by enabling predictive analytics to identify potential risks and real-time monitoring to recover to the original state in case of disruptions quickly (Cui et al., 2023; Iftikhar et al., 2023). Companies can model various disruption scenarios and develop contingency plans by understanding and analyzing historical data.

Our findings suggest that innovation capability is significantly associated with supply chain integration and resilience. Innovation capability enhances supply chain integration by improving coordination and collaboration among partners for innovative outcomes (Singh and El-Kassar, 2019; Solaimani and van der Veen, 2022). Innovative capability also improves supply chain resilience by enabling more flexible and adaptable responses to disruptions with innovative products or services (Bahrami and Shokouhyar, 2022; Shen and Sun, 2023). This study shows that supply chain integration improves the resilience. Integrated supply chains can leverage their strong partnerships to share risks and support each other in recovery efforts, facilitating supply chain resilience. This result aligns with Li et al. (2023).

We find that innovation capability, supply chain integration and resilience are significantly associated with organizational performance. Innovation capability advances the optimization of processes, reducing costs and streamlining operations, contributing directly to improving organizational performance. This result aligns with Saleem et al. (2020) and Bag et al. (2023). Effective integration of supply chain activities significantly reduces logistics, inventory holding and procurement costs, enabling higher organizational performance (Wong et al., 2020; Li et al., 2022). Resilient supply chains can better anticipate, prepare for, respond to and recover from disruptions. This capability protects the organization from potential losses, ensuring the continuity of operations and improving organizational performance (Forbes, 2022; Yu et al., 2021).

Regarding mediating effects, this study shows that innovation capability partially mediates between BA and supply chain integration and between BA and supply chain resilience, positing an intriguing framework where innovation capability acts as a pivotal force enhancing and leveraging the benefits of BA on supply chain integration and resilience. Furthermore, supply chain integration and resilience partially mediate the relationship between innovation capability and organizational performance, underscoring a strategic framework where the effectiveness of innovation capability in enhancing organizational performance is significantly influenced by how well supply chains are integrated and resilient. When innovation capability leads to the creation of new processes, products or services, the extent to which these innovations can be integrated across the supply chain determines their impact on performance. Effective supply chain integration ensures that innovations are efficiently implemented, leading to cost savings, improved speed to market and better operational performance (Munir et al., 2020). Integrated supply chains are better positioned to be resilient (Li et al., 2023).

Finally, in meta-regression, we find that the DCS of a country is positively impacting the link between BA and innovation capability. This indicates that countries with higher DCS facilitate BA to innovation capability links more strongly than countries with lower DCS. In the case of national culture, three cultural dimensions, individualism, uncertainty avoidance and a long-term orientation, positively influence the BA−innovation capability relationship. On the other hand, power distance and masculinity dimensions negatively impact the BA to innovation capability link. Understanding these cultural nuances is crucial for multinational organizations seeking to leverage big data for innovations across diverse geographical locations. In individualistic societies, there is a stronger emphasis on personal achievements and individual rights than collective achievements (Hofstede et al., 2010; Eisend, 2019). People in such cultures are more inclined to take initiative, experiment, be competitive and embrace new ideas (Boubakri et al., 2021; Liu et al., 2021), which are essential traits for innovation capability. Thus, an individualistic culture encourages using big data for personal initiative, thereby enhancing the relationship between big data and innovation capability.

The positive impact of uncertainty avoidance on BA to innovation capability link signifies that it supports using big data to drive innovation capability. This counters the common perception that high uncertainty avoidance cultures focus on detailed planning, rules and regulations, which might seem counterintuitive to innovation (Boubakri et al., 2021). However, our results partially align with Kull et al. (2014) and Liu et al. (2021). Our results confirm that cultures high on uncertainty avoidance can use big data to develop innovation capability. This is because BA provides a structured approach to data analysis and decision-making, which bolsters the innovation capability of the organizations (Bahrami and Shokouhyar, 2022).

The positive impact of long-term orientation on BA to innovation capability link indicates that cultures with high long-term orientation prioritize future rewards over short-term gains (Hofstede et al., 2010). The long-term orientation culture encourages dealing with change, persistence and preparing for the future (Hofstede et al., 2010; Saldanha et al., 2021). As innovation benefits are generally realized in the long term, a long-term orientation culture is more likely to improve innovation capability (Saldanha et al., 2021). In relation to big data and innovation capability, organizations with long-term orientation cultures are more likely to invest in big data analytics as a long-term strategy to drive sustained innovation.

The negative impact of power distance culture signifies that large power distance is detrimental for the BA to innovation capability link. This is because large power distance promotes hierarchy and hampers communication and collaboration among people of different social strata, which are detrimental to innovation (Shane, 1993; Saldanha et al., 2021). In masculine culture, the focus is on achievement, competitiveness and ambition for success (Hofstede et al., 2010). Such ambitions for success or achievement are common reasons for unethical behavior (Vitell and Festervand, 1987; Eisend, 2019), which may be detrimental to innovation.

While analyzing the type of economies, we find that firms operating in developing economies harness more benefits from BA to drive innovations than developed economies (Oesterreich et al., 2022). Developing economies often have the advantage of leapfrogging, which means they can skip over traditional technologies directly to achieve advanced solutions (Chandrasekaran et al., 2022). Adopting big data in developing economies can leapfrog older analytical methods, enabling these firms to adopt cutting-edge practices for data-driven decision-making quickly.

5.1 Theoretical implications

This study offers multiple theoretical implications. First, building on OIPT, we explore how BA can enhance the information processing capacity of supply chains. While OIPT traditionally emphasizes the development of analytical capabilities within the supply chain, we extend the theory by identifying key conditions − innovation capability, supply chain integration and resilience − under which supply chains can leverage these information processing capabilities to boost organizational performance. Despite the continuous advancement of big data, it is important to note that our theoretically grounded research model, which considers the varying roles of innovation capability, supply chain integration and resilience, remains robust. According to OIPT, an organization’s ability to effectively process information − by gathering, analyzing and using it for decision-making − is crucial for its performance, especially in the face of environmental uncertainties. By enhancing an organization’s capacity to manage disruptions, supply chain integration and resilience directly support its information processing capabilities, thereby improving organizational performance. This study is one of few that analyzed how BA and innovation capability complement each other to drive supply chain integration, resilience and organizational performance, which was not collectively studied in existing studies, meta-analyses or reviews to ascertain the direct and mediating mechanisms (Aryal et al., 2020; Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Bag and Rahman, 2023; Alvarenga et al., 2023).

Second, our study offers integrated and more definitive results regarding identified relationships. More precisely, we provide statistically significant direct effects with the help of meta-analysis and meta-SEM to remove the ambiguity in the literature. For instance, BA significantly and positively influences innovation capability, supply chain integration and resilience, which counters many fragment views (e.g. Cappa et al., 2021; Jum’a et al., 2024). Our results partially align with a few studies exploring supply chain-related factors such as integration or resilience in conjunction with innovations (Morita and Machuca, 2018; Bag et al., 2023). For instance, Morita and Machuca (2018) find that many companies are making trade-offs between innovation capability and supply chain capabilities, i.e. they usually prioritize strengthening the supply chain capability more than innovations to achieve better performance. Our study clarified that innovation capability improves organizational performance and significantly enables supply chain-related capabilities such as integration or resilience.

Third, apart from the above definitive relationships, mediation analysis contributes to academia in identifying significant mediating mechanisms related to innovation capability, supply chain integration and resilience. Innovation capability partially and significantly mediates between BA and supply chain integration/resilience. The study findings contribute to the literature because the mediating role of innovation capability between BA and supply chain integration/resilience is largely unexplored in the literature (Wang and Ali, 2023; Arias-Pérez et al., 2022; Zhao et al., 2023; Zhou et al., 2023). Furthermore, supply chain integration and resilience partially mediate between innovation capability and organizational performance, whereas supply chain resilience mediates between integration and organizational performance. These additional mediating mechanisms also add to the literature, which mostly focused on direct relationships (Calic and Ghasemaghaei, 2021; Zhao et al., 2023).

Fourth, meta-regression provides valuable insights related to DCS, national culture, type of economies and GDP per capita in the BA and supply chain context. In fact, our study is the first one to examine the effects of DCS and all dimensions of national culture on the BA−innovation capability relationship, thus contributing to the BA and supply chain literature (Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023; Nakandala et al., 2023). For instance, the Oesterreich et al. (2022) meta-analysis captured moderators such as economic regions, publication year between big data resources, capabilities and firm performance; however, it fails to identify the role of DCS and national culture in the context of BA and supply chain innovation. Dubey et al. (2023) used government effectiveness as a moderator between agility and supply chain resilience, whereas they ignored the importance of DCS, national culture and type of economies as moderators. These moderators are important to investigate because DCS reflects a country’s digital capability, national culture indicates how society accepts/rejects new technology adoption and drives innovations, and the type of economies and GDP per capita reflects the development stage of a country, which may significantly influence the BA to innovation capability relationship.

Finally, integrating meta-analysis, meta-SEM and meta-regression enables a unique understanding regarding BA and innovation capability in relation to other factors that cannot be accomplished within a single study dealing with a specific region or culture. Our analysis offers an understanding of the nuances of organizational capabilities and reconciles the inconsistencies among primary studies and the specific impacts of factors (Oesterreich et al., 2022; Ansari and Ghasemaghaei, 2023).

5.2 Practical implications

Big data is captured through evolving digital technologies such as intelligent sensors, RFID tags, GPS locations and social media, which generate large data sets. Thus, managers must extract value from such a large data set and transition from big data to BA. This transition encompasses retrieving unknown patterns and insights from big data, its interpretations and extracting meaningful actions (Gupta et al., 2020; Hallikas et al., 2021).

Our study confirms that organizational capabilities in terms of BA and innovation enable supply chain integration and resilience. Managers must simultaneously concentrate on BA and innovation capability rather than making a trade-off between capabilities (Morita and Machuca, 2018; Lin et al., 2023) to drive supply chain integration, resilience and performance. Hence, our findings clarified confusion among practitioners and confirmed that BA improves innovation capability, consequently enabling higher supply chain integration and resilience. Thus, managers investing in innovation capability will be more confident about integration, resilience and performance outcomes.

Furthermore, to remove ambiguity among practitioners regarding investing in big data analytics (Aryal et al., 2020; Chatterjee et al., 2022), we highlight three important advantages: (i) BA directly improves innovation capability, (ii) BA facilitates supply chain integration and resilience, which helps to improve organizational performance and (iii) innovation capability directly and indirectly improves organizational performance. Hence, managers investing in big data analytics benefit on all three fronts. Along similar lines, Lehrer et al. (2018) observed that many sectors, such as banking, marketing and manufacturing, provide better customer service with the help of big data analytics. Customer data about their lifestyles can become a significant information source for organizations. Hence, data related to customers’ expectations and competitors can help firms to predict future innovations. BA creates some opportunities regarding innovation (i.e. product or process), which is an important pillar in creating a competitive advantage for organizations (Saleem et al., 2020).

Finally, to reap more benefits from investment decisions, decision-makers should pay attention to a few indicators such as DCS, the type of economies and the country’s national culture while launching big data projects. This is because one of the unanticipated negative effects of information technology use is the rebound effect, and prior studies found that the efficiency achieved from technology may not necessarily improve performance (Dieste et al., 2023). Thus, an in-depth understanding of big data analytics and its outcomes in different countries would help business managers and policymakers to avoid potential pitfalls and plan a more focused strategy toward innovations.

6. Conclusion and limitations

This study attempts to integrate the fragmented outcomes related to BA and innovation capability in sync with supply chain integration, resilience and performance with the help of meta-analysis, meta-SEM and meta-regression. The use of different methods of meta-analysis provides a more definitive direction for future researchers. Extant literature used different theoretical perspectives to examine the relevance of big data analytics in facilitating supply chain integration, resilience and performance. Meanwhile, how all simultaneously work toward innovations in a big data context is unexplored. This study offers a nuanced understanding of innovation capability with the help of OIPT and removes the inconsistencies among studies (Oesterreich et al., 2022; Dubey et al., 2023; Wang et al., 2024). This study fills the existing research void by statistically confirming the role of BA, innovation capability related to supply chain integration, resilience and performance through both direct and mediating mechanisms. Such mediation analysis is important because it demonstrates how different types of constructs are interlinked in technology adoption literature to drive various outcomes. Furthermore, meta-regression analysis revealed that the DCS, national culture, type of economies and GDP per capita influence the BA−INV relationship. The analysis provides multifaceted insights for policymakers regarding using BA to drive innovations based on country-specific culture and indicators.

Apart from a list of important findings, this study has some limitations. First, our study focused on BA, innovation capability, supply chain integration and resilience to drive organizational performance. However, many other factors such as organizational design and regulations may also influence supply chain innovation and thus need a more comprehensive view of research. Future research may include various types of innovations in the supply chain to capture larger data ecosystems related to innovations.

Author Affiliation

Figure 1

Research model

Figure 2

Meta-analysis procedure

Figure 3

Emergent model

Table 1

Construct definitions

Constructs	Description	Aliases	Representative studies
Big data adoption(BA)	Big data adoption refers to “a firm’s ability to assemble, integrate and deploy its big data-specific resources”	Data analytics, supply chain analytics	Edwin Cheng et al. (2022)Li et al. (2023)
Innovation capability (INV)	It can be defined as “a firm’s ability to generate, accept, and implement new ideas, processes, products, or services” to create or improve value for the stakeholders	Product/process/service innovation, innovative performance	Wang and Dass (2017); Bahrami and Shokouhyar (2022)
Supply chainintegration (SCI)	Supply chain integration is the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra and inter-organizational processes	Supplier/customer/internal integration, supply chain collaboration	Flynn et al. (2010)Liu et al. (2021)
Supply chainresilience (SCR)	Supply chain resilience refers to a supply chain’s ability to return to its original state within an acceptable period after being disturbed	Disaster capability, supply chain survivability, firm resilience	Gupta et al. (2022)Lin et al. (2023)
Organizationalperformance (OP)	Organizational performance includes performance measures such as average return on investment, profit, sales growth, market share increases and improvement in delivery, quality and product/service performance	Firm performance, supply chain performance, operational performance	Gawankar et al. (2020); Gupta et al. (2020)

Source: Authors’ own work Table 2

Summary of the meta-analysis-based papers and the moderators

References	Type of moderators
Cao and Lumineau (2015)	Country, national culture, institutional environments, interorganizational relationship
Eisend (2019)	National culture, GDP per capita, Internet users per 100 inhabitants, rule of law of countries
Govindan et al. (2020)	Type of economy (developed, developing) and industry, year of study
Suurmond et al. (2020)	Industry, national culture, GDP per capita, publication data and year
Chen et al. (2023)	Type of economy (developed, developing) and industry
Akın Ateş et al. (2022)	National culture, country, supply chain complexity, industry and data sources
Kumar et al. (2022)	National culture, study design, sample type and individual characteristics
Karim et al. (2022)	Information and communication technology development index
Oesterreich et al. (2022)	Type of economy (developed, developing), performance and publication year
Alfalla-Luque et al. (2023)	Type of economy (developed, developing) and industry
Liu et al. (2024)	Industry, type of economy (developed, developing), firm size and certification

Source: Authors’ own work Table 3

Meta-analysis results

Relationship	No. ofstudies	Correctedeffect size	Totalsample size	Confidence interval (95%)		Prediction interval		Q-statistic forhomogeneitytest	Z-value
				Lowerlimit	Upperlimit	Lowerlimit	Upperlimit
BA−INV	55	0.55	15,996	0.50	0.60	0.05	0.83	1280.5***	16.87***
BA−SCI	17	0.60	5,043	0.52	0.68	0.18	0.84	276.11***	12.14***
BA−SCR	15	0.47	3,846	0.36	0.56	0.06	0.74	150.46***	8.54***
BA−OP	44	0.49	12,528	0.43	0.54	0.04	0.77	731.8***	14.92***
INV−SCI	9	0.63	2,582	0.48	0.74	0.08	0.88	171.1***	8.02***
INV−SCR	4	0.46	1,056	−0.03	0.77	−0.59	0.93	84.47***	2.99**
INV−OP	37	0.58	10,135	0.49	0.66	−0.09	0.89	1316.4***	10.68***
SCI−SCR	4	0.53	1,061	0.01	0.83	−0.54	0.95	85.99***	3.20**
SCI−OP	13	0.57	3,784	0.45	0.67	0.06	0.85	246.7***	8.68***
SCR−OP	7	0.64	1,891	0.46	0.77	0.12	0.88	94.87***	7.17***

Notes:

p < 0.001 (***); p < 0.01(**)

Source: Authors’ own work Table 4

Meta-analytic correlations among constructs

Sl. no.	Construct	Mean	SD	BA	SCI	SCR	INV	OP
1	BA	3.37	1.17	1	17b(5,043)	15(3,846)	55(15,996)	44(12,528)
2	SCI	3.24	1.08	0.60a	1	4(1,061)	9(2,582)	13(3,784)
3	SCR	3.46	0.95	0.47	0.53	1	4(1,056)	7(1,891)
4	INV	3.35	1.06	0.55	0.63	0.46	1	37(10,135)
5	OP	3.48	1.04	0.49	0.57	0.64	0.58	1

Notes:

^aEntries in the lower half are corrected correlations; ^brepresents the number of studies (sample size)

Source: Authors’ own work Table 5

Mediation test analysis

Hypothesis	Relationship	Mediating through	Direct effect	Indirect effect	Mediating effect (%)	Mediation
Hypothesis 2a	BA → SCI	INV	0.33***	0.22***	40%#	Partial mediation
Hypothesis 2b	BA → SCR	INV	0.16***	0.07**	30%	Partial mediation
Hypothesis 3	INV → OP	SCI	0.27***	0.07**	19%	Partial mediation
Hypothesis 4	INV → OP	SCR	0.27***	0.07**	18%	Partial mediation
Additional insights	SCI → OP	SCR	0.15***	0.13***	46%	Partial mediation

Note:

^#indicates that about 40% of the effect of BA on SCI is mediated by INV

Source: Authors’ own work Table 6

Meta-regression results (BA−INV)

Parameter	Estimate	SE	t-value
Intercept (γ00)	−1.494***	0.482	−3.098
DCS (γ01)	0.0247***	0.005	4.580
Individualism (γ02)	0.011***	0.002	4.720
Uncertainty avoidance (γ03)	0.013***	0.003	4.636
Long-term orientation (γ04)	0.006***	0.001	4.442
Indulgence (γ05)	0.0013	0.001	0.920
Power distance (γ06)	−0.009**	0.003	−2.69
Masculinity (γ07)	−0.006***	0.001	−4.40
Type of economy (γ08)	−0.372***	0.103	−3.601
GDP per capita (γ09)	−0.00002***	0.00000	−6.186
Number of effects	34

Notes:

***(p < 0.01); **(p < 0.05); *(p < 0.1); ns = (p > 0.1)

Source: Authors’ own work Table A1

Studies included in meta-analysis

Sl. No.	Study topic
1	A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context
2	Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture
3	An empirical investigation on how big data analytics influence China SMEs performance: do product and process innovation matter?
4	Antecedents to firm performance and competitiveness using the lens of big data analytics: a cross-cultural study
5	Assessing the impact of big data on firm innovation performance: big data is not always better data
6	Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: an empirical study
7	Big data analytics as a roadmap towards green innovation, competitive advantage and environmental performance
8	Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
9	Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity
10	Big data analytics capabilities and MSME innovation and performance: a double mediation model of digital platform and network capabilities
11	Big data analytics capabilities and organizational performance: the mediating effect of dual innovations
12	Big data analytics capabilities and performance: evidence from a moderated multi-mediation model
13	How big data analytics use improves supply chain performance: considering the role of supply chain and information system strategies
14	Big data analytics capabilities for reinforcing green production and sustainable firm performance: the moderating role of corporate reputation and supply chain innovativeness
15	Big data analytics capability as a major antecedent of firm innovation performance
16	Big data and big disaster: a mechanism of supply chain risk management in global logistics industry
17	Big data for social benefits: innovation as a mediator of the relationship between big data and corporate social performance
18	Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation
19	Boosting innovation performance through big data analytics: an empirical investigation on the role of firm agility
20	Business intelligence and analytics use, innovation ambidexterity and firm performance: a dynamic capabilities perspective
21	Consequential factors of big data’s analytics capability: how firms use data in the competitive scenario
22	Customer involvement in big data analytics and its impact on B2B innovation
23	Data-driven supply chain capabilities and performance: a resource-based view
24	Does data-driven culture impact innovation and performance of a firm? An empirical examination
25	Effect of eco-innovation on green supply chain management, circular economy capability and performance of small and medium enterprises
26	Green innovation and organizational performance: the influence of big data and the moderating role of management commitment and HR practices
27	How and when do big data investments pay off? The role of marketing affordances and service innovation
28	How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?
29	Investigating the influence of big data analytics capabilities and human resource factors in achieving supply chain innovativeness
30	Knowledge absorption capacity’s efficacy to enhance innovation performance through big data analytics and digital platform capability
31	Leveraging Internet of things and big data analytics initiatives in European and American firms: is data quality a way to extract business value?
32	New product success through big data analytics: an empirical evidence from Iran
33	Performance effects of analytics capability, disruption orientation and resilience in the supply chain under environmental uncertainty
34	Role of big data analytics in developing sustainable capabilities
35	IT capabilities, strategic flexibility and organizational resilience in SMEs post-COVID-19: A mediating and moderating role of big data analytics capabilities
36	Strategic business value from big data analytics: an empirical analysis of the mediating effects of value creation mechanisms
37	Supply chain analytics adoption: determinants and impacts on organisational performance and competitive advantage
38	Supply chain management professionals’ proficiency in big data analytics: antecedents and impact on performance
39	Sustainable competitive advantage driven by big data analytics and innovation
40	Sustainable supply chain management performance in post COVID-19 era in an emerging economy: a big data perspective
41	The determinants of export performance in the digital transformation era: empirical evidence from manufacturing firms
42	The impact of big data analytics and data security practices on service supply chain performance
43	The impact of big data analytics capabilities on green supply chain performance: is green supply chain innovation the missing link?
44	The impact of business analytics capability on data-driven culture and exploration: achieving a competitive advantage
45	The role of alliance management, big data analytics and information visibility on new-product development capability
46	The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view
47	The role of big data analytics in manufacturing agility and performance: moderation–mediation analysis of organizational creativity and of the involvement of customers as data analysts
48	The role of business analytics capabilities in bolstering firms’ agility and performance
49	Transforming big data into knowledge: the role of knowledge management practice
50	Understanding big data-driven supply chain and performance measures for customer satisfaction
51	Unlocking the drivers of big data analytics value in firms
52	Data analytics capability and servitization: the moderated mediation role of bricolage and innovation orientation
53	The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events
54	The impact of supply chain concentration on integration and business performance
55	The impact of information technology usage on supply chain resilience and performance: an ambidextrous view
56	Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach
57	The role of information governance in big data analytics driven innovation
58	Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – a multi-theoretical approach
59	Data-driven digital transformation for emergency situations: the case of the UK retail sector
60	Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?
61	Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity and the mediating role of virtual enterprise
62	Exploring big data use to predict supply chain effectiveness in Chinese organizations: a moderated mediated model link
63	Data-driven capabilities, supply chain integration and competitive performance: evidence from the food and beverages industry in Pakistan
64	Big data analytics in supply chain and logistics: an empirical approach
65	Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19
66	Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience
67	Impacts of IT capability and supply chain collaboration on supply chain resilience: empirical evidence from China in COVID-19 pandemic
68	Does big data enhance firm innovation competency? The mediating role of data-driven insights
69	Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment
70	Examining the influence of big data analytics and additive manufacturing on supply chain risk control and resilience: an empirical study
71	Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities
72	Exploring the impact of big data analytics capabilities on business model innovation: the mediating role of entrepreneurial orientation
73	Big data and big disaster: a mechanism of supply chain risk management in global logistics industry
74	Building SMEs’ resilience in times of uncertainty: the role of big data analytics capability and co-innovation
75	Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience
76	Optimizing firm’s supply chain resilience in data-driven business environment

Source: Authors’ own work

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Further reading

Schmidt, F.L. and Hunter, J.E. (2014), Methods of Meta-Analysis: Correcting Error and Bias in Research Findings, Sage publications, London.

Appendix

Table A1