Enabling innovation with big data analytics capabilities: the moderating role of organizational learning culture

PurposeThe prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.Design/methodology/approachThis research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).FindingsThe novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.Research limitations/implicationsThe novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.Practical implicationsPracticing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.Originality/valueTo the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

Purpose In the era of digital transformation, leveraging big data has become a strategic imperative for sustaining competitive advantage. However, the impact of Big Data Analytics Capability (BDAC) on firm performance depends on the development of complementary organisational capabilities and governance mechanisms. Grounded in the integrated model, this study aims to investigate how BDAC influences firm performance through the mediating roles of organisational capabilities and the moderating role of data governance. Design/methodology/approach Survey data from 227 Forbes Global 2000 firms were analysed using partial least squares hierarchical component modelling (HCM). Findings Dynamic and innovative capabilities significantly mediate the BDAC–performance relationship. Data governance strengthens the effect of BDAC on dynamic and operational capabilities but not on innovative capability. Research limitations/implications HCM improves model parsimony for complex capability–performance relationships, while cross-validated predictive ability test (CVPAT) confirms strong predictive validity and explains the internal mechanisms linking BDAC to performance. These methodological insights strengthen the theoretical understanding of how data-driven capabilities create value. Practical implications The findings suggest that managers should leverage BDAC to enhance dynamic and innovative capabilities, supported by robust data governance that reinforces BDAC's influence on dynamic and operational capabilities. Effective governance fosters organisational agility, enabling firms to sustain performance and support broader sustainability objectives. Originality/value BDAC influences firm performance through dynamic, operational and innovative capabilities, with a contingent effect of data governance. The study advances theory by integrating the resource-based view with the dynamic capabilities perspective and applies rigorous predictive modelling to clarify these mechanisms.

Despite big data analytics (BDA) capabilities have been increasingly recognized for their potential to improve sustainability, the underlying mechanisms by which BDA capabilities influence hospital environmental performance in the context of healthcare supply chains are not well understood. This paper aims to bridge this significant empirical void by examining the mediating effect of supply chain innovation, decision-making quality and risk-taking on the links between BDA capabilities and environmental performance for Chinese hospitals. Based on Stimulus-Organism-Response theory, the theoretical model depicts BDA capability as a key stimulus factor affecting the hospital sustainability outcomes. This research employed a quantitative research method, and a structured survey instrument was administered to 653 healthcare providers from various hospitals. The participants were recruited using a random sampling method to achieve broad representation. Variables in the survey include measures of big data analytics capability, supply chain innovation, quality of decision-making, risk-taking, and environmental performance. AMOS was used for Structural Equation Modeling (SEM) analysis to test the proposed relationships and mediation effects among the variables in due diligence. Empirical results support a positive relationship between hospitals’ BDA capability and environmental performance, indicating that it is statistically significant. Crucially, this link is to some degree mediated by supply chain innovation, quality of decision-making and risk taking behaviour. In particular, hospitals with high analytical capability were more innovative in their supply chain production, had better decision-making structures, and showed a tendency to be more risk takers, leading to good environmental outcomes. This research limns the manner in which improving BDA capabilities can systematically contribute to hospital sustainability in innovative, informed, and strategically bold supply chain management practices; thereby new theoretical and practical aspects are provided. These results not only enrich current theoretical constructs but also give insights to healthcare managers and policy makers for harnessing the big data analytics to promote environmental sustainability and implement a real change in the healthcare performance.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-16541-0.

PurposeThe purpose of this study is to investigate the impact of big data (BD) analytics capabilities (BDACs) on green supply chain integration (GSCI) and green innovation (GI) in the context of a developing country, Jordan. In addition, the mediating effect of GSCI on the relationship between BDAC and GI is investigated.Design/methodology/approachData collection was carried out through a survey with 300 respondents from food and beverages manufacturing firms located in Jordan. Partial least squares-structural equation modeling (PLS-SEM) technique was applied to analyze the collected data. Natural resource-based view (NRBV) theory was the adopted theoretical lens for this study.FindingsThe results revealed that BDAC positively and significantly affects both GSCI and GI. In addition, the results demonstrated that GSCI positively and significantly affects GI. Further, it is also found that GSCI positively and significantly mediates the relationship between BDAC and GI.Originality/valueThis study developed a theoretical and empirical model to investigate the relationship between BDAC, GSCI and GI. This study offers new theoretical and managerial contributions that add value to the supply chain (SC) management literature by testing the mediation model in food and beverages manufacturing firms located in Jordan.

PurposeThis study aimed to examine the role of big data analytics capabilities (BDAC) in fostering organizational innovation capabilities and, consequently, in achieving economic, social and environmental sustainability.Design/methodology/approachThrough the lens of dynamic capability theory, this study surveyed 115 hotels using purposive sampling to gain in-depth insights regarding the factors affecting organizational sustainability in the hospitality industry. The data analysis was conducted using partial least squares-structural equation modeling (PLS-SEM).FindingsThe findings reported a substantial impact of seven core dimensions (i.e. technology, data, basic resources, technological skills, managerial skills, organizational learning and data-driven culture) in building BDAC among hotels. Moreover, BDAC was also revealed to significantly influence innovation capabilities, positively impacting all three sorts of sustainability performance. Innovation capability also mediated the relationship between BDAC and all sustainability factors.Practical implicationsThe findings will assist policymakers and practitioners in developing effective initiatives to enhance the adoption and implementation of data science and technologies, substantially contributing to the “National IR 4.0 Policy” and “Malaysia Digital Economy Blueprint” and achieving sustainable development goals (SDGs).Originality/valueThe originality of this study is established by investigating the interplay between BDAC, innovation capability and sustainability performance, particularly in the context of the hotel industry, whereas the existing studies focus on exploring the advantages of BDA.

In order for organisations to generate competitive advantages from big data investments, they need to acquire a unique blend of technology, human skills, financial resources and a data-driven culture. Organisations need to measure their big data analytics capability in order to yield competitive performance. This study sought to examine the relationship between a firm's big data analytics capability (BDAC) and competitive performance through mediating role of dynamic and operational capabilities. To test the proposed research model, we used survey data from 110 employees across 54 insurance companies in Kenya. Using partial least squares structural equation modelling, the results provide evidence that BDAC leads to superior firm performance. Various resources that form big data analytics (BDA) capability have been identified and an instrument to measure BDAC is proposed. The findings from this study provide a roadmap strategy for implementing BDA projects.

Purpose: Big Data Analytics Capabilities (BDAC) facilitate the generation of critical insights required for competitive performance. This study evaluates the relationship between Big Data Analytics Capabilities and competitive performance and argues the effect is mediated by Business Model Innovation (BMI). This is assessed through the theoretical lens of Dynamic Capabilities, where Big Data Analytics Capabilities enabled sensing identification opportunities that initiate the mobilisation of resources to transform firms’ business models, via BMI, to enhance performance. Design/methodology/approach: A quantitative research approach using a survey was utilised. Data from 272 firms were collected. Findings/results: The research model is evaluated by Partial Least Squares Structural Equation Modelling. The findings show that Big Data Analytics Capabilities have a direct and indirect influence on competitive performance, where BMI mediates the latter. The results enrich Dynamic Capabilities, Big Data Analytics Capabilities and BMI literature by demonstrating that Big Data Analytics Capabilities have a positive effect on BMI and subsequently competitive performance, to create value for firms and their stakeholders. Practical implications: Practitioners need to invest in Big Data Analytics Capabilities to enhance the probability of success of their BMI endeavours. Originality/value: Firstly, organisations’ efforts in nurturing big data infrastructure, human resources, and data-driven cultures drive actions that enhance both operational and strategic execution, leading to enhanced performance. Secondly, the positive effect of Big Data Analytics Capabilities is carried through BMI to influence competitive performance positively, thus suggesting that the sensing enabled by Big Data Analytics Capabilities leads to transformational activities that drive performance.

PurposeWhile big data analytics can spur innovation among firms, it is unclear whether it can effectively drive value creation, value proposition, value delivery and value capture to deal with disruptions and the ever-changing demands of customers. This study therefore aims to examine how value creation, value proposition, value delivery and value capture can be improved through big data analytics capability (BDAC). This study advances the discourse by investigating how the market environment and strategic orientations play significant but little-studied roles in enhancing or lessening BDAC’s impact on business model innovation (BMI).Design/methodology/approachDrawing on dynamic capability and contingency perspectives, a model of five hypotheses was developed and validated using survey data from 208 managers of manufacturing firms in Ghana. Covariance-based structural equation modeling was used for the analysis.FindingsThe findings revealed that BDAC and strategic orientation (market and learning) directly influence the dimensions of BMI (value creation, value proposition, value delivery and value capture). The findings further showed that strategic orientations partially mediate the BDAC–BMI link. The authors also noted that the BDAC–BMI link is amplified at high levels of market dynamism.Practical implicationsThe findings suggest that investing in BDA alone may not be sufficient to drive superior business model innovation. However, market orientation and continuous learning are crucial to fully realizing BDAC’s full potential in enabling value creation, value proposition, value delivery and value capture, especially in a dynamic market environment.Originality/valueThis study contributes to existing BMI literature by being the first to examine how BDAC facilitates value creation, value proposition, value delivery and value capture in developing countries. This paper also advances BM literature by theorizing and validating important but rarely studied roles of strategic orientations and market dynamism. Thus, this paper extends the understanding of the conditions and mechanisms through which the effect of BDAC on value creation, value proposition, value delivery and value capture can be optimized.

PurposeThe literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.Design/methodology/approachThe authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.FindingsThe results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.Originality/valueAnalyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

Big data analytics capabilities and firm performance: An integrated MCDM approach

Extant research shows that big data analytics (BDA) capability is often employed as a part of organizational resources to enhance firm performance. Drawing upon the resource-based view, dynamic capabilities, and contingency theory, this study endeavors to examine the alignment between BDA capability and a specific type of procurement strategies (i.e., supplier development) and its impact on firm performance. The study extends the BDA capability research by investigating the direct impact of BDA capability on supplier development and firm performance, respectively, and by exploring both mediating and moderating effects on the relationship between supplier development and firm performance. The main results show that a firm’s BDA capability has not only a direct positive significant impact on supplier development, but also a direct positive significant impact on its business performance. More importantly, the results indicate strong moderating and mediating effects of BDA capability on supplier development, which in turn affects the improvement of firm performance. Theoretical and managerial implications along with future research directions are provided in the end.

BackgroundAs the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.MethodsThis scoping review was conducted following Arksey and O’Malley’s 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.ResultsOf 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).ConclusionsThis review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

Manuscript type: Research paper Research aims: Drawing from the lens of dynamic capability view (DCV), this study investigates whether companies with big data analytics (BDA) capabilities, specifically BDA management, infrastructure, and personnel capabilities, disclose more sustainability posts on social media and whether such disclosure affects their competitive advantage. Design/Methodology/Approach: Data from 100 public listed firms in Malaysia were obtained from questionnaires and content analysis of Facebook pages. Smart PLS was employed to analyse the data. Research findings: The results suggest that in the context of Malaysia, BDA management capability significantly impacts sustainability reporting on social media (SRSM). The evidence also points to SSRM positively impacting a company’s competitive advantage. Theoretical contribution/Originality: Theoretically, this study contributes to the literature on DCV. The findings provide insights into how BDA capabilities can help organisations focus on social media platforms and communicate with their stakeholders on sustainability performance. It also suggests that sustainability reporting on social media is associated with competitive advantage, as it allows for two-way interaction between organisations and its stakeholders. Practitioner/Policy implications: Practically, this study provides insights into the roles of accounting, social media, and big data within the current digital revolution. Specifically, it offers guidance to executives and managers on identifying the conditions that need to be present for BDA capability to add value to SRSM. Additionally, the findings here have implications for policymakers and businesses looking to use BDA in the context of SRSM to gain competitive advantage. Research limitation/Implications: Future studies could consider increasing the sample size. This study sheds light on the relevance of BDA capabilities in promoting sustainability issues using social media.

Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms

Toward the development of a big data analytics capability