The causal mechanisms linking Big Data Analytics Capability (BDAC) to AI-Driven dynamic capabilities: A mixed-methods investigation

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.

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.

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

Many studies on big data analytics provide empirical evidence showing that Big Data Analytics Capabilities (BDAC) do not always result in value creation or competitive advantage. This study sheds light on how entrepreneurial orientation (EO) mediates the relationship between big data analytics capabilities (BDAC) and value creation in SMEs. The study employed a mixed-method design, integrating desk research, executive interviews, and a survey of 447 Canadian SME leaders. Partial least squares structural equation modelling(PLS-SEM) analysis, supported by reliability and validity diagnostics, common-method-bias tests, and model-fit assessments, ensured the robustness of results and enabled triangulation across methods. Findings reveal that EO mediates the BDAC–value creation relationship, suggesting that SMEs with stronger EO leverage BDAC more effectively. Thus, developing EO is essential for unlocking BDAC's value-creation potential. A practical implication for senior executives is that employing EO – proactiveness, risk-taking, and innovativeness – can lead to better leverage of BDAC insights for value creation. A theoretical implication is that EO can complement other organizational capabilities such as analytics culture to extract greater value from BDAC. The empirical evidence provided in this study complements previous studies that have examined other mediators such as analytics culture and absorptive capacity. It provides additional insights into how SMEs can leverage their BDAC for value creation by being more innovative, proactive and encouraging risk-taking across the organization.

Objective: This study aims to investigate the impact of artificial intelligence (AI) on digital marketing performance, considering the mediating role of dynamic organizational capabilities (DOC) and big data analytics capability (BDAC). The research seeks to provide a comprehensive understanding of the mechanisms through which AI enhances digital marketing outcomes and the contextual factors that facilitate this impact. Methodology: The study employs a structural equation modeling (SEM) approach to analyze data collected from  samplle size 226 professionals working in e-commerce, digital media, and marketing services companies in  Mashhad city of Iran. . The research utilizes standardized questionnaires to measure AI, digital marketing performance, DOC, and BDAC. The validity and reliability of the measurement instruments are assessed using content validity ratio (CVR), content validity index (CVI), and Cronbach’s alpha coefficient. The study uses proportionate stratified random sampling to ensure representativeness of the sample. Findings: The results provide empirical evidence that AI has a positive and significant impact on digital marketing performance, both directly (β = 0.37, p < 0.001) and indirectly through the mediating effects of DOC (β = 0.12, p < 0.01) and BDAC (β = 0.15, p < 0.001). The findings also demonstrate that AI positively influences DOC (β = 0.42, p < 0.001) and BDAC (β = 0.45, p < 0.001), which in turn enhance digital marketing performance (β = 0.29, p < 0.001 and β = 0.33, p < 0.001, respectively). Conclusion: This study contributes to the literature on AI and digital marketing by providing empirical evidence on the impact of AI on digital marketing performance and the underlying mechanisms through which AI enhances marketing outcomes. The findings highlight the importance of developing organizational capabilities and leveraging big data analytics to fully realize the potential of AI in digital marketing. The study offers practical insights for managers and practitioners, emphasizing the need to invest in AI technologies, develop DOC and BDAC, and undertake broader organizational transformations to effectively harness the power of AI in digital marketing.

PurposeThis study explores the connection between big data analytics capabilities and the competitive advantage of the manufacturing sector in Jordan through the mediating role of green radical innovation and green incremental innovation as well as the moderating role of a data-driven culture.Design/methodology/approachFor the purpose of this study, 356 questionnaires were analysed. Convergent validity and discriminant validity tests were performed through structural equation modelling in the Smart-PLS programme, and the data reliability was confirmed. A bootstrapping technique was used to analyse the data. The mediating effect for green radical and green incremental innovation and the moderating effect for data-driven culture were performed.FindingsThe empirical results showed that the proposed moderated-mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between big data analytics capabilities and the competitive advantage as well as a mediating effect of green radical innovation and green incremental innovation. It was confirmed that there is a moderating relationship for data-driven culture between green radical innovation, green incremental innovation and competitive advantage.Research limitations/implicationsThis cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalization of its results, and the results are limited to one country.Originality/valueThis research developed a theoretical model to incorporate big data analytics capabilities, green radical innovation, green incremental innovation, data-driven culture, and competitive advantage. This study provides new findings that bridge the existing research gap in the literature by testing the moderated mediation model with a focus on the organizational benefits of big data analytics capabilities to improve levels of green innovation and competitive advantage in the Jordanian manufacturing sector.

As the economic environment of the global market is rapidly changing, uncertainty is increasing, and technological innovation, as well as financial and human resources, are becoming important factors. In particular, small and medium-sized export manufacturing companies are building global supply chains to improve their competitive advantage, and big data analytics capabilities to collect and analyze manufacturing and import/export information have been raised as a way to solve these risks. Therefore, this study analyzed the influence of big data analytics capabilities on supply chain performance through logistics flexibility and supply chain resilience in the supply chain formation of small and medium-sized export manufacturing firms. The survey was distributed to small and medium-sized export manufacturing companies from January 3, 2024, to March 31, 2024, through face-to-face, email, mail, and fax, focusing on the list of member companies provided by the Korea International Trade Association, the Ministry of SMEs and Startups, the Smart Manufacturing Innovation Association, and the SME Technology Information Promotion Agency. More than 2,000 copies were distributed and 215 copies (21.5%) were collected, and 203 copies of the questionnaire were constructed after removing 12 non-response questionnaires. Based on the collected questionnaires, the analysis was conducted using SPSS Ver. 28.0 and the extension program Process Macro Ver. 4.2. The results of the empirical analysis of this study are as follows. First, big data analytics capabilities have a positive impact on supply chain performance. Second. big data analytics capabilities have a positive effect on supply chain performance through the mediation of logistics flexibility. Third, big data analytics capabilities have a positive effect on supply chain performance through the mediation of supply chain resilience. Fourth, big data analytics capabilities have a positive effect on supply chain performance through the dual media.

Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability.

The impact of business intelligence, big data analytics capability, and green knowledge management on sustainability performance

The purpose of this study is to investigate how Big Data Analytics is used and perceived to affect a firm's competitive advantage. The study will focus on the telecommunication sector, due to the high volume of data being used for analysis in this sector; it is always an area of great interest. This paper investigates the role that Big Data Analytics Capabilities play in achieving a competitive advantage in the Egyptian telecommunication sector. Using the Dynamic Capabilities framework, this paper looks at how a firm can gain an advantage from big data. For collecting data, the study adopted the questionnaire method; the study used the questionnaire-based survey method; the study used two questionnaires; one for big data analysis department employees to investigate the independent variable Big Data Analytics capabilities, and the other one for the top and middle managerial levels employees to investigate the dependent variable competitive advantage. The results show that there is a statistically significant impact of "Big Data Analytics Capabilities" on Competitive advantage. Infrastructure flexibility and management capabilities were relatively more important than Personnel Expertise between all the dimensions of Big Data Analytics capabilities. But these differences are small, so all the dimensions should be given the same value in building Big Data Analytics capabilities. Management in the telecommunication sector should take into account that enhancing Big Data capabilities and considering big data as part of their operations can improve their Competitive Advantage and their Performance.

This research explores the transformative role of Big Data Analytics Capabilities (BDAC) in fostering organizational sustainability within the modern digital marketplace. Grounded in the resource-based view, dynamic capabilities, and stakeholder theory, the paper proposes a holistic framework where BDAC functions as a strategic asset composed of seven core dimensions: technology, data quality, simple resources, technological skills, managerial skills, organizational learning, and a data-driven culture. The study establishes that BDAC does not directly translate into sustainability outcomes rather, it operates through a series of mediation and moderation pathways. The impact of BDAC on sustainability is mediated by the generation of data-driven insights and the quality of strategic decision-making. Organizations must successfully convert raw analytical potential into actionable strategic understanding to realize performance gains. The effectiveness of these pathways is significantly influenced by talent management and Artificial Intelligence (AI) orientation. Comprehensive talent strategies that prioritize digital literacy and human-AI collaboration serve to amplify the value of analytical investments. The framework addresses sustainability as a multidimensional construct encompassing economic strength, environmental stewardship, and social well-being. BDAC enhances these areas by optimizing resource use, fostering green innovation, and improving human resource accuracy. The research concludes that achieving long-term sustainability requires a shift from intuition-based to evidence-based organizational cultures. Success depends on a holistic implementation strategy where all seven BDAC dimensions are developed interdependently, alongside robust governance frameworks to address emerging ethical challenges such as algorithmic bias and data privacy. Future research is encouraged to pursue longitudinal designs and investigate these dynamics within the context of small and medium-sized enterprises (SMEs) and developing economies.

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.

This study investigates the impact of various big data analytics capabilities on firm performance, ‎through big data analytics capability (BDAC) (a combined set of capabilities) in Saudi Arabia’s ‎tourism sector. BDAC is modeled as a second-order construct formed by five capabilities: data-driven culture, technology, technical skills, managerial skills, and data-driven decision-making. The ‎study basically examines the mediating role of BDAC. Data were collected through a structured ‎questionnaire, resulting in 471 valid responses from professionals in tourism firms. Structural ‎Equation Modeling (PLS-SEM) was used to analyze the data. The results show that managerial ‎skills, technical skills, and a data-driven culture significantly improve performance both directly and ‎indirectly through BDAC. However, data and technology capabilities had no significant impact on ‎performance, either directly or through BDAC. These findings highlight the importance of human ‎and cultural elements over technical infrastructure alone. The study contributes to theory by ‎confirming that BDAC is a multidimensional construct and plays a partial mediating role. Practical ‎implications suggest that tourism firms should invest in developing managerial talent and fostering ‎a data-driven culture to maximize performance outcomes‎.

Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation

PurposeThis study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).Design/methodology/approachTo achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.FindingsThe empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.Research limitations/implicationsOne of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.Originality/valueThis study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.