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This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI).

Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model.

This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP.

This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.

A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.

The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.

This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.

This research investigates the mechanism by which big data capability enables superior supply chain resilience (SCRe) by empirically examining the links among big data analytics (BDA), supply chain flexibility (SCF) and SCRe, with innovation-focused complementary assets (CA-I) as the moderator.

Extensive surveys were conducted to gather 308 responses from Malaysian manufacturing firms in order to explore this framework. The structural and measurement models were examined and evaluated by using partial least squares structural equation modelling.

The findings revealed that BDA is linked to flexibilities in a manufacturing firm’s value chain, which in turn is related to the firm’s SCRe. However, the association between BDA and SCRe is surprisingly non-significant. Additionally, CA-I was discovered to moderate the connections between all of the constructs, except for the relationship between BDA and SCRe. Such findings imply that with the aim of enhancing resilience, a company should concentrate on SCF; and that BDA capability is a prerequisite for increasing these flexibilities.

This research extrapolates the findings of previous studies regarding BDA’s influence on SCRe by investigating the indirect effect of SCF, as well as the moderating influence of CA-I. This research is one of the first few studies to empirically examine the relationships between BDA, SCF and SCRe across manufacturing firms, with CA-I acting as a moderator.

This study is the first to examine how big data analytics (BDA) capabilities affect green absorptive capacity (GAC) and green entrepreneurship orientation (GEO). It uses the dynamic capability view, BDA and knowledge-sharing literature. There is a lack of studies addressing the BDA–GAC and BDA–GEO relationships and their potential impact on green innovation. Continuing the ongoing research discussion, a few studies examined the vital implications of knowledge sharing (KS) on GAC, GEO and green innovation.

The study used a cross-sectional and stratified random sampling technique to collect data through self-administered surveys among Chinese manufacturing firm employees. The study applied SmartPLS to analyze the obtained data.

The findings revealed that BDA capabilities positively influence GAC and GEO. In addition, GEO and KS positively impact green innovation. The KS recorded a positive impact on GAC and GEO. Furthermore, GAC and GEO recorded a partial mediating effect.

The study acknowledges that GAC is the backbone of a firm green entrepreneurial orientation, which needs to be aligned with BDA capabilities to anticipate future green business trends. GAC's help drives GEO's green business agenda. KS plays a strategic role in developing GAC, fostering GEO and improving green innovation.

The study highlights the necessity of aligning BDA capabilities to fit firms' GEO green business agendas. This study focuses on the role of BDA capabilities in developing firms' green dynamics capabilities (e.g. GAC), which helps GEO drive superior green business growth. KS develops GAC and boosts GEO to enhance green innovation.

This study views Net-Zero as a dynamic capability for decarbonising supply chains (SCs). This study aims to investigate the relationship between three information processing-related capabilities (supply chain visibility [SCV], supply chain integration [SCI] and big data analytics [BDA]) as its antecedents and SC performance as its competitive advantage outcome.

The authors conceptualise a research model grounded in the literature based on dynamic capabilities and information processing views. The study uses a structural equation modelling technique to test the hypotheses’ relationship using the survey data from 311 industrial enterprises.

The results show that SCI and BDA positively and directly influence the Net-Zero capability (NZC). No significant direct impact is found between SCV and NZC. BDA fully mediates SCV and partially mediates SCI in their relationship with NZC. The results also confirm that NZC positively impacts SC performance (SCP).

This study contributes to operations management and SC literature by extending the knowledge about Net-Zero SCs through an empirical investigation. In particular, the study suggests BDA is essential to enhance NZC as SCV alone does not significantly contribute. The study also documents the benefit of NZC on SCP, which can encourage more volunteer actions in the industry.

This paper aims to propose a research model with eight constructs, i.e. BDA leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation, financial performance, market performance and customer satisfaction.

The research model focuses on whether (1) Big Data Analytics (BDA) leadership influences BDA talent quality, (2) BDA talent quality influences BDA security quality, (3) BDA talent quality influences BDA privacy quality, (4) BDA talent quality influences Innovation and (5) innovation influences a firm's performance (financial, market and customer satisfaction). An instrument was designed and administered electronically to a diverse set of employees (N = 188) in various organizations in the USA. Collected data were analyzed through a partial least square structural equation modeling.

Results showed that leadership significantly and positively affects BDA talent quality, which, in turn, significantly and positively impacts security quality, privacy quality and innovation. Moreover, innovation significantly and positively impacts firm performance. The theoretical and practical implications of the findings are discussed. Recommendations for future research are provided.

The study provides empirical evidence that leadership significantly and positively impacts BDA talent quality. BDA talent quality, in turn, positively impacts security quality, privacy quality and innovation. This is important, as these are all critical factors for organizations that collect and use big data. Finally, the study demonstrates that innovation significantly and positively impacts financial performance, market performance and customer satisfaction. The originality of the research results makes them a valuable addition to the literature on big data analytics. They provide new insights into the factors that drive organizational success in this rapidly evolving field.

Big data analytics (BDA) plays a crucial role in understanding customer behavior through Customer Relationship Management (CRM), especially in a rapidly changing business environment. This paper investigates the direct effect of BDA use on market performance, besides the mediating effect through Big Data-enabled CRM strategies adoption (e.g. customization and personalization). The paper also examines the moderating role of competitive intensity in these effects.

Drawing from a knowledge-based view (KBV) and Organizational Information Processing Theory (OIPT), the authors formulated the research model. Subsequently, the measurement model and hypotheses were tested through PLS-SEM on online survey data of 229 managers from 167 companies out of Egypt's top 500.

The results indicated that BDA use does not directly affect the market performance, but this effect was significant through customization and personalization strategies adoption. The results also revealed a positive association between BDA use and the adoption of these strategies. Furthermore, competitive intensity only moderates the relationship between BDA use and personalization strategy adoption.

Companies can use BDA to improve customer knowledge and experience through customization and personalization, leading to better market performance and moving towards becoming a Big Data-driven organization. This study is limited to companies in the Egyptian context, which restricts the generalizability of the results.

This study conceptually and empirically explores how BDA usage, customization and personalization strategies impact market performance under competitive intensity situations, especially in the context of emerging markets.

This paper investigated the organizational adoption of big data analytics (BDA) in the context of supply chain planning (SCP) to conceptualize how resources are orchestrated for organizational BDA adoption and to elucidate how resources and capabilities intervene with the resource management process during BDA adoption.

This research elaborated on the resource orchestration theory and technology innovation adoption literature to shed light on BDA adoption with multiple case studies.

A framework for the resource orchestration process in BDA adoption is presented. The authors associated the development and deployment of relevant individual, technological and organizational resources and capabilities with the phases of organizational BDA adoption and implementation. The authors highlighted that organizational BDA adoption can be initiated before consolidating the full resource portfolio. Resource acquisition, capability development and internalization of competences can take place alongside BDA adoption through structured processes and governance mechanisms.

A relevant discussion identifying the capability gap and provides insight into potential paths of organizational BDA adoption is presented.

The authors call for attention from policymakers and academics to reflect on the changes in the expected capabilities of supply chain planners to facilitate industry-wide BDA transition.

This study opens the black box of organizational BDA adoption by emphasizing and scrutinizing the role of resource management actions.

Drawing from dynamic capability (DC) theory, this study aims to investigate how big data analytics (BDA)-enabled dynamic capabilities (DCs) prompt firm performance. This study proposes that BDA-enabled DCs lead firms toward simultaneous exploration and exploitation of new knowledge about markets and products (i.e. marketing ambidexterity) which in turn improves firms' market and financial performance. This study also examines if environmental dynamism strengthens the aforementioned relationship.

This study uses survey questionnaire and data were collected in the form of two heterogeneous samples from Turkey and Pakistan. Partial least square-structural equation modeling (PLS-SEM) was used to test the hypotheses.

Results reveal that BDA-enabled DCs positively affect both dimensions of marketing ambidexterity (exploration and exploitation). Marketing exploration and exploitation have positive effects on firms' market and financial performance. Results also demonstrate that environmental dynamism moderates the link between BDA-enabled DCs and firms' marketing exploitation. The moderating effect for BDA-enabled DCs and firms' marketing exploration was not consistent across both samples.

This study contributes to the literature of BDA and marketing ambidexterity in the light of DC theory in a way that when and how the marketing ambidexterity, derived from BDA-enabled DCs, has a positive impact on firm performance. Moreover, findings imply that the development and enhancement of BDA-enabled DCs facilitate firms to calibrate marketing exploitation and exploration to seek new knowledge about markets and products and using such knowledge to achieve superior performance.

The novelty of present study is development of dynamic capabilities-based framework which sheds light on the role of big data for sensing, seizing and (re)configuring firms' resources to develop marketing ambidextrous capabilities in order to stay successful. From methodological perspective, this study uses two heterogeneous samples to assess robustness of results for ensuring greater generalizability and theoretical resonance.

This study aims to examine the impact of cultural values [power distance (PD), uncertainty avoidance (UC), individualism vs collectivism (IC) and time orientation] and users’ perceptions on the adoption of big data analytics (BDA) within the context of the United Arab Emirates (UAE) health-care sector. It uses the unified theory of acceptance and use of technology (UTAUT) model as its theoretical foundation.

A cross-sectional survey involving 256 health-care organization users in major hospitals across the UAE was conducted. Smart partial least squares (PLS) structural equation modeling was used to assess users’ behavioral intentions (BI) to use BDA in the health-care context.

Results indicate that performance expectancy, social influence, facilitating conditions and perceived trust significantly predicted respondents’ BI to use BDA. However, effort expectancy, perceived security and time orientation were found to have insignificant impacts on BI. Interestingly, the remaining cultural values (PD, UC and IC) did not significantly affect the relationship between social influence and BI in the context of BDA adoption in health care.

This study contributes to the literature by examining the role of cultural dimensions in BDA adoption within health-care organizations, particularly in the underrepresented UAE health-care context. Moreover, it extends the application of the UTAUT model to the BDA adoption in health care, providing insights into the factors affecting users’ BIs to use the technology.