Big Data Analytics Capabilities Research Articles

Responsible innovation and sustainable competitive advantage: is big data the missing link?

PurposeThis study aims to examine the influence of organizational flexibility (OF) and shared vision (SV) on sustainable competitive advantage (SCA) with the mediation role of responsible innovation (RI) in the manufacturing industry of a developing country. Furthermore, big data analytics capability (BDAC) serves as a moderator between RI and SCA.Design/methodology/approachThe study's hypotheses are investigated using the structural equation modeling (SEM) method. Through simple random sampling, information was gathered from 247 owners/managers of manufacturing SMEs.FindingsThe results elucidate that OF and SV significantly determine RI and SCA. Moreover, RI significantly mediates between SV, OF and SCA. Besides, RI significantly determines SCA. BDAC significantly leads to SCA. Finally, BDAC significantly moderates between RI and SCA.Research limitations/implicationsRI is crucial for manufacturing small and medium-sized enterprises (SMEs) to gain SCA and BDAC is important to address the changing demands of consumers for environment-friendly products. This study gives the public an overview of the different degrees to which SMEs are embracing RI and BDAC; with more environment-friendly initiatives, the natural environment will become more sustainable. Environmental sustainability will benefit each individual living in society.Originality/valueThis study adds value to the existing literature by focusing on predictors that affect SCA. Using dynamic capability theory, this initial study examines the influence of SV and OF on SCA and RI as mediators. Furthermore, BDAC is used as a moderating variable between RI and SCA. Managers, students and researchers can benefit from this study.

How to improve firm performance using big data analytics capability and business strategy alignment

The current fascination with big data has some organizations make big data analytics activities to improve firm performance. Thus, regarding big data analytics capability, it might work for some companies but not for others. It seems that almost no one has made a significant difference through big data. To address this challenge, this study suggests a big data analytics capability model based on the resource-based theory and the entanglement view of socio-materialism. The results show the big data analytics capability hierarchy, which has three basic components (i.e., management, technology, and employee capability) and eleven subcomponents (i.e., planning, investment, coordinating, controlling, connectivity, compatibility, modularity, technology management skills, technical skills, business skills, and relational skills). Through Delphi studies together with online surveys of business analysts at the organization, the entanglement perspective of the high-order big data analytics capability model and its impact on firm performance was affirmed. This article investigates how organizations capitalize on big data analytics to create a sustainable competitive edge and progress business efficiency through big data analytics. On the other hand, the study delineates different resources and sub-capabilities which it takes to develop the big data analytics capability.

Big data analytics capabilities, direct and mediating relationships with innovative and business performance

This study examines the effect of key big data analytics capabilities (data, skills, technology, and culture) on innovation performance and business performance. Data were gathered from 91 companies and analyzed to determine correlations in the proposed model. The results find that big data analytics capabilities (BDAC) partially mediate the effect between innovative performance and business performance. Further, as a company’s performance is a multidimensional element, was necessary to analyze more than one attribute to evaluate the relationship with BDAC through a canonical correlation analysis. The results in this sense reveal that the four big data capabilities increase the growth of sales, revenue, the number of workers, the net profit margin, innovation management, the development of new products and services, and the adoption of new information technologies.

The impact of big data analytics on innovation capability and sustainability performance of hotels: evidence from an emerging economy

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.

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

Circular supply chain management: Antecedent effect of social capital and big data analysis capability and their impact on sustainable performance

AbstractThe circular economy era has begun, and circular supply chain management (CSCM) is widely acknowledged as a management paradigm that promotes firm sustainability. Through previous research, we have learned that the key challenge in the circular transformation of manufacturing firms is to encourage supply chain partners to collaborate and effectively participate in resource cycles. Drawing inspiration from social capital theory and resource‐based views, we explored how social capital effect sustainable performance through CSCM and the moderating effect of big data analytics capability (BDAC). We tested this by collecting data from 414 Chinese manufacturing firms. The results indicate that both internal and external social capital are positively correlated with CSCM. In addition, CSCM mediates the relationship between social capital and organizational environmental and social sustainability. The results also indicate that BDAC strengthen the impact of external social capital on CSCM, but the moderating effect on the relationship between internal social capital and CSCM is not significant. This essay advances the theory and practice of CSCM by offering fresh perspectives on how social capital influences sustainable performance of firms.

Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach

The maritime industry is facing increasing challenges due to decarbonization requirements, trade disruptions, and geoeconomic fragmentation, such as International Maritime Organization (IMO) sets out clear framework to reach net zero emissions by 2050, Russia-Ukraine war disrupted maritime activities in the Black and Azov seas, and increased trade tensions between the United States and China. To enhance their sustainability, operational efficiency, and competitiveness, maritime organizations are therefore very keen to build big data analytics capability (BDAC). However, various barriers, mean that only a handful are able to do so. We adopt a mixed-method approach to analyze these barriers. Thematic analysis is used to identify five categories of barriers and 16 individual barriers based on empirical data collected from 26 maritime organizations. These are then prioritized using the analytic hierarchy process (AHP), followed by total interpretive structural modelling (TISM) to understand their interrelationships. Finally, cross-impact matrix multiplications applied to classification (MICMAC) is employed to differentiate the role of each barrier based on its driving and dependence power. This paper makes several theoretical contributions. First, China's hierarchical cultural value orientation encourages competition and obedience to rules, resulting in unwillingness to share knowledge, lack of coordination, and lack of error correction mechanisms. These cultural barriers hinder BDAC development. Second, organizational learning category barriers are found to be the most important in impeding BDAC development. This study also raises practitioners' awareness of the need to tackle cultural and organizational learning barriers.

Big data analytics capability, marketing agility, and firm performance: a conceptual framework

ABSTRACT This study proposes that the proficient use of big data enables organizations to respond swiftly to market opportunities and threats, leading to positive business outcomes. Drawing on research from marketing, information technology, and management, the study builds a conceptual framework that outlines the relationships between big data analytics capability, marketing agility, and firm performance and pinpoints the contingencies that shape these relationships. While highlighting the importance of agility in harnessing big data for a firm’s success, the study offers avenues for further research and valuable insights for managers looking to invest in big data.

Managing big data and blockchain for enterprise internationalization process: Mediating role of dynamic accounting system capability

Abstract This study develops and confirms a model that examines the relationship between Big Data Analytics Capabilities (BDAC), Blockchain Technology (BT), and Internationalization Process (IP). It also requires various efforts to explore how Dynamic Accounting Information System Capability (DAISC) mediates the interconnections between BDAC, BT, and IP. This study focuses on small and medium enterprises (SMEs) in Vietnam, which are widely recognized as crucial to the Vietnamese economy. The current research utilized the hypothetico-deductive method. The proposed model's hypothesized linkages were based on a comprehensive evaluation of relevant literature and investigations. The statistics database was created using a paper-and-pencil survey distributed to 612 accountants in SMEs in southern Vietnam utilizing the convenient and snowball sampling technique. A covariance-based structural equation modelling approach was used to assess the measurement and structural models in this research due to its ability to offer a strong foundation for analyzing intricate linkages within a research framework. The outcome confirmed the significant and positive relationships between BDAC, BT, and IP. Concurrently, these relationships were partially influenced by DAISC. The practitioners would greatly benefit from detailed insights on the findings, as they would provide guidance on how to identify and take advantage of the growth opportunities afforded by digital technology. Moreover, the observations could provide policymakers with new insights on necessary policy measures to support the application of digital technologies and encourage the implementation of a dynamic accounting information system, which could result in significant benefits in the future.

The Role of Artificial Intelligence in Supply Chain Agility: A Perspective of Humanitarian Supply Chain

The present study aims to establish a link between digital and humanitarian supply chain management. This study has focused on using artificial intelligence-big data analytical capabilities and information alignment to develop and maintain supply chain collaboration to achieve supply chain agility in a dynamic environment like a disaster. The targeted population is humanitarian organizations in Pakistan. Simple random sampling method, data was collected from 242 respondents using an online questionnaire. The Partial Least Square – Structural Equation Modelling technique has been used for analysis. Resource Based Theory and Contingency Theory in this study have provided foundations to develop and test the relationships among information alignment, supply chain agility, artificial intelligence – big data analytical capabilities, and supply chain collaboration in disaster management. Findings showed the use of artificial intelligence – big data analytical capabilities are beneficial for information alignment and supply chain agility.

Unraveling the transformation: the three-wave time-lagged study on big data analytics, green innovation and their impact on economic and environmental performance in manufacturing SMEs

PurposeThe 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.Design/methodology/approachA time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.FindingsThe 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.Originality/valueThis 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.

Do reliable big and cloud data analytics capabilities in manufacturing firms' supply chain boosting unique comparative advantage? A moderated-mediation model of data-driven competitive sustainability, green product innovation and green process innovation at North Africa region

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.

The Moderating Effect of Big Data Analytics Capabilities on the Relationship Between Organizational Culture and Administrative Effectiveness at Higher Education Institution in Sichuan

In the rapidly evolving landscape of higher education, the role of organizational culture and administrative effectiveness has garnered significant attention. This study aims to investigate the moderating effect of big data analytics capabilities on the relationship between organizational culture and administrative effectiveness in higher education institutions in Sichuan, China. The study will employ a mixed-methods approach, combining quantitative analysis through surveys and qualitative insights through interviews. The sample will consist of faculty members, administrative staff, and management personnel from various higher education institutions across Sichuan province. The theoretical framework will draw upon literature on organizational culture, administrative effectiveness, and big data analytics capabilities. Organizational culture dimensions such as innovation, adaptability, collaboration, and leadership will be evaluated using established scales. Administrative effectiveness will be assessed based on factors such as efficiency, responsiveness, transparency, and stakeholder satisfaction. The research will explore how the organizational culture of higher education institutions in Sichuan influences administrative effectiveness. Additionally, it will investigate the role of big data analytics capabilities in moderating this relationship. Specifically, it will examine how institutions with varying levels of big data analytics capabilities leverage data-driven insights to enhance administrative practices and overall effectiveness. Findings from this study are expected to provide valuable insights for higher education institutions in Sichuan, enabling them to cultivate a conducive organizational culture and leverage big data analytics capabilities to improve administrative effectiveness. The results will also contribute to the broader literature on organizational culture, administrative effectiveness, and the role of technology in shaping organizational outcomes.

Navigating a net‐zero economy future: Antecedents and consequences of net‐zero economy‐based green innovation

AbstractThe pursuit of net‐zero economy goals and government emphasis on sustainable performance has compelled numerous firms to focus on sustainable production through the adoption of net‐zero economy‐based green innovation. The digital transformation of manufacturing systems and supply chains, supported by the utilization of big data and the adoption of net‐zero economy‐oriented lean practices in the presence of net‐zero economy‐based green practices, can converge to enhance net‐zero economy‐based green innovation in manufacturing firms. This convergence leads to an improvement in green competitive advantage and net zero‐based green performance. To examine the aforementioned linkages using a framework based on the dynamic capabilities view paradigm, a time‐lagged design was employed to collect data from 594 manufacturing firms. The empirical findings indicate that big data analytics capabilities and net‐zero economy‐oriented lean practices of firms significantly influence net‐zero economy‐based green innovation. Green organizational identity strengthens the effect of net zero‐based green innovation on green competitive advantage and net zero‐based green performance.

Big data analytics capability in building supply chain resilience: the moderating effect of innovation-focused complementary assets

PurposeThis 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.Design/methodology/approachExtensive 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.FindingsThe 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.Originality/valueThis 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.

Six Sigma, Big Data Analytics and performance: an empirical study of Brazilian manufacturing companies

The introduction of several digital technologies in manufacturing organizations has generated Big Data sets that can be explored using Big Data Analytics (BDA) to bring competitive advantages to organizations. Data exploration can be strengthened when analyzed within Six Sigma (SS) business improvement methodology domains, which may impact organizational performance. Using data from 171 SS experts from Brazilian manufacturing companies, this study aims to test the relationships among BDA capability, SS practices, Quality Performance (QP), and Business Performance (BP). Thus, this study empirically investigates these relationships in a developing country, since Big Data sets and the capability to use them can be highlighted by structured SS analysis structure and procedures, leading to better decision-making. Findings show that BDA is beneficial for SS practices, and both BDA and SS practices can positively impact perceived QP and BP in Brazilian manufacturing companies. Additionally, this study shows that not only BDA and SS reinforce each other, but when used together, they increase the positive impact on performance. These results can drive BDA investments by managers, and integrate efforts between SS and BDA.

The Impact of Big Data Analytics on Decision-Making Within the Government Sector.

The government sector has started adopting big data analytics capability (BDAC) to enhance its service delivery. This study examines the relationship between BDAC and decision-making capability (DMC) in the government sector. It investigates the mediation role of the cognitive style of decision makers and organizational culture in the relationship between BDAC and DMC utilizing the resource-based view of the firm theory. It further investigates the impact of BDAC on organizational performance (OP). This study attempts to extend existing research through significant findings and recommendations to enhance decision-making processes for a successful utilization of BDAC in the government sector. A survey method was adopted to collect data from government organizations in the United Arab Emirates, and partial least-squares structural equation modeling was deployed to analyze the collected data. The results empirically validate the proposed theoretical framework and confirm that BDAC positively impacts DMC via cognitive style and organizational culture, and in turn further positively impacting OP overall.

Big Data Analytical for Sustainable Information Quality in an Emerging Market

Sustainability-oriented decision-making requires adequate information and dedicated information technology support. However, integrating economic, social and environmental information requires a new understanding of how data is collected and shared among stakeholders. Ensuring the quality of sustainable information will depend on capabilities to collect and analyze large-scale data. While the potential benefits of digital technologies and sustainability have been widely publicized in the cutting-edge literature, little is known about the analytical capabilities of big data and sustainable information. This study aims to examine the prominence of big data analytical capabilities for sustainable information quality in an emerging market, in this case, Brazil. A scalar-type questionnaire was used to collect primary data from Brazilian multinational companies. The results indicate that big data analytical capabilities are substantive for sustainable information quality. This study is original, fills a gap in the literature and makes significant contributions: (a) sheds light on the prominence of big data analytical capabilities for sustainable information quality (b) signals the relevance of intangible capabilities; (c) serves as a guide for managers in deciding on priority capabilities to improve the quality of sustainable information directed to stakeholders; (d) ex-tends existing studies on analytical capabilities of big data and sustainable information quality; (e) shows that big data analytical capabilities bring benefits to support sustainable information quality in emerging markets; and finally (f) this research contributes to the literature on sustainable information and digital trans-formation with emerging technologies.

Supply chain risks in the age of big data and artificial intelligence: The role of risk alert tools and managerial apprehensions

As supply networks become more complex and international, the task of controlling associated risks becomes more difficult. This article investigates the usefulness of risk alert technologies in supply chain management, with a focus on Big Data Analytics (BDA) and Artificial Intelligence (AI). The study investigates the impact of BDA capabilities, solid IT infrastructure, managerial views, and AI-apprehensions on the effectiveness of risk alert tools using a questionnaire-based survey of 420 managerial personnel and Structural Equation Modeling (SEM) via SMART PLS. The work proposes the concept of AI-Apprehensions as a moderating variable, which is a relatively unexplored field. According to the findings, while BDA capabilities and IT infrastructure considerably improve the effectiveness of risk alert tools, AI-apprehensions can negate these advantages. The study provides useful insights for policymakers and practitioners, emphasizing the importance of balancing technical and human components for effective risk management.

Roles of Innovation Leadership on Using Big Data Analytics to Establish Resilient Healthcare Supply Chains to Combat the COVID-19 Pandemic: A Multimethodological Study

This article empirically examines the effect of big data analytics (BDA) on healthcare supply chain (HSC) innovation, supply chain responsiveness, and supply chain resilience under the moderating effect of innovation leadership in the context of the COVID-19 pandemic. The scanning interpretation–action–performance model and organization information processing theory are used to explain BDA, HSC innovation, responsiveness, and resilience relationships. First, the hypotheses were tested using data collected from 190 experienced respondents working in the healthcare industry. Our structural equation modeling analysis using the partial least squares (PLS) method revealed that BDA capabilities play a pivotal role in building a responsive HSC and improving innovation, which has contributed to resilience during the current pandemic situation. High innovation leadership strengthens the effect of BDA capabilities on HSC innovation. High innovation leadership also increases the effect of BDA capabilities on responsiveness. Second, we validated and supplemented the empirical research findings using inputs collected in 30 semistructured qualitative questionnaires. Our article makes a unique contribution from the perspective of innovation leaderships. In particular, we argue that the role of innovative leadership in the COVID-19 pandemic situation is critical as it indirectly affects HSC resilience when BDA is in place.