To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly ...technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.
•A big data analytics architecture for healthcare organizations is built.•We identify five big data analytics capabilities from 26 big data cases.•We present several strategies for being successful with big data analytics in healthcare settings.•We provide a comprehensive understanding of the potential benefits of big data analytics.
Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big ...data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.
•We define what is meant by big data.•We review analytics techniques for text, audio, video, and social media data.•We make the case for new statistical techniques for big data.•We highlight the ...expected future developments in big data analytics.
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
This study argues that.•BDA capability and BI&A are positively related to data driven insights, decision-making quality.•BDA capability and BI&A are positively related to circular economy ...performance.•Data driven insights are positively related to decision-making quality.•Decision making quality influences circular economy performance.•Data driven insights mediates the relationship of BI&A and decision-making quality.
Big data analytics (BDA) is a revolutionary approach for sound decision-making in organizations that can lead to remarkable changes in transforming and supporting the circular economy (CE). However, extant literature on BDA capability has paid limited attention to understanding the enabling role of data-driven insights for supporting decision-making and, consequently, enhancing CE performance. We argue that firms drive decision-making quality through data-driven insights, business intelligence and analytics (BI&A), and BDA capability. In this study, we empirically investigated the association of BDA capability with CE performance and examined the mediating role of data-driven insights in the relationship between BDA capability and decision-making. Data were collected from 109 Czech manufacturing firms, and partial least squares structural equation modeling was applied to analyze the data. The results reveal that BDA capability and BI&A are positively associated with decision-making quality. This effect is stronger when the manufacturer utilizes data-driven insights. The results demonstrate that BDA capability drives decision-making quality in organizations, and data-driven insights do not mediate this relationship. BI&A is associated with decision-making quality through data-driven insights. These findings offer important insights to managers, as they can act as a reference point for developing data-driven insights with the CE paradigm in organizations.
The emergence of big data brings a new wave of Customer Relationship Management (CRM)’s strategies in supporting personalization and customization of sales, services and customer services. CRM needs ...big data for better customers experiences especially personalization and customization of services. Big data is a popular term used to describe data that is volume, velocity, variety, veracity, and value of data both structured and unstructured. Big data requires new tools and techniques to capture, store and analyse it and is used to improve decision making for enhancing customer management. The aim of the research is to examine big data for CRM’s scenario. The method of collection of data for this study was literature review and thematic analysis from recent studies. The study reveals that CRM with big data has enabled business to become more aggressive in term of marketing strategy like push notification through smartphone to their potential target audiences.
Big data analytics has been widely regarded as a breakthrough technological development in academic and business communities. Despite the growing number of firms that are launching big data ...initiatives, there is still limited understanding on how firms translate the potential of such technologies into business value. The literature argues that to leverage big data analytics and realize performance gains, firms must develop strong big data analytics capabilities. Nevertheless, most studies operate under the assumption that there is limited heterogeneity in the way firms build their big data analytics capabilities and that related resources are of similar importance regardless of context. This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments. Our empirical investigation followed a mixed methods approach using survey data from 175 chief information officers and IT managers working in Greek firms, and three case studies to show that depending on the context, big data analytics resources differ in significance when considering performance gains. Applying a fuzzy-set qualitative comparative analysis (fsQCA) method on the quantitative data, we show that there are four different patterns of elements surrounding big data analytics that lead to high performance. Outcomes of the three case studies highlight the inter-relationships between these elements and outline challenges that organizations face when orchestrating big data analytics resources.
•Review of the ongoing research on BDA in manufacturing process.•Proposition of a framework grouping BDA capabilities in manufacturing process.•Identification of research trends in implementing BDA ...in manufacturing process.•Conducting multiple case studies of the application of BDA in real-life context of the manufacturing process in a leading manufacturers of phosphate derivatives.•Drawing a set of recommendations based on the findings of the multiple case study.
Today, we are undoubtedly in the era of data. Big Data Analytics (BDA) is no longer a perspective for all level of the organization. This is of special interest in the manufacturing process with their high capital intensity, time constraints and given the huge amount of data already captured. However, there is a paucity in past literature on BDA to develop better understanding of the capabilities and strategic implications to extract value from BDA. In that vein, the central aim of this paper is to develop a novel model that summarizes the main capabilities of BDA in the context of manufacturing process. This is carried out by relying on the findings of a review of the ongoing research along with a multiple case studies within a leading phosphate derivatives manufacturer to point out the capabilities of BDA in manufacturing processes and outline recommendations to advance research in the field. The findings will help companies to understand the big data analytics capabilities and its potential implications for their manufacturing processes and support them seeking to design more effective BDA-enabler infrastructure.
This study investigates the effectiveness of the DistilBERT model in classifying tweets related to disasters. This study achieved significant predictive accuracy through a comprehensive analysis of ...the dataset and iterative refinement of the model, including adjustments to hyperparameters. The benchmark model developed highlights the benefits of DistilBERT, with its reduced size and improved processing speed contributing to greater computational efficiency while maintaining over 95% of BERT's capabilities. The results indicate an impressive average training accuracy of 92.42% and a validation accuracy of 82.11%, demonstrating the practical advantages of DistilBERT in emergency management and disaster response. These findings underscore the potential of advanced transformer models to analyze social media data, contributing to better public safety and emergency preparedness.
The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. ...Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed.
Big data analytics capability (BDAC) is the key resource for competitive advantage in the drastically changing market. Although some studies have investigated the impacts on firm performance, there ...is limited understanding of how firms enhance their BDAC. This study draws on organisational culture and investigates the effects of responsive and proactive market orientations on BDAC and firm performance. The results show that both responsive and proactive market orientations increase BDAC. Further, BDAC fully mediates the relationship between these two market orientations and firm performance. Our findings suggest that BDAC researchers should focus on market orientations that enhance BDAC.