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Challenges of big data analytics for sustainable supply chains in healthcare – a resource-based view
PurposeRegardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.Design/methodology/approachDrawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).FindingsUsing thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.Research limitations/implicationsThe identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.Practical implicationsThis research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.Originality/valueOriginality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.
Journal Article
The nexus of big data analytics, knowledge sharing, and product innovation in manufacturing
In today‘s highly competitive business environments, manufacturers face stiff competition. As digital technologies have become more pervasive, many businesses in the manufacturing sector have begun to tap into the potential of big data analytics to gain an edge in their markets. Companies in the manufacturing sector can gain a significant competitive advantage by strategically utilizing big data analytics to uncover profound insights that have the potential to significantly enhance their capabilities in product innovation.
This research delves into communication’s role as a go-between for big data analytics and product innovations’ success at manufacturing firms. The validity and reliability of the measurement scales were first thoroughly examined in this study. The research model was then tested using structural equation modeling and process macro analysis.
The analytical findings unveil those big data analytics exert a pronounced, positive, and statistically significant impact on product innovation performance and information-sharing dynamics. Furthermore, it is discerned that information-sharing exerts a substantial and affirmative influence on the capacity for product innovation. Additionally, it is established that the impact of big data analytics on product innovation performance undergoes moderation by the information-sharing mechanism.
Journal Article
An Introduction to Machine Learning
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of \"boosting,\" how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Book
The role of big data analytics capability in the telecommunication sector of Pakistan: the chain mediating effect of data integration capability and data-driven decision making
The current study aimed to explain the effect of big data analytics capabilities on firm performance in the telecommunication sector of Pakistan. The proposed research model examines the effect of big data analytics capabilities on firm performance in the presence of chain mediating effect of data integration capability and data-driven decision-making, along with moderating influence of analytics culture. The research model was developed using the proven theory of resource-based view. In this cross-sectional study, an online questionnaire was used including 34 response items for data collection, whereas SPSS and Smart-PLS 4.0 were used for descriptive statistics & inferential analysis respectively. The results of this study indicate that adoption of big data analytics capabilities positively influence firm performance. It also confirms about the effective implementation of data driven decision making & data integration capability leads to better performance of the organization. However, no moderation of analytics culture on firm performance was found. The results also suggest the managers to take effective decision-making based on data integration for enhancing business performance. Study faces some potential challenges due to high data volume availability, small sample size methodological and sector specifics limitations. Study suggests the potential for future research evaluating this model with two serial mediations like process-oriented dynamic capabilities, business process agility etc. along with some moderators such as customer knowledge management etc. to identify the response in more complicated connections.
Journal Article
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review
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.
Journal Article
Python 3 and Data Visualization Using ChatGPT /GPT-4
This book is designed to show readers the concepts of Python 3 programming and the art of data visualization.It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories.
eBook