... pricing assumptions by uncovering how these signals interact under different competitive conditions. Design/methodology/approach The study analyses 1,039 completed eBay UK auctions using an explainable machine-learning framework. A predictive model is estimated and interpreted using SHAPley Additive...

... experiment with 74,758 consumers on a prominent e-book platform in China, comparing the effects of offering the first 50 chapters for free against no free content. Additionally, a causal random forest machine learning algorithm was applied to analyze data and optimize strategies based on individual social...

... impeding blockchain adoption. The purpose of this study is to identify blockchain adoption barriers in sustainable supply chain and uncovers their interrelationships. Design/methodology/approach A three-phase framework that combines machine learning (ML) classifiers, BORUTA feature selection algorithm...

... connection. This research investigates a predictive model blending variables from these theoretical perspectives to determine crowdfunding success. Design/methodology/approach The research is based on data from 176,614 Kickstarter projects. A number of machine learning and artificial intelligence...

..., such as user problems and necessities. This model will detect the causes of defects and prioritize improving components in a product-service system based on FMEA results. Design/methodology/approach Ontology-based methods, text mining and sentiment analysis with machine learning methods are performed...

..., this study intends to address the impact of supply chain data and network activity data on CCR prediction, by integrating machine learning technology into the prediction to verify whether adding new data can improve the predictability. Findings The results show that the predictive errors of the datasets...

... methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete...

... data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data. Design/methodology/approach As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily...

... intelligence Machine learning Safety-critical system Neural network Bayesian Formal verification Adversarial examples The rapid development of artificial intelligence (AI) in the past decades has greatly realized its huge potential. Several definitions of AI in Schwendicke et al. (2020...

... transformation, artificial intelligence (AI) and machine learning (ML) revolutionize various industry domains (Kapletia et al., 2019 ; Roscoe et al., 2019 ; Wamba and Queiroz, 2020). Such disruptive technologies are garnering significant attention due to their many advantages, including...

...Lei Li; Desheng Wu Purpose The infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve...

... throughput Forecasting Machine learning LSTM For empirical analysis, the yearly CT data of the PLA are considered from the year 1995–2018. PLA is one of the busiest seaports worldwide as well as the largest container port by volume in the United States of America. It is ranked 18 in the world...

... and the level of fit within the industry between CIO requirements and appointments via the resource-based view. Design/methodology/approach Job postings and CIO curriculum vitae were collected and analysed through the lens of organisational capability theory using the machine learning method of Latent...

... near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better. Design/methodology/approach The paper proposes a novel machine learning predictive methodology...

... of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I...

..., constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80...

... 2019 06 05 2019 19 08 2019 28 08 2019 © Emerald Publishing Limited 2019 Emerald Publishing Limited Licensed re-use rights only Machine learning Business intelligence Risk analytics Credit risk scoring Decision support The recent financial crises resulted...

...Yoon-Sung Kim; Hae-Chang Rim; Do-Gil Lee Purpose The purpose of this paper is to propose a methodology to analyze a large amount of unstructured textual data into categories of business environmental analysis frameworks. Design/methodology/approach This paper uses machine learning to classify...

... significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences. Originality/value The authors use...

... on Facebook. Design/methodology/approach Facebook profile data consisting of 32 attributes were collected for 111,123 Korean users. Inferences were made for four private attributes (gender, age, marital status, and relationship status) based on five machine learning-based classification algorithms...