... Publishing Limited 2025 Emerald Publishing Limited Licensed re-use rights only Fraud risk factors Auditing Throughput model Machine learning Artificial intelligence Cognitive-behavioral framework The integration of artificial intelligence (AI) and machine learning (ML) into auditing...

... and process diagrams, are now viewed as cognitive tools that support decision-making, facilitate knowledge externalization, and promote collaboration (Aier et al., 2011). The emergence of artificial intelligence (AI) is accelerating this convergence. AI technologies, including machine learning...

.... Design/methodology/approach The methodology involves analyzing the proximity of rental stations to significant locations, historical demand, environmental factors and regional dynamics to inform the development of AI models. Various machine learning (ML) models were evaluated to identify an optimized...

... AHP Artificial intelligence Big data analytics DEMATEL FMCG Machine learning Organization information processing theory (OIPT) Supply chain resilience Every organization’s supply chain in the contemporary global business climate is wide open to various risks that could affect...

...Oscar F. Bustinza; Luis M. Molina Fernandez; Marlene Mendoza Macías Purpose Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product...

... consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose. Findings SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving...

... from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). Design/methodology/approach In the present study, the authors use...

... After comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant...

... modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers...

... as well as to provide a colourimetric classification, rather than using any augmented reality as shown in Figure 4 . Although machine learning has been investigated for colourimetric tests in the past (Feng et al., 2014 ; Wang et al., 2016 ; Kim et al., 2017 ; Mutlu et...

... a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance. Findings This study shows that the success of crowdfunding...

... decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning. Design/methodology/approach This framework focuses on the automatic annotation and metadata completion through new deep...

...Murat Özemre; Ozgur Kabadurmus Purpose The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this study, two different machine learning algorithms, Random Forest (RF) and Artificial...