Article
Business
Morten Brinch, Angappa Gunasekaran, Samuel Fosso Wamba
Summary: The study identified firm-level capabilities required to create value from big data, confirming the application of adjacent theories and uncovering unexplored capabilities in the realm of big data. This provides a holistic overview of the capabilities needed for big data value creation.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Gerda Zigiene, Egidijus Rybakovas, Rimgaile Vaitkiene, Vaidas Gaidelys
Summary: As supply chains become more complex globally, businesses are looking for efficient tools for managing supply-chain risk, such as business analytics, business intelligence, and artificial intelligence. The current methods for supply-chain risk management mostly rely on experts' judgments and past data, but artificial intelligence can increase objectivity and reduce human mistakes. However, the transition from business analytics to artificial intelligence in supply-chain risk management is not straightforward and requires further research to explore its conceptual grounds and implementation terms.
Article
Chemistry, Multidisciplinary
Mirjana Pejic Bach, Amir Klincar, Ana Aleksic, Sanda Rasic Jelavic, Jusuf Zeqiri
Summary: This paper explores the relationship between supply chain management maturity (SCMM) and business performance using the balanced scorecard (BSC) framework. The study investigates this relationship from different perspectives and examines the moderating effects of industry characteristics. The survey results confirm a positive relationship between SCMM and business performance, particularly in industries with higher technological dynamism.
APPLIED SCIENCES-BASEL
(2023)
Article
Business
Yanhua Sun, Yu Gong, Yufang Zhang, Fu Jia, Yangyan Shi
Summary: The research indicates that end-user innovation-driven is a prerequisite for tea supply chain business model innovation, and SCBMI will continuously facilitate changes in the entire supply chain network structure to meet consumer demands.
CORPORATE SOCIAL RESPONSIBILITY AND ENVIRONMENTAL MANAGEMENT
(2021)
Article
Management
Trevor Cadden, Ronan McIvor, Guangming Cao, Raymond Treacy, Ying Yang, Manjul Gupta, George Onofrei
Summary: This study empirically develops and tests a supply chain analytical capabilities model, finding that supply chain organizational learning and supply chain data-driven culture are moderating factors that significantly impact the relationship between big data characteristics and supply chain agility.
INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT
(2022)
Article
Information Science & Library Science
Monica Fait, Rosa Palladino, Francesco Saverio Mennini, Domenico Graziano, Martina Manzo
Summary: This paper aims to show the role of knowledge brokers in sustainable development. An empirical analysis of 200 companies in the agro-food sector participating in a knowledge brokerage system was conducted. The study found that understanding and guiding absorptive, adaptive, and innovative capabilities have a positive impact on sustainable supply chain management.
JOURNAL OF KNOWLEDGE MANAGEMENT
(2023)
Article
Computer Science, Software Engineering
Mert O. Gokalp, Kerem Kayabay, Ebru Gokalp, Altan Kocyigit, P. Erhan Eren
Summary: The ability to leverage data science can generate valuable insights and actions in organisations, but successfully becoming a data-driven organisation requires a change in management, alignment, and culture. The proposed Data Drivenness Process Capability Determination Model aims to help organisations evaluate their capabilities, identify gaps, and create a roadmap for continuous improvement in a structured and standardised way.
Article
Business
Aleksi Harju, Jukka Hallikas, Mika Immonen, Katrina Lintukangas
Summary: This study investigates the role of procurement digitalization in reducing uncertainty in the supply chain and its relationship with mitigating supply chain risks and improving supply chain resilience. Through survey data collected from 147 Finnish firms, this study conceptualizes data analytics, information sharing, and procurement process digitalization as drivers of procurement digitalization and examines their impact on supply chain risk management and supply chain resilience using partial least squares path modeling.
SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Humza Naseer, Sean B. Maynard, Kevin C. Desouza
Summary: The study investigates how organizations utilize business analytics to develop, process, and exploit analytical information in cybersecurity incident response, highlighting the importance of using analytical information processing capability to enhance enterprise security performance. The findings contribute valuable insights to both BA and cybersecurity literature, shedding light on the facilitation of analytics-driven decision making in CSIR.
DECISION SUPPORT SYSTEMS
(2021)
Article
Engineering, Industrial
Kate Mc Loughlin, K. Lewis, D. Lascelles, S. Nudurupati
Summary: This paper presents empirical data from a case study on a sustainable cocoa supply chain network, identifying 8 critical business processes in sustainable supply chain management, and demonstrating the role of a phased approach in implementing these processes.
PRODUCTION PLANNING & CONTROL
(2023)
Article
Engineering, Manufacturing
Morgan Swink, Kejia Hu, Xiande Zhao
Summary: Technology, market, and competitive dynamics are pushing restaurant/food service supply chain firms to improve their analytics capabilities. However, this industry has been lagging behind compared to other industries. Our research integrates best practices of analytics technologies and provides insights for decision-makers in restaurant supply chains. Additionally, we identify the limitations of current processes, opportunities for development, and challenges to implementation.
PRODUCTION AND OPERATIONS MANAGEMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Mohammad Daneshvar Kakhki, Alan Rea, Mehdi Deiranlou
Summary: This study examines the mediating role of data analytics management capability (DAMC) in the relationship between supply chain integration (SCI) and supply chain agility, adaptability, and alignment (Triple-A). It also explores the impact of Triple-A supply chains on performance improvement. The results show that DAMC positively mediates the impact of SCI on performance.
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2022)
Article
Business
Surajit Bag, Muhammad Sabbir Rahman
Summary: In a changing business environment, firms face challenges in achieving sustainable development goals. However, firms can make progress by adopting regenerative approaches based on circular economy principles, enabling them to pursue sustainable development objectives effectively. This study investigates the relationship between the political and SC analytics skills of top SC executives and the environmental orientation of SCs, as well as the impact of a firm's environmental orientation on SC viability. Additionally, the study examines the moderating role of a firm's AI-driven big data analytics culture in these relationships.
BUSINESS STRATEGY AND THE ENVIRONMENT
(2023)
Article
Business
Smail Benzidia, Naouel Makaoui, Omar Bentahar
Summary: This study investigates the impact of BDA-AI technologies on green supply chain processes through empirical research on French hospitals. The results demonstrate a significant influence of BDA-AI on environmental process integration and green supply chain collaboration, ultimately enhancing environmental performance.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Business
R. Sreedevi, Haritha Saranga, Sirish Kumar Gouda
Summary: This study examines the relationship between environmental factors, risk perception, and decision-making in risk management. The findings suggest that a country's logistical capabilities have a significant impact on a firm's risk perception and subsequent decision-making in risk management.
SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Information Systems
Amy Van Looy, Peter Trkman, Els Clarysse
Summary: The study identified seven archetypes for the development of business process orientation and revealed performance differences between organizations at different maturity levels. The results help strengthen the theoretical foundations of business process orientation, make maturity assessments more multifaceted, and assist organizations in focusing their managerial efforts by enabling comparison with peers in the same archetype and showing various improvement paths.
BUSINESS & INFORMATION SYSTEMS ENGINEERING
(2022)
Article
Business
Franciely Morais Dias, Marcos Paulo Valadares de Oliveira, Helio Zanquetto Filho, Alexandre Loureiros Rodrigues
Summary: This study aims to explore the most important value attributes for supermarket retail customers and propose a ranking method. Through quantitative and qualitative research, the study found that price, quality, variety, and proximity are the most important attributes for supermarket consumers. Furthermore, the research provides a methodological contribution to other studies in the marketing field.
REVISTA BRASILEIRA DE MARKETING
(2021)
Article
Computer Science, Information Systems
Maximilian Roeglinger, Ralf Plattfaut, Vincent Borghoff, Georgi Kerpedzhiev, Joerg Becker, Daniel Beverungen, Jan vom Brocke, Amy Van Looy, Adela del-Rio-Ortega, Stefanie Rinderle-Ma, Michael Rosemann, Flavia Maria Santoro, Peter Trkman
Summary: This research paper explores the impact of exogenous shocks on organizational business process management and identifies related challenges and opportunities. Through analysis of existing literature and future research directions, it provides insights to invigorate the academic discourse on this topic.
BUSINESS & INFORMATION SYSTEMS ENGINEERING
(2022)
Review
Business
Marko Budler, Ivan Zupic, Peter Trkman
Summary: Recent reviews have highlighted the usefulness of the business model concept in management analysis, but less attention has been given to the paths of past research. Through bibliometric methods, this study traced the development of business model literature from its origins in e-business to its current state, revealing the influence of various business sub-disciplines on business model foundations and the potential for increased inter-connectedness in research.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Information Science & Library Science
Marina Trkman, Ales Popovic, Peter Trkman
Summary: Researchers proposed a research model to examine how individuals assess the severity of a crisis and decide whether or not to adopt precautionary behaviors during events like COVID-19. They found that the perceived severity of COVID-19 influences citizens' intention to use proximity tracing applications, with personal and societal benefits mediating this relationship.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2021)
Article
Information Science & Library Science
Luka Tomat, Peter Trkman, Anton Manfreda
Summary: This study aims to identify archetypal IS professions, their associated personality types, and examine the reliability of personality tests in IS recruitment. Findings indicate that candidates struggle to fake-good on personality tests for a particular IS profession, suggesting the importance of proper interpretation of test results.
INFORMATION TECHNOLOGY & PEOPLE
(2021)
Article
Business
Marcos Paulo Valadares de Oliveira, Kevin P. McCormack, Marcelo Bronzo, Peter Trkman
Summary: Decision makers are exposed to a growing amount of information and algorithms can assist in making better data-driven decisions. Previous research has examined the role of companies' analytics use and individuals' skills, but individuals can make decisions based on either analysis or intuition. This study investigated the factors influencing the likelihood of making analytical decisions, focusing on individuals' analytical orientation and capabilities.
INTERNATIONAL JOURNAL OF BUSINESS ANALYTICS
(2022)
Article
Information Science & Library Science
Peter Trkman, Matej Cerne
Summary: This article discusses the transition from digitization to human digitization, emphasizing the important role of government and research institutions in this process. It also highlights the need for redefining human processes, virtual experiences, ethics, and information systems education in the future.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2022)
Article
Management
Marcos Paulo Valadares de Oliveira, Robert Handfield
Summary: The study highlighted the importance of specialized analytic skills and an analytics-focused organizational culture in utilizing real-time analytics for supply chain performance improvements. Embracing analytics cultural bias and having a background in statistical fluency can enable decision-makers to make sense of a large amount of data and derive significant improvements in supply chain performance.
INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT
(2022)
Article
Management
Larissa Alves Sincora, Marcos Paulo Valadares de Oliveira, Helio Zanquetto-Filho, Murilo Zamboni Alvarenga
Summary: This study examines the relationship between process management and organizational resilience (OR) in the business context. The findings suggest that maturity in business process management positively influences OR, with the highest level of maturity having the greatest impact. This highlights the importance of mature and well-established processes in enhancing organizational resilience. The proposed model explains 78.5% of OR.
INNOVATION & MANAGEMENT REVIEW
(2023)
Article
Management
Marko Budler, Peter Trkman
Summary: This paper examines the nature of management frameworks in specific realms using analogical reasoning between biological and social systems, and drawing on memetics, intersubjective reality, and the network effect. It explores the origins of well-known frameworks through memetics, the role of the network effect in increasing the value of a framework until it becomes an intersubjective reality, and explains such frameworks as autopoietic within a particular realm.
JOURNAL OF MANAGEMENT & ORGANIZATION
(2023)
Review
Management
Marko Budler, Bernardo F. Quiroga, Peter Trkman
Summary: The growing interest in Supply Chain Transparency (SCT) has led to a lack of clarity in its definition. A scoping review of literature on SCT is necessary to establish a clear understanding of this concept. This review provides a formal conceptualization of SCT, identifies relevant concepts and primary outcomes, and explores future research opportunities.
JOURNAL OF BUSINESS LOGISTICS
(2023)
Article
Business
Murilo Zamboni Alvarenga, Marcos Paulo Valadares de Oliveira, Helio Zanquetto Filho, Kevin C. Desouza, Paula Santos Ceryno
Summary: The ability to recover from disruptions is crucial for both organizations and supply chains. Factors such as collaboration, visibility, flexibility, analytical orientation, and supply chain risk management can positively affect supply chain recovery. Improvement in these capabilities will enable better recovery from disruptions. Additionally, there are mutual impacts between resilience capabilities and supply chain risk management.
RAE-REVISTA DE ADMINISTRACAO DE EMPRESAS
(2022)
Article
Economics
Amila Pilav-Velic, Matej Cerne, Peter Trkman, Sut Wong, Anela Kadic Abaz
Summary: This study found that digital literacy and personal innovativeness have important impacts on individual's innovative work behavior, where digital practices and attitude toward digitalized innovation act as mediators. Digital literacy plays a relatively more important role in stimulating attitudes toward digitalized innovation and innovative work behavior.
SOUTH EAST EUROPEAN JOURNAL OF ECONOMICS AND BUSINESS
(2021)
Article
Management
Larissa Alves Sincora, Teresa Cristina Janes Carneiro, Marcos Paulo Valadares de Oliveira
REVISTA ADMINISTRACAO EM DIALOGO
(2020)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
