Article
Business
Kichan Nam, Christopher S. Dutt, Prakash Chathoth, Abdelkader Daghfous, M. Sajid Khan
Summary: This paper investigates the trend of adopting AI and robotics in the hotel industry, focusing on the factors affecting their adoption through in-depth case studies with senior hotel asset managers in Dubai. The study aims to provide insights into the full spectrum of AI application in hotels and how its adoption can be facilitated.
ELECTRONIC MARKETS
(2021)
Article
Health Care Sciences & Services
Glorin Sebastian, Amrita George, George Jackson
Summary: The aim of this study was to examine whether communication strategies (ethos, pathos, and logos) are effective in overcoming factors that hinder AI product adoption among patients. The results indicate that using communication strategies in promoting AI products can improve users' trust, customer innovativeness, and perceived novelty value, leading to increased adoption.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Business
Darius-Aurel Frank, Polymeros Chrysochou, Panagiotis Mitkidis
Summary: This research explores the impact of negative valence on consumers' technology adoption decisions. The findings suggest that consumers are more influenced by the negative properties of innovative technologies and that this influence is mediated by consumers' perception of risk and trust towards the technology. The study also highlights the overlooked bias of negativity in consumer decision-making and its implications for marketing practice.
PSYCHOLOGY & MARKETING
(2023)
Article
Business
Katharina Bloecher, Rainer Alt
Summary: The restaurant technology market is rapidly evolving with the use of AI and robots, transforming the guest experience and automating operations. However, the adoption of these technologies in restaurants is still in its early stages, and managers are seeking guidance to leverage them for service excellence while balancing emotional skills and automation potentials.
ELECTRONIC MARKETS
(2021)
Article
Business
Hsi-Peng Lu, Hsiang-Ling Cheng, Jen-Chuen Tzou, Chiao-Shan Chen
Summary: This research introduces a novel technology roadmap methodology for new retail after studying 30 smart retail cases. Based on panel discussions, it identified 16 service applications, 10 technology applications, 4 application types, and 4 AI development stages for new retail. The findings suggest that new retail should aim towards the fourth stage of AI development, develop a seamless shopping process, and formulate cloud integration platforms integrating data on consumer shopping behavior.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Business
Helmi Issa, Rachid Jabbouri, Mark Palmer
Summary: With recent technological advancements, artificial intelligence (AI)-driven technologies have become increasingly important in the AgriTech sector. This paper explores the process of AI adoption in AgriTech firms and identifies strategic components through a mixed-methods approach. The findings have significant implications for understanding AI technological readiness.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Construction & Building Technology
Seunguk Na, Seokjae Heo, Wonjun Choi, Cheekyung Kim, Seoung Wook Whang
Summary: This research focuses on the workers in construction-related companies in South Korea and the UK as research subjects to analyze the factors that influence their usage intention of AI-based technologies. The results show that perceived usefulness has a positive impact on technological satisfaction and usage intention. The most remarkable differences between the two countries lie in personal competence and social influence when choosing AI-based technologies.
Article
Health Care Sciences & Services
Avishek Choudhury, Hamid Shamszare
Summary: This study examines the impact of users' trust in ChatGPT on their intent and actual use of the technology. The findings indicate that trust is crucial for users' adoption of ChatGPT. However, overreliance or blind trust in the technology can have severe consequences in high-stakes decision-making contexts, while lacking trust may result in missed opportunities.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Psychology, Multidisciplinary
Leah Chong, Guanglu Zhang, Kosa Goucher-Lambert, Kenneth Kotovsky, Jonathan Cagan
Summary: This research investigates the impact of human self-confidence on decision-making regarding accepting or rejecting AI suggestions, finding that human self-confidence plays a significant role in determining decisions.
COMPUTERS IN HUMAN BEHAVIOR
(2022)
Article
Engineering, Industrial
Lars Meyer-Waarden, Julien Cloarec
Summary: Artificial intelligence-powered autonomous vehicles are highly anticipated technological advancements, but consumer reluctance towards adoption exists. Understanding the factors influencing user acceptance, such as psychological, social, and cognitive factors, is crucial. The study found that factors like performance/effort expectancy, social recognition, well-being, hedonism, technology trust, and security positively influence user intention to use AI-powered AVs, while privacy concerns have a negative impact.
Review
Anesthesiology
M. McKendrick, S. Yang, G. A. McLeod
Summary: The current fourth industrial revolution is characterized by the blurring of physics, computing, and biology, driven by data and artificial intelligence. In the field of regional anaesthesia, the application of artificial intelligence is limited, but new technologies such as robotics and artificial sensing may have an impact. The future of robotics in anaesthesia includes pharmaceutical, mechanical, and cognitive robots, with a focus on developing accurate sensors and providing training in augmented reality environments.
Article
Management
Abhinav Hasija, Terry L. Esper
Summary: This study explores the role of organizational factors in reconciling the differences between the potential benefits of AI in supply chain management and its actual acceptance and use. The findings suggest several tactics that could be used to convey AI trustworthiness and call for further research on the effects of AI trustworthiness on internal, upstream, and downstream activities in the supply chain.
JOURNAL OF BUSINESS LOGISTICS
(2022)
Article
Information Science & Library Science
Victoria Uren, John S. Edwards
Summary: Artificial Intelligence (AI) is considered to have significant potential, but its history of boom and bust cycles can make adopters cautious. A study with AI experts suggests that readiness in people, process, data, and technology is necessary for long-term success. The findings also highlight the importance of bridging technical and business functions in innovative organizations.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2023)
Article
Business
Scott Thiebes, Sebastian Lins, Ali Sunyaev
Summary: Artificial intelligence presents both opportunities and challenges, with Trustworthy AI emphasizing the importance of trust in its development and deployment. Its five foundational principles include beneficence, non-maleficence, autonomy, justice, and explicability. A data-driven research framework can help delineate fruitful avenues for future research in the realization of Trustworthy AI.
ELECTRONIC MARKETS
(2021)
Article
Business
Edward Felten, Manav Raj, Robert Seamans
Summary: The study introduces and validates a new measure of an occupation's exposure to AI, known as the AI Occupational Exposure (AIOE). The AIOE is used to create industry-level (AIIE) and county-level (AIGE) measures of AI exposure, and can be utilized in various applications by management, organization, and strategy scholars.
STRATEGIC MANAGEMENT JOURNAL
(2021)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)
