Hasil untuk "Information technology"

Yogesh K. Dwivedi, D. L. Hughes, Crispin R. Coombs et al.

Abstract The COVID-19 pandemic has forced many organisations to undergo significant transformation, rethinking key elements of their business processes and use of technology to maintain operations whilst adhering to a changing landscape of guidelines and new procedures. This study offers a collective insight to many of the key issues and underlying complexities affecting organisations and society from COVID-19, through an information systems and technological perspective. The views of 12 invited subject experts are collated and analysed where each articulate their individual perspectives relating to: online learning, digital strategy, artificial intelligence, information management, social interaction, cyber security, big data, blockchain, privacy, mobile technology and strategy through the lens of the current crisis and impact on these specific areas. The expert perspectives offer timely insight to the range of topics, identifying key issues and recommendations for theory and practice.

V. Venkatesh, Hillol Bala

Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre- and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.

M. Porter, Victor A. Millar

V. Venkatesh

Much previous research has established that perceived ease of use is an important factor influencing user acceptance and usage behavior of information technologies. However, very little research has been conducted to understand how that perception forms and changes over time. The current work presents and tests an anchoring and adjustment-based theoretical model of the determinants of system-specific perceived ease of use. The model proposes control (internal and external--conceptualized as computer self-efficacy and facilitating conditions, respectively), intrinsic motivation (conceptualized as computer playfulness), and emotion (conceptualized as computer anxiety) as anchors that determine early perceptions about the ease of use of a new system. With increasing experience, it is expected that system-specific perceived ease of use, while still anchored to the general beliefs regarding computers and computer use, will adjust to reflect objective usability, perceptions of external control specific to the new system environment, and system-specific perceived enjoyment. The proposed model was tested in three different organizations among 246 employees using three measurements taken over a three-month period. The proposed model was strongly supported at all points of measurement, and explained up to 60% of the variance in system-specific perceived ease of use, which is twice as much as our current understanding. Important theoretical and practical implications of these findings are discussed.

D. Robey, Marie-Claude Boudreau

Todd Dewett, Gareth R. Jones

Purnendu Mandal

R. Kohli, Sarv Devaraj

Patrick Y. K. Chau, P. H. Hu

Anol Bhattacherjee, Neşet Hikmet

Anthony Vance, Christophe Elie-Dit-Cosaque, D. Straub

The topic of trust in information technology (IT) artifacts has piqued interest among researchers, but studies of this form of trust are not definitive regarding which factors contribute to it the most. Our study empirically tests a model of trust in IT artifacts that increases our understanding in two ways. First, it sets forth two previously unexamined system quality constructs-navigational structure and visual appeal. We found that both of these system quality constructs significantly predict the extent to which users place trust in mobile commerce technologies. Second, our study considers the effect of culture by comparing the trust of French and American potential users in m-commerce technologies. We found that not only does culture directly affect user trust in IT artifacts but it also moderates the extent to which navigational structure affects this form of trust. These findings show that system quality and culture significantly affect trust in the IT artifact and point to rich possibilities for future research in these areas.

E. Turban, D. Leidner, E. McLean et al.

S. Radenkovic, M. Dugic, I. Radojevic

The answers on the current status and future development of Quantum Science and Technology are presented.

Ebrahim Fihaid Alsubaiy

Complete denture (CD) fabrication has changed dramatically with the introduction of computer-aided design and computer-aided manufacture (CAD-CAM) technologies. These techniques provide benefits including shorter chair times, better material qualities, and cost effectiveness. This systematic study assessed the variables that affect the successful manufacture of CAD-CAM CDs and examined the results and difficulties that follow. Using a thorough approach based on the patient population, intervention, comparison, and outcome framework, manual mining and citation mining were added to electronic searches performed on Google Scholar and PubMed. Studies and clinical reports assessing the clinical outcomes of CAD-CAM CDs were included. Several factors that might affect the performance of CAD-CAM CDs were examined: bond strength, precision, CAD-CAM polymers, and comparisons between digital and traditional dentures. Also examined were issues and mistakes related to clinical performance, time management, denture tooth features, surface qualities, digital workflow, cleaning processes, burs effects, impression techniques, strength, tissue adaptability, patient satisfaction, and retention. The results showed that, albeit with certain drawbacks, CAD-CAM dentures are stronger, more true to size, and fit better than three dimensionally printed options. Overall, digital dentures, as a potential treatment option, are clinically effective, require fewer visits, and improve patient information management. However, problems including speech problems, medical complications, and cosmetic flaws still exist. Before digital dentures become widely accepted, it is imperative to address these limitations. This study highlights the potential of CAD-CAM CDs and identifies areas for further development in clinical practice, offering insightful information about the present status of the technology.

Chen Xinyu, Hui Tianfang, Li Yanlin et al.

As artificial intelligence (AI) is increasingly integrated into society, people are relying on it more and more, and higher requirements are put forward for the safety and ethical standards of AI. This article explores the development of artificial intelligence technology and its potential safety and ethical challenges in various fields. In terms of security, the risk of adversarial attacks is analyzed in depth, and the robustness of the model is enhanced through adversarial training and data enhancement techniques. In addition, it is recommended to adopt measures such as data encryption and differential privacy to address data privacy and security issues. Regarding ethical considerations, this paper identifies the origins of algorithmic bias and argues for mitigating it through rigorous testing, validation, and regulatory frameworks. It also highlights the importance of increasing the transparency and explainability of AI to enhance public trust. Finally, the paper emphasizes the importance of defining accountability for AI behavior and suggests establishing laws and regulations that effectively govern AI applications. In conclusion, the study argues that the development of AI should emphasize safety and ethical considerations. Through the combination of technical intervention, legal supervision and social responsibility, the sustainable development of artificial intelligence is effectively promoted.

Ran Zhang, Xiaoping Wu, Xude Zhang et al.

With the rapid development of information technology, personalized education has become a key direction for improving the quality of online learning and optimizing individualized learning paths. However, accurately recommending appropriate courses and exercises for diverse learners remains a significant challenge. Existing recommendation methods often struggle with effectively modeling learner interests, addressing the cold-start problem, and dynamically adapting recommendation strategies to meet personalized needs. To address these limitations, this paper proposes RL-TBTNet, a novel teaching optimization recommendation framework that integrates a bidirectional Transformer, BERT, and reinforcement learning (DQN). The model first vectorizes user behavior data, learning content, and knowledge base information. Transformer layers are employed for feature encoding, while BERT extracts deep semantic representations to form individualized feature vectors. These features are then fused via Transformer-based processing to predict optimal learning content. In addition, a DQN-based reinforcement learning module models dynamic shifts in user interests, enabling adaptive refinement of learning trajectories over time. Experimental evaluations on public datasets show that RL-TBTNet outperforms existing Transformer-based methods such as BST in terms of key metrics like HR and NDCG, particularly excelling in cold-start scenarios. Ablation studies further confirm the effectiveness of semantic enhancement through BERT and reinforcement-driven optimization. These results demonstrate the framework’s potential as a robust and adaptive solution for personalized educational content recommendation, offering both practical value and theoretical insights for the development of intelligent education systems.

Rui-Hua Jing, Deng-Ke Zhou, Zhuo-Yan Yang et al.

AIM: To use two-sample Mendelian randomization (MR) method to study uveitis causal association with wet age-related macular degeneration (wAMD) risk from the genetic level. METHODS: Two-sample MR analysis was used to assess the causal role of uveitis on wAMD risk, using the 8 genetic variants associated strongly with uveitis as instrumental variables. Besides, eight MR methods [inverse variance weighted (IVW), weighted median, MR-Egger regression, weighted mode, simple mode, robust adjusted profile score (RAPS), contamination inverse-variance weighted method, and debiased inverse-variance weighted method] were used to get the whole causal estimate for multiple instrumental single nucleotide polymorphism (SNPs). The MR analysis was based on Europeans. RESULTS: Uveitis was related to a higher risk of wAMD [odds ratio (OR): 1.08, 95% confidence interval (CI) 1.03–1.12; P=1.03×10-3] with the IVW method. No heterogeneity and directional pleiotropy were detected. On the contrary, no significant results were detected in reverse MR analysis. CONCLUSION: Uveitis is related to an increased risk of wAMD. Due to the high blindness rate of wAMD, understanding and controlling the risk factors of AMD is of great significance for reducing its incidence and early diagnosis and treatment.

Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut et al.

Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be understood in terms of properties of this channel. In this article we examine a few of the key connections between information theory and the formulation/application of differential privacy, giving an ``operational significance'' for relevant information measures.

Badar Almarri, Gaurav Gupta, Ravinder Kumar et al.

Chun-Wei Huang, Si Ying Yau, Chiao-Ling Kuo et al.

Study region: The Choushui River Fan, Taiwan. Study focus: Groundwater overdraft has led to not only groundwater depletion but also environmental disasters, such as subsidence and seawater intrusion in the Choushui River Alluvial Fan, Taiwan. The influence of land subsidence is gradually shifting from the coast to the center of the fan and threatening Taiwan high-speed rail. However, it remains a great challenge to manage and model the groundwater aquifer due to numerous unregulated wells. This study maps and locates private wells using deep learning technologies. We trained and validated convolutional-based deep learning neural networks (DNNs), using street view images. We applied the DNNs to a land subsidence area along the Taiwan high-speed rail, termed the Golden Corridor in Taiwan. The results showed that DNNs can recognize pumping wells with at least 90% accuracy. The testing cases showed their capability to recall all the pumping wells in three road segments along the Golden Corridor. Finally, we spatially estimated potential pumping of a subsidence area using the fine-trained DNNs. New hydrological insights for the region: Given the prevalence of unknown private pumping in the Choushui River Fan, our image data-driven computer vision approach not only eases labor-intensive private well investigations but also advances hydrologic understanding for groundwater modeling. We enhance comprehension of unknown sinks and provide their spatial distribution to improve groundwater modeling.