Article
Computer Science, Cybernetics
Abdelkader M. A. Mobarak, Mona Dakrory, Mohamed M. Elsotouhy, Mohamed A. Ghonim, Mohamed A. Khashan
Summary: This study investigates the effects of reasons for and reasons against on Egyptian customers' continuance intentions to use and recommend m-payment services. The findings suggest that both reasons for and reasons against have an impact on attitude and continuance intention to use m-payment services, which in turn affects word-of-mouth. Relative advantage, mobility, gamification, and service quality are identified as reasons for continuance intention, while image barriers, anxiety, skepticism, and perceived time risk are reasons against continuance intention. Additionally, consumer attitude influences customers' continuance intentions to use m-payment services.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2022)
Article
Business
Ruolan Chen, Ruizhi Yuan, Bo Huang, Martin J. Liu
Summary: Marketing managers often use incentives to encourage eWOM sharing, but it is unclear which types of incentives are more effective. Three experimental studies were conducted to investigate the effect of incentive type (economic vs. altruistic) on customers' eWOM sharing intentions. The results showed that altruistic incentives generated higher eWOM sharing intentions compared to economic incentives. The studies also revealed the mediating roles of customers' perceptions of warmth and skepticism towards the company in the relationship between incentive type and eWOM sharing intentions. Furthermore, the studies identified the target customers for incentivized eWOM programs, showing that altruistic incentives are more effective for customers who are alone and economic incentives are more effective for customers with weak ties to the company.
JOURNAL OF BUSINESS RESEARCH
(2023)
Article
Business
Raouf Ahmad Rather, Linda D. Hollebeek
Summary: This study examines the impact of customer engagement (CE) on experience and behavioral intent, finding that cognitive engagement has a stronger effect on experience for younger customers compared to older ones. The associations between affective/behavioral engagement and experience are significant across all age groups, with the strength of the association increasing with customer age. Additionally, customer experience has a stronger influence on behavioral intention as customers get older.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2021)
Article
Psychology, Multidisciplinary
Bing Han, Hua Fan
Summary: This study explores how online sales agents' balanced and imbalanced ambidextrous learning influence customers' e-loyalty and, in turn, their patronage intention and behavior. The results support the hypothesized balance effect and identified asymmetrical imbalance effects.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Construction & Building Technology
Xia Wang, Lizhen Shen, Shuyue Shi
Summary: Understanding user perception of underground space is crucial for constructing a human-friendly environment. This study constructed evaluation indexes for user perception and assessed the Xinjiekou underground complex in China. The findings shed light on theoretical investigation and practical construction of urban underground spaces.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2023)
Article
Humanities, Multidisciplinary
Maohao Che, Sze Yee Ashley Say, Han Yu, Qingji Zhou, Jared Shu, Wen Sun, Xi Luo, Hong Xu
Summary: This study investigates how continuous trust is formed among mobile banking customers at the continuous-use stage, and proposes a model to predict trust. Data from an online survey of 450 frequent mobile banking users were analyzed using structural equation modeling. The findings validate the proposed model, and suggest that customers' continuous trust can be predicted by factors such as perceived ease of use, privacy assurance and security features of mobile banking apps, organization reputation, customer support, and previous experience. The model also proposes and validates the interactive relationships among these factors.
HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS
(2023)
Article
Food Science & Technology
Teerapong Pienwisetkaew, Sasichakorn Wongsaichia, Benyapa Pinyosap, Supakkarn Prasertsil, Kunjira Poonsakpaisarn, Chavis Ketkaew
Summary: This research explores the agricultural food waste in Thailand, focusing on the manufacturing and retail sector in the northeastern region. The study aims to investigate user segments and factors influencing their intentions to use mobile technology for agricultural waste utilization. Demographic variables such as age, income, and gender were used to classify user segments, and multigroup structural equation modeling was employed to analyze their behavioral intentions. The results revealed two user types: older users with varying income ranges and younger users with a low-income range. Age and income were significant variables for demographic segmentation, while gender was not. Factors like social influence, price value, and trust significantly influenced the intentions of older and various-income users, while privacy strongly affected the intentions of younger users. Habit or regularity influenced the intentions of users in both segments. This study provides valuable implications for platform strategies and user behaviors in the circular agricultural sector.
Article
Education & Educational Research
Liang-Miin Tsai, Yu-Hua Yan
Summary: The study found that medical education behavioral intention of clinical teachers and students is influenced by subjective norm and perceived behavioral control. It is recommended to design holistic medical education teaching templates and check forms in the curriculum to encourage clinical teachers to reassess their beliefs in teaching, learning, and knowledge.
BMC MEDICAL EDUCATION
(2021)
Article
Business, Finance
Megha Gupta, Suhasini Verma, Smita Pachare
Summary: Alternative financing has been growing globally, and India is focusing on the application of financial technology to enhance personalized financial services. Customer perceptions and experiences are crucial for the growth of the service sector. This study aims to understand customer adaptability and perceived ease of use of alternative financing, providing insights for financial institutions to strategize better.
INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS
(2023)
Article
Business
Filipe Araujo Silva, Alireza Shabani Shojaei, Belem Barbosa
Summary: The main objective of this article is to investigate the factors influencing customers' intention to reuse chatbot-based services. The study develops a theoretical model based on the technology acceptance model (TAM) and other contributions in the literature. Using structural equation modeling (PLS-SEM), the research finds that user satisfaction, perceived usefulness, and subjective norm significantly predict chatbot reuse intentions. Additionally, perceived usefulness, perceived ease of use, and trust have a positive impact on attitudes toward using chatbots. The article concludes with theoretical contributions and recommendations for managers.
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
(2023)
Article
Psychology, Multidisciplinary
Mingli Zhang, Yafei Liu, Yu Wang, Lu Zhao
Summary: Live streaming commerce has become the mainstream of e-commerce, yet a comprehensive model explaining why customers continue to use this new sales format is lacking. Research findings show that trust can be enhanced through live interactivity and technical enablers, influencing users' continuance intention.
COMPUTERS IN HUMAN BEHAVIOR
(2022)
Article
Engineering, Civil
Oliver Werth, Marc-Oliver Sonneberg, Max Leyerer, Michael H. Breitner
Summary: Ridepooling is a new mobility service aimed at city dwellers, offering potential reductions in road traffic and emissions through optimized pooling. Attitude towards use, perceived usefulness, and performance expectancy were found to influence users' intention to use ridepooling services, while environmental awareness, price value, and effort expectancy did not.
TRANSPORTATION RESEARCH RECORD
(2021)
Article
Green & Sustainable Science & Technology
Meng Yin, Syed Muhammad Usman Tayyab, Xiao-Yu Xu, Shuo-Wei Jia, Chih-Lun Wu
Summary: This study investigates the influence of stickiness, instrumental and social, on continuance usage intentions and in-app purchase intentions in mobile fitness apps, based on social support theory. The research reveals that different types of social support play a significant role in stickiness, and that instrumental stickiness and social stickiness have different impacts on user continuity intentions.
Article
Psychology, Multidisciplinary
Yun Liu, Xin Sun
Summary: By constructing an integrated model, this study investigates the impact of algorithmic ethical perceptions and algorithmic legitimacy on the continuous usage intention of e-commerce platforms. The results show that the fairness, accountability, and transparency of algorithmic processes positively affect algorithmic legitimacy, which in turn positively influences the continuous usage intention of e-commerce platforms. Additionally, user innovativeness moderates the relationship between algorithmic legitimacy and continuous usage intention.
COMPUTERS IN HUMAN BEHAVIOR
(2024)
Article
Green & Sustainable Science & Technology
R. Spielhofer, T. Thrash, U. Wissen Hayek, A. Gret-Regamey, B. Salak, J. Grubel, V. R. Schinazi
Summary: The research findings suggest that viewing landscapes with high RES can lead to higher physiological arousal, while participants tend to prefer landscapes with low RES, especially natural landscapes. Preferences for landscape also varied significantly among different landscape types.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Computer Science, Software Engineering
Mah Noor Asmat, Saif Ur Rehman Khan, Shahid Hussain
Summary: This study aims to identify the state-of-the-art approaches, tools, root causes, and metrics for uncertainty mitigation in Cyber-Physical Systems (CPS). The study conducted a systematic literature review and keyword-based search to find potential relevant studies and validated the selection using an index engine. The results of this study are important for guiding future research in mitigating uncertainty causes in CPS with new approaches or tools.
JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
(2023)
Article
Computer Science, Artificial Intelligence
Meera Ramadas, Ajith Abraham
Summary: Air pollution is a global issue that can cause major health hazards. Satellite remote sensing is an effective way to monitor the atmosphere and improve understanding of complex images through clustering and segmentation techniques. The novel DiDE algorithm showed superior outcomes compared to traditional approaches, and its application in multi-level thresholding significantly reduced computational delay and improved image quality.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Analytical
Bhaskar Kapoor, Bharti Nagpal, Praphula Kumar Jain, Ajith Abraham, Lubna Abdelkareim Gabralla
Summary: This paper proposes a hybrid optimization-controlled ensemble classifier to automatically analyze EEG signal dataset for epileptic seizure prediction, combining signal processing and machine learning. The proposed technique shows high accuracy, sensitivity, and specificity in early seizure prediction.
Article
Computer Science, Information Systems
E. Rajalakshmi, R. Elakkiya, V. Subramaniyaswamy, L. Prikhodko Alexey, Grif Mikhail, Maxim Bakaev, Ketan Kotecha, Lubna Abdelkareim Gabralla, Ajith Abraham
Summary: A novel vison-based hybrid deep neural net methodology is proposed in this study for recognizing Indian and Russian sign gestures. The proposed framework aims to establish a single framework for tracking and extracting multi-semantic properties, such as non-manual components and manual co-articulations. By using a 3D deep neural net with atrous convolutions for spatial feature extraction, attention-based Bi-LSTM for temporal and sequential feature extraction, modified autoencoders for abstract feature extraction, and a hybrid attention module for discriminative feature extraction, the proposed sign language recognition framework yields better results than other state-of-the-art frameworks.
Article
Computer Science, Information Systems
S. U. Aswathy, P. P. Fathimathul Rajeena, Ajith Abraham, Divya Stephen
Summary: Lung malignancy, one of the most common types of cancer worldwide, was studied in this research. The study focused on the multifaceted nature of lung cancer diagnosis and proposed a method using nanotechnology for precise segmentation of lesions in nano-CT images. The results showed high accuracy and precision in tumor classification and segmentation.
Article
Multidisciplinary Sciences
Atika Qazi, Najmul Hasan, Christopher M. Owusu-Ansah, Glenn Hardaker, Samrat Kumar Dey, Khalid Haruna
Summary: Online communities provide opportunities for sharing public opinions and sentiments on various subjects. This study examines the underutilization of mobile learning applications (MLAs) and proposes the SentiTAM model to understand students' sentiments on MLA platforms. The results show that sentiments and self-motivation significantly influence MLA use intention, while perceived usefulness and ease of use directly affect MLA usage. This study contributes to the understanding of important factors in MLA adoption and can guide developing countries in utilizing MLA with emerging technology.
Article
Computer Science, Artificial Intelligence
Benkuan Cui, Kun Ma, Leping Li, Weijuan Zhang, Ke Ji, Zhenxiang Chen, Ajith Abraham
Summary: Despite the benefits provided by the Internet and social media, the proliferation of fake news has had negative effects on society and individuals. This paper proposes a Chinese fake news detection model using a Third-order Text Graph Tensor and Information Propagation Network. Data augmentation and a novel text graph tensor representation are employed to address the challenges of feature sparsity and capturing context information. The model outperforms existing methods in fake news detection according to experimental results on four public datasets.
APPLIED INTELLIGENCE
(2023)
Article
Multidisciplinary Sciences
Mincheol Shin, Mucheol Kim, Geunchul Park, Ajith Abraham
Summary: High-performance computing supports advancements in various scientific disciplines by providing computing power and insights. This paper proposes an adaptive variable sampling model for performance analysis in high-performance computing environments. The model automatically selects optimal variables for performance prediction without requiring expert knowledge. Experiments show that the model improves speed by at least 24.25% and up to 58.75% without sacrificing accuracy.
Article
Engineering, Electrical & Electronic
Ankit Rajpal, Subodh Kumar, Neeraj Kumar Sharma, Ajith Abraham, Anurag Mishra, Naveen Kumar
Summary: This paper proposes a chest X-ray image watermarking scheme (CXRmark) using an online sequential reduced kernel extreme learning machine (OS-RKELM). The scheme segments the lung area into the region of non-interest (RONI) and region of interest (ROI) using U-Net, and modulates the approximation coefficients using OS-RKELM with different embedding strengths for ROI and RONI. Experimental results on 461 CXR images demonstrate that CXRmark outperforms other schemes in terms of perceptual quality and robustness.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2023)
Review
Automation & Control Systems
Shreyas Gawde, Shruti Patil, Satish Kumar, Pooja Kamat, Ketan Kotecha, Ajith Abraham
Summary: Industry 4.0 is the era of smart manufacturing, which relies heavily on machinery. Maintaining critical rotating machines is the top priority for engineers to minimize unplanned shutdowns and increase their useful life. This paper aims to provide a systematic literature review on the data-driven approach for multi-fault diagnosis of industrial rotating machines, highlighting the foundational work, comparative study, major challenges, and research gaps in this field.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Gargi Joshi, Ananya Srivastava, Bhargav Yagnik, Mohammed Hasan, Zainuddin Saiyed, Lubna A. Gabralla, Ajith Abraham, Rahee Walambe, Ketan Kotecha
Summary: Web Information Processing (W.I.P.) has had a significant impact on modern society as many people rely on the internet for information. Social Media platforms provide both a means of disseminating information and a breeding ground for misinformation. Machine learning models have been used to detect misinformation, but the development of generalized and explainable detectors remains a challenge. Integrating domain adaptation and explainable A.I. approaches can address these challenges.
Article
Computer Science, Information Systems
Mayur Wankhade, Chandra Sekhara Rao Annavarapu, Ajith Abraham
Summary: Sentiment classification is a crucial task in natural language processing. This research investigates the impact of text preprocessing techniques on sentiment classification and proposes a novel framework called CBMAFM that leverages the synergistic power of CNN and BiLSTM through a multi-attention fusion mechanism. The framework preserves both local and global context dependencies, resulting in improved performance compared to other state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Behzad Saemi, Ali Asghar Rahmani Hosseinabadi, Azadeh Khodadadi, Seyedsaeid Mirkamali, Ajith Abraham
Summary: The task scheduling problem in Mobile Cloud Computing (MCC) is a difficult problem to solve, and this study proposes a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique to address this issue. By comparing with other algorithms, it is found that the proposed method performs better in terms of job completion time and energy savings.
Article
Computer Science, Information Systems
Deepali Arun Bhanage, Ambika Vishal Pawar, Ketan Kotecha, Ajith Abraham
Summary: This paper proposes a semantic log analysis model that utilizes three log features to capture the essence of the log message. By employing the BERT pre-trained model and an attention-based OLSTM classifier, the proposed model is able to detect failures in different infrastructures. The evaluation results demonstrate that the system delivers improved and stable results across various IT infrastructures.
Article
Information Science & Library Science
Muhammad Javed Ramzan, Saif Ur Rehman Khan, Inayat Ur-Rehman, Muhammad Habib Ur Rehman, Ehab Nabiel Al-khannaq
Summary: This study aims to guide transmuters in becoming data scientists by exploring the challenges faced by data scientists according to their educational backgrounds. The findings reveal significant variability in skill requirements and tool usage based on educational background, but regardless of academic background, data scientists spend more time analyzing data than operationalizing insight. The study provides suggestions for universities and online academies to recommend required knowledge for prospective students based on their educational background.
