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Journal articles on the topic 'User Behavioral Analysis'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Xia, Chunyan. "Research on User Interface Design of Home Appliances Based on Behavioural Logic." SHS Web of Conferences 152 (2023): 05003. http://dx.doi.org/10.1051/shsconf/202315205003.

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It is to solve the usability problem of user interface of home appliances under the development trend of home appliance automation, informatization and multi-function through the design of home appliance user interface dominated by user behavioral logic. It uses the methods of comparative analysis and empirical analysis, and it's distilled and compared the main difference between behavioral logic-based user interface and the functional logic-based user interface of home appliances, and then it summarizes and analyzes the main characteristics and advantages of behavioural logic-based user inter

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Wang, Yaoxuan. "Research on User Profile and User Behavior of Integrating Big Data Platforms." Transactions on Economics, Business and Management Research 8 (August 8, 2024): 446–54. http://dx.doi.org/10.62051/3a6dex21.

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This paper discusses the construction and analysis method of user behavioral portrait by the data provided by the electric power platform in the big data environment. Firstly, it introduces the construction and analysis of user profiles based on big data platforms, which covers the construction of user basic attribute profiles, user behavioral characteristics profiles, user product characteristics profiles and user interaction characteristics profiles from different dimensions. Secondly, for the electric power sector, the article discusses the analysis of big data provided by electric power pl

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Hidayah, Nur Aeni, Meinarini Catur Utami, and Irfan Nur Rizki. "Behavioral Intentions of Generation Z and Millennial Users of Telemedicine: A UTAUT 2 Analysis from the Halodoc User Perspective." Journal of Information Systems and Informatics 6, no. 3 (2024): 1373–99. http://dx.doi.org/10.51519/journalisi.v6i3.786.

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The rapid development of telemedicine has significantly transformed health services, leveraging technological sophistication to enhance medical consultations and treatments. Halodoc, a leading telemedicine application, has positively impacted the health sector. However, user reviews on the Google Play Store indicate issues that may affect user satisfaction and behavioral intention. Despite Millennials and Generation Z being major digital platform users, Generation X reports the highest satisfaction with telemedicine services. This study aims to validate the factors influencing and interrelatin

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Qiao, Yu, Kaixian Xu, and Alan Wilson. "Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis." Artificial Intelligence Advances 7, no. 1 (2025): 10–21. https://doi.org/10.30564/aia.v7i1.9761.

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Real-time personalized ad recommendation systems are crucial for enhancing user engagement and satisfaction. To address the challenge of delivering highly relevant ads in a dynamic, large-scale environment, this paper proposes a novel approach that integrates real-time user behavior analysis with advanced time series modeling and stream processing techniques. Specifically, the system leverages Long Short-Term Memory (LSTM) networks to capture both short-term and long-term user preferences, ensuring accurate and personalized ad recommendations. By utilizing stream processing frameworks like Apa

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Liang, He, Sufri Muhammad, and M. N. Shah Zainudin. "Data-Driven User Personas in Requirement Engineering with NLP and Behavior Analysis." JOIV : International Journal on Informatics Visualization 8, no. 4 (2024): 2033. https://doi.org/10.62527/joiv.8.4.3625.

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As technology rapidly evolves, software development faces growing complexity, requiring adaptation to dynamic user expectations. This study addresses a critical gap in the existing literature by integrating behavioral data and sentiment analysis into the user persona development process within the requirement engineering framework. The primary objective is to create more accurate and representative user personas that better guide software design and development. To achieve this, the research employs advanced Natural Language Processing (NLP) techniques to systematically analyze extensive behav

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Praneeth, Praneeth, Naresh Naresh, Sai Kiran, Anvesh Anvesh, and Deepak Deepak. "Smart Attendance and Behavioural Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42936.

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This project introduces the Smart Attendance and Behavioural Analytic System, designed to enhance student engagement and learning outcomes in modern educational environments. Utilizing advanced voice recognition technology, the system automates attendance marking, allowing students to check in simply by stating their name or a designated phrase. This automation significantly reduces the time and effort required for manual attendance procedures. The system incorporates Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyze students' vocal expressions, tone, and speech

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Cai, Xiaobo, Jiajin Zhang, Yue Zhang, Xiaoshan Yang, and Ke Han. "LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT Applications." Electronics 13, no. 16 (2024): 3234. http://dx.doi.org/10.3390/electronics13163234.

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In Internet of Things (IoT) applications, user behavior is influenced by factors such as network structure, user activity, and location. Extracting valuable patterns from user activity traces can lead to the development of smarter, more personalized IoT applications and improved user experience. This paper proposes a LIME-based user behavior preference mining algorithm that leverages Explainable AI (XAI) techniques to interpret user behavior data and extract user preferences. By training a black-box neural network model to predict user behavior using LIME and approximating predictions with a l

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Qu, Li. "Optimization Strategies for Intelligent Book Reading Based on User Behavior Analysis." Journal of Social Science and Humanities 7, no. 4 (2025): 141–45. https://doi.org/10.53469/jssh.2025.7(04).24.

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This study addresses the issues of user attention fragmentation and cognitive mismatch caused by static content presentation in digital reading platforms, proposing intelligent book reading optimization strategies grounded in user behavior analysis. By capturing interaction frequency, temporal continuity, and composite behavioral data, we developed a multi-dimensional indicator model to reflect reading states and employed clustering algorithms, temporal pattern analysis, and association rule mining to decode behavioral patterns. Building on these insights, a three-tiered optimization framework

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Rahul, C. R., and A. Rengarajan. "Behavioural Biometrics as a User Authentication Mechanism in ISMS." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 03 (2024): 1699–706. http://dx.doi.org/10.15680/ijircce.2024.1203054.

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Securing sensitive data and mitigating cyber threats necessitates robust user authentication mechanisms within Information Security Management Systems (ISMS). Conventional authentication approaches, such as passwords and static biometric identifiers, exhibit vulnerabilities, urging the adoption of more sophisticated solutions. Behavioral biometrics, which analyzes unique behavioral patterns like typing rhythm and mouse movements, offers a promising avenue for enhancing authentication security while optimizing user experience. This paper examines the integration of behavioral biometrics into IS

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Fuchs, Beth E., Cristina M. Thomsen, Randolph G. Bias, and Donald G. Davis. "Behavioral Citation Analysis: Toward Collection Enhancement for Users." College & Research Libraries 67, no. 4 (2006): 304–24. http://dx.doi.org/10.5860/crl.67.4.304.

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A pilot study was developed to determine use of the University of Texas at Austin General Libraries’ research collections in the fields of civil engineering and educational psychology and to investigate the research behavior of graduate students. First, the authors sampled bibliographic citations from dissertations completed during the years 1997 and 2002 in the above-named fields. Then, a survey was sent to the dissertation writers to gain insight into use and opinions of library services for their graduate research. Analysis of information provided by both collection-and user-centered data-g

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Vardhineedi, Padma naresh, and Dr Pooja Sharma. "User Behavior Analysis in AI-Powered Financial Advisory Platforms." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 142–60. https://doi.org/10.63345/ijrmeet.org.v13.i3.9.

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The integration of Artificial Intelligence (AI) in financial advisory platforms has profoundly altered the financial services sector, providing users with personalized advice and predictive analytics. Yet, amidst the technological advancements, the research gap in understanding user behavior in relation to AI-driven systems remains a vital challenge. This paper seeks to investigate the research gap in the area of how AI platforms engage with users, affect their decision-making, and drive user engagement. While existing research has revolved around AI’s technical potential, few have considered

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Indah, Dwi Rosa, Arwin Permata Putra, and Mgs Afriyan Firdaus. "Analysis of User Acceptance Using UTAUT2 Model in KAI Access Application." Jurnal Teknologi Informasi dan Pendidikan 15, no. 2 (2023): 85–95. http://dx.doi.org/10.24036/jtip.v15i2.553.

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The convenience provided by mobile applications provides substantial opportunities for companies, especially those engaged in the service sector to improve performance and service to customers and get the best feedback as the value of the company's competitive advantage. This study aims to determine the factors that influence behavioral intention and use behavior in the KAI Access application using the UTAUT2 model for users of the KAI Access application. The variables used in this study are performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivatio

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Gamage, Dilrukshi, Indika Perera, and Shantha Fernando. "Exploring MOOC User Behaviors Beyond Platforms." International Journal of Emerging Technologies in Learning (iJET) 15, no. 08 (2020): 161. http://dx.doi.org/10.3991/ijet.v15i08.12493.

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MOOC user behavior is generally studied using the data collected within platform interactions in the learning system or via outside social media platforms. It is important to understand the root causes of anomalies in MOOCs, such as the 80% attrition, less interactions within platforms and what causing the reflected behaviors beyond platforms. We study MOOC student behaviors outside the platform using ethnographic methods, mainly focusing on diary study and interviews. Two groups, 11 extreme users who have completed many MOOCs and 10 who never completed MOOC have been used to collect data. The

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Wang, Wei Hua. "User Behavioral Characteristics Mining Algorithms Based on Net-Flow." Advanced Materials Research 791-793 (September 2013): 1130–33. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1130.

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Network security technology-based firewall, intrusion detection, less user behavior from the network point of view to is considered possible vandalism. Net Flow collector for this problem, the network traffic statistics, the design of said data structures and statistical models of user behavior characteristics, the behavior of specific information, the establishment of the database behavior in a certain period of time granularity; on the basis of the behavior of database the design the clustering mining algorithm applicable to user behavior features, defined from the user behavior, determine t

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Perdananti, Primanola, and Siska Noviaristanti. "Extended UTAUT2-Based Analysis of User Acceptance of EIGER’s Mobile Shopping App." Journal Research of Social Science, Economics, and Management 4, no. 7 (2025): 959–80. https://doi.org/10.59141/jrssem.v4i7.782.

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This study investigates user acceptance of the EIGER Adventure mobile application, designed to enhance online shopping convenience for outdoor gear enthusiasts in Indonesia. The research employs an extended UTAUT2 model, incorporating Perceived Risk and Trust variables, to analyze factors influencing consumer behavior. A purposive sampling method was used to collect data from 478 respondents who are active users of the EIGER application. The research adopts a quantitative approach, with data analyzed using Structural Equation Modeling (SEM) through SmartPLS 4. The results indicate that Social

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Sadabadi, Ali Asghar, Narges Shahi, and Zohreh Rahimirad. "Analysis of User Behavior in Order to Reduce Energy Consumption through Responsible Innovation." Weather, Climate, and Society 17, no. 2 (2025): 269–86. https://doi.org/10.1175/wcas-d-24-0030.1.

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Abstract The worldwide increase in energy consumption presents significant challenges spanning social, economic, and environmental spheres. Governments respond to these challenges by implementing responsible innovation (RI) and participatory governance. A key strategy involves promoting energy-saving behaviors that are recognized for their role in reducing consumption. This paper integrates behavioral intervention variables into RI using the theory of planned behavior (TPB) to predict energy-saving behaviors. In exploring attitudes, subjective norms, perceived behavioral control, and intervent

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Zhou, Wei, and Minnan Cang. "Research on the Impact of User Behavior on Sustainable Design Strategies." Journal of Education and Educational Research 10, no. 2 (2024): 192–98. http://dx.doi.org/10.54097/eh8yv519.

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This study aims to explore the influence of user behavior on sustainable design strategies and proposes a framework for optimizing design interventions. User behavior data regarding energy use, resource consumption, and waste management were collected through surveys, in-depth interviews, and virtual reality simulations. K-means clustering and Principal Component Analysis (PCA) were employed to systematically identify user behavior patterns. The findings reveal three distinct user groups: environmentally conscious, economically driven, and neutral/passive. Experimental results validated the ef

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Ankit Awasthi. "Adapting Language Models to User Behavior: A Technical Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1453–64. https://doi.org/10.32628/cseit25112493.

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The adaptation of transformer architectures to user behavior analysis marks a pivotal advancement in processing event streams and temporal data. While originally designed for natural language processing, these architectures have been successfully modified to handle the unique challenges of user behavior sequences, including concurrent events, variable time intervals, and complex temporal dependencies. Through enhanced positional encoding and specialized attention mechanisms, these models can effectively capture both short-term actions and long-term behavioral patterns in B2C environments. The

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Ghule, Poonam Ajit. "AI in Behavioral Economics and Decision-Making Analysis." Journal for Research in Applied Sciences and Biotechnology 4, no. 1 (2025): 124–31. https://doi.org/10.55544/jrasb.4.1.15.

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Artificial Intelligence (AI) have greatly transformed the science of Behavioural Economics and decision-making in applications predominantly on digital marketplaces, finance among others. Research examines the AI impact on consumer decision making, pricing and financial decision making with a specific emphasis on the Indian e-cockpit and fin-tech. By utilizing AI-driven recommendation systems, dynamic pricing models, and behavioral nudging techniques, businesses can enhance user engagement and optimize sales. The research examines Flipkart’s AI-powered recommendation system as a case study, re

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Jang, So-Jeong, HENGYU KANG, YANAN CHEN, and Ha-Kyun Kim. "The Impact of UTAUT2-Based Artificial Intelligence Technology on User Satisfaction through Actual System Use." Global Convergence Research Academy 3, no. 2 (2024): 101–13. https://doi.org/10.57199/jgcr.2024.3.2.101.

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Artificial Intelligence (AI) is a computer system that performs perception, reasoning, learning, and language abilities similar to humans, making it a core technology of the Fourth Industrial Revolution. Particularly, generative AI, which creates new content such as text, images, and music through large-scale data learning, has garnered significant attention. This technology utilizes artificial neural networks and machine learning to understand user intentions, learn from data, and generate various types of content. AI is defined as a technology that implements human intelligence-related funct

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Stawarz, Katarzyna, Chris Preist, Debbie Tallon, Nicola Wiles, and David Coyle. "User Experience of Cognitive Behavioral Therapy Apps for Depression: An Analysis of App Functionality and User Reviews." Journal of Medical Internet Research 20, no. 6 (2018): e10120. http://dx.doi.org/10.2196/10120.

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Arasu, Thiruchendhil, E. George Dharma Prakash Raj, and Murali Krishna. "Scaled User Rating Algorithm to Perform Behavioral Analysis for Cloud Secure 360." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 551–59. http://dx.doi.org/10.26438/ijcse/v6i6.551559.

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Han, Sang-Lin, and Myoung-a. An. "Analysis of User Telepresence and Behavioral Intention in Virtual Reality Shopping Environment." Journal of Channel and Retailing 24, no. 1 (2019): 51–71. http://dx.doi.org/10.17657/jcr.2019.01.31.3.

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Han, Sang-Lin, and Myoung-a. An. "Analysis of User Telepresence and Behavioral Intention in Virtual Reality Shopping Environment." Journal of Channel and Retailing 24, no. 1 (2019): 51–71. http://dx.doi.org/10.17657/jcr.2019.1.31.3.

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Arun, V., and R. Sudhakar. "User Behavioral Analysis Using MarkovChain and Steady-State in Tracer andChecker Model." Journal of Cyber Security and Mobility 8, no. 2 (2019): 277–94. http://dx.doi.org/10.13052/jcsm2245-1439.826.

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Tzafilkou, Katerina, Nicolaos Protogeros, Charalampos Karagiannidis, and Adamantios Koumpis. "Gender-based behavioral analysis for end-user development and the ‘RULES’ attributes." Education and Information Technologies 22, no. 4 (2016): 1853–94. http://dx.doi.org/10.1007/s10639-016-9519-4.

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Sleiman, Kamal Abubker Abrahim, Lan Juanli, Hongzhen Lei, Ru Liu, Yuanxin Ouyang, and Wenge Rong. "User Trust levels and Adoption of Mobile Payment Systems in China: An Empirical Analysis." SAGE Open 11, no. 4 (2021): 215824402110565. http://dx.doi.org/10.1177/21582440211056599.

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The technology acceptance model (TAM) has been regarded as a promising model for understanding technology adoption and can be extended to different situations. Currently, mobile payment services have been widely applied in people’s daily lives in China, and understanding their critical success factors is becoming important. Mobile payments are a complex system, and a large number of factors affect their success. Since mobile payments are directly related to financial issues, their wide adoption relies heavily on people’s trust. We developed a model based on the TAM to investigate the most infl

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Ramesh Prasad Pokhrel. "Behavioral analysis of malware using sandboxing techniques." International Journal of Science and Research Archive 15, no. 3 (2025): 582–86. https://doi.org/10.30574/ijsra.2025.15.3.1781.

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This research paper investigates the dynamic behavioral analysis of Windows-based Portable Executable (PE) malware samples using sandboxing techniques. The study focuses on comparing various sandboxing methodologies with an emphasis on their ability to detect sophisticated malware behaviors in a controlled environment. In particular, techniques such as the incorporation of realistic user behavior emulation and the integration of machine learning with sandbox environments are examined. The methodology involves deploying agent-based and agent-less sandbox systems to monitor malware execution and

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Farikhah, Sayyidah 'Aisy, and Paramitha Nerisafitra. "Analysis of User Satisfaction Level of myIM3 Application Using TAM Method." Applied Technology and Computing Science Journal 4, no. 2 (2022): 130–37. http://dx.doi.org/10.33086/atcsj.v4i2.3096.

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User satisfaction is a critical thing in handling a business. The company must be capable to provide quality services for the products/services provided by them. MyIM3 application is an application developed by PT. Indosat Ooredoo Hutchison to check and purchase credit and data packages online, find promotions offered by Indosat Ooredoo Hutchison, etc. More than 10 million users have downloaded the myIM3 app on the PlayStore and AppStore. The number of features offered makes users often experience problems in accessing the application so it affects user satisfaction. This study aims to find a

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Sari, Anggi Puspita, Budi Sukardi, and Muhammad Kurnia Rahman Abadi. "ADOPTION OF USER SATISFACTION WITH THE UTAUT2 MODEL IN USING INDONESIA SHARIA MOBILE BANKING." FINANSIA : Jurnal Akuntansi dan Perbankan Syariah 7, no. 1 (2024): 21. http://dx.doi.org/10.32332/finansia.v7i1.8165.

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Indonesia has a large number of internet users, which at the very least indicates its enormous digital potential. Banks and other financial institutions are benefiting from improved digital offerings. This study strengthens literacy and inclusion as an effective strategy for strengthening the effects of the integrated UTAUT2 construct model with user satisfaction using Islamic mobile banking applications. Quantitative research type, with a research sample of 206 respondents as the primary data source using a questionnaire survey. Data analysis using partial regression analysis (PLS-SEM). The r

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Junus, Ayub Dwi Pranata, Prijono Tjiptoherijanto, Nurdin Sobari, and Athor Subroto. "Indonesian seafarers in global job competition: Developing global work competencies in increasing end-user acceptance." Uncertain Supply Chain Management 12, no. 2 (2024): 815–28. http://dx.doi.org/10.5267/j.uscm.2024.1.005.

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The demand for global seafaring labor has also increased drastically because of a shortage of qualified seafarers. This study examined the effect of global employability competence and sensory, behavioral, and intellectual experience mediating the end-user acceptance of ship crew management. The study's sample consisted of 240 ship crew managers who employed Indonesian seafarers. The analysis was completed utilizing the Partial Least Square-Structural Equation Method. The results stated that global employability competence significantly influenced end-user acceptance, even though the sensory e

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Yoga Anunggita and Akmal Suryadi. "Analysis of User Acceptance of the Mobile Application of National Health Insurance Using the UTAUT Model." Journal La Multiapp 5, no. 5 (2024): 568–80. http://dx.doi.org/10.37899/journallamultiapp.v5i5.1463.

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Development technology in the field health own positive impact. On the field Health services are available developed mobile applications Forgive Health services to public by online. The Mobile JKN Application has objective For give service health to community and make it easier power health For give service optimally. On research This analyze about reception user The Mobile JKN application uses the Unified Theory of Acceptance and Use of Technology (UTAUT). Respondent data obtained during spread questionnaire that is as many as 110 respondents. Respondent data Then analyzed using Structural Eq

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Vitiello, Massimo, Simon Walk, Denis Helic, Vanessa Chang, and Christian Gütl. "User Behavioral Patterns and Early Dropouts Detection: Improved Users Profiling through Analysis of Successive Offering of MOOC." JUCS - Journal of Universal Computer Science 24, no. (8) (2018): 1131–50. https://doi.org/10.3217/jucs-024-08-1131.

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Massive Open Online Courses (MOOCs) are one of the fastest growing and most popular phenomena in e-learning. Universities around the world continue to invest to create and maintain these online courses. Reuse of material from previous courses is a shared practice that helps to reduce production costs and enhance future offerings. However, such re-runs still experience a high number of users not completing the courses, one of the most compelling issues of MOOCs. Hence, this research utilizes the information from the first run of a MOOC to predict the behavior of the users on a successive offeri

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Sathiyamoorthi V., T. Nadana Ravishankar, Ilavarasi A. K., et al. "Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications." International Journal of Information Systems in the Service Sector 13, no. 4 (2021): 40–61. http://dx.doi.org/10.4018/ijisss.2021100103.

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Reviewing and buying the right goods from online websites is growing day by day in today's fast internet environment. Numerous goods in the same label are available to consumers. It is thus a difficult job for consumers to pick up the correct commodity at a decent price under different market conditions. Therefore, it is important for owners of online shopping websites to better understand their customers' needs and offer better services. For these reasons, the access log documented a vast amount of data related to user interactions with the websites. This access log therefore plays a key role

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Guo, Yuanyuan, and Peng Dong. "Factors Influencing User Favorability of Government Chatbots on Digital Government Interaction Platforms across Different Scenarios." Journal of Theoretical and Applied Electronic Commerce Research 19, no. 2 (2024): 818–45. http://dx.doi.org/10.3390/jtaer19020043.

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This study investigates the direct and indirect influences of behavioral quality, social support, perceived system, emotional perception, and public expectation on user favorability regarding government chatbots in both government service and policy consultation contexts. The findings reveal that while behavioral quality, social support, and perceived system directly affect user favorability in both scenarios, public expectation uniquely impacts user favorability in policy consultation settings, but not in government service scenarios. Furthermore, the analysis indicates that social support, e

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Zabiniako, Vitaly, Toms Rožkalns, Erika Nazaruka, and Jurijs Kornienko. "Analysis of Algorithms for Detecting Users’ Behavioral Models based on Sessions Data." Complex Systems Informatics and Modeling Quarterly, no. 41 (December 31, 2024): 55–79. https://doi.org/10.7250/csimq.2024-41.04.

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Analysis of user behavior models based on user session data can be conducted using clustering (or community detecting) algorithms that do not require a predefined number of clusters. An unknown number and quality of potential behavioral models, non-efficient utilization of memory and processing units, and a large amount of data are the main difficulties developers could meet. Besides, the suitability of clustering algorithms for the task highly depends on the characteristics of datasets and still requires additional research. The first step and the goal of this research is to analyze the capab

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Nashivochnikov, Nikolay, and Valery Pustarnakov. "Topological Methods of Analysis in Behavioral Analytics Systems." Voprosy kiberbezopasnosti, no. 2(42) (2021): 26–36. http://dx.doi.org/10.21681/2311-3456-2021-2-26-36.

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Purpose of the article: development of a methodology for the application of methods for analyzing big data based on topological constructions in relation to behavioral analytics systems to ensure corporate and cyber-physical security. Method: the technique is based on the algebraic theory of persistent homology. Along with algebraic topology, embedology (Takens-Mane embedding theory) and the theory of metric spaces are used. Result: the necessary concepts of algebraic topology are given, which underlie the analysis of user / entity behavior profiles: Vietoris-Rips simplicial complex, filtering

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Assaf Arief, Achmad Fuad, Suyuti, and Suhartini. "Age-Dependent User Perception Analysis of Web Application Using Technology Acceptance Model Approach: A Case Study." Technium: Romanian Journal of Applied Sciences and Technology 17 (November 1, 2023): 95–99. http://dx.doi.org/10.47577/technium.v17i.10052.

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In the pursuit of technologically advanced governance, the Ministry ABC has integrated technology into daily operations, particularly through web applications like an employee administration information system. However, challenges persist in the implementation of online administration, especially in terms of technology adoption among the older generation. This study employs the Technology Acceptance Model (TAM) to investigate the usage of the administrative information system, focusing on System Quality (SQ), Perceived Ease of Use (EU), Perceived Usefulness (PU), and Behavioral Intention (BI).

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Cao, Jianfei, Hanlin Feng, Yeongjoo Lim, Kota Kodama, and Shuo Zhang. "How Social Influence Promotes the Adoption of Mobile Health among Young Adults in China: A Systematic Analysis of Trust, Health Consciousness, and User Experience." Behavioral Sciences 14, no. 6 (2024): 498. http://dx.doi.org/10.3390/bs14060498.

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As mobile health (mHealth) offers several advantages in healthcare, researchers are exploring the motivational factors for its adoption. However, few studies have elucidated the complex relationship between social influence and behavioral intentions to adopt mHealth among young adults in China. This study explored the impact of social influence on young adults’ behavioral intentions to adopt mHealth, the mediating roles of trust and health consciousness, and the moderating effect of mHealth user experience on the relationship between the predictors. In total, 300 valid responses were collected

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B.S.Mounika Yadav, Komali Guthikonda, and Venu Matta. "Cybersecurity Threat Analysis and Behaviour Analysis: A Comprehensive Approach to Proactive Défense." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 67–70. https://doi.org/10.46647/ijetms.2025.v09si01.010.

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The escalating complexity and frequency of cyber threats have exposed the limitations of traditional reactive cybersecurity measures, necessitating a shift toward proactive and predictive defence strategies. This research paper presents a comprehensive framework that integrates cybersecurity threat analysis and behavior analysis to address the dynamic and evolving nature of modern cyber risks. By combining advanced analytical techniques, such as time-series analysis and machine learning, with insights from behavioral psychology, this study aims to enhance threat detection, prediction, and miti

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N, Nitha, Rahana, Rahana,, Rizwan M, and Sajitha A S. "Behavioral based threat detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43633.

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Insider threats pose a significant risk to organizations as they exploit legitimate access to bypass traditional security measures, making them harder to detect than external attacks. This study addresses the challenge by utilizing deep learning to analyze user behavior and identify malicious activities through a carefully selected set of event- based features. By training on the CMU CERT r4.2 dataset, the proposed model effectively learns patterns of adversarial behavior, reducing false positives while maintaining high detection accuracy. The paper presents a deep learning-based approach for

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Khatana, Sweta, and Anurag Jain. "Malware Detection Using the Behavioral Analysis of the Web based Applications and User." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 1026–31. http://dx.doi.org/10.26438/ijcse/v7i5.10261031.

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Prof. Chethan Raj C, Abhishek V Dhapte and Namratha V. "Behavioral Analysis of User Data on Social Media Applications using Machine Learning Algorithms." International Journal for Modern Trends in Science and Technology 6, no. 8S (2020): 89–94. http://dx.doi.org/10.46501/ijmtstciet17.

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Trusov, Michael, Liye Ma, and Zainab Jamal. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting." Marketing Science 35, no. 3 (2016): 405–26. http://dx.doi.org/10.1287/mksc.2015.0956.

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Zhong, Hua, and Ming Jing. "Digital soft decoration display design microspecialty based on double layer clustering user behavior analysis algorithm." Intelligent Decision Technologies 19, no. 3 (2025): 1551–64. https://doi.org/10.1177/18724981251314270.

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Conventional approaches to multi-perspective data clustering generally presume that all samples across various perspectives have a complete set of features. Yet, in real-world scenarios, it's common for some perspectives to lack certain features, causing incomplete data sets. This research utilized behavioral data from 1000 users, including their purchase habits, browsing activities, preferred content, and feedback. The data spans six months and comprises 20,000 individual behavioral records. The K-means algorithm was employed to initial cluster user behaviors, categorizing the 1000 participan

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Aini, Nur, and Annisya Fitri Khairina Parinduri. "Influence of Perceived Usefulness, Ease of Use, User Satisfaction, and Security Against Intentional Behavior Using GoPay E-Wallet." Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis 12, no. 2 (2024): 171–80. https://doi.org/10.30871/jaemb.v12i2.9022.

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This research aims to analyze the population of Batam City, especially application-based GoPay e-wallet users from Gen Z and Millennials (20-30 years). This research was also conducted to complement research on behavioral intentions when using the GoPay e-wallet by developing replication of previous research. This research uses perceived usefulness, perceived ease of use, perceived user satisfaction, and perceived security as independent variables and behavioral intention as the dependent variable. The population of this study consisted of residents of the city of Batam and used a purposive sa

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Pisarovic, Ivo, David Prochazka, Dan Vybiral, and Jana Prochazkova. "Providing Customised Information Panel Content Based on User Behavioral Patterns." MENDEL 24, no. 1 (2018): 173–80. http://dx.doi.org/10.13164/mendel.2018.1.173.

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Although mobile applications are commonly using user location and behavior to provide relevant content, public information panels usually lack the ability to adjust the content for a particular user or a group of users. Therefore, we focused on the development of information panels that are able, in combination with a mobile application, to collect anonymous location data about the users, identify key behavioral patterns and provide content that is relevant for the users in the panel vicinity. The key property of our solution is the anonymity of the collected information and privacy in general

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Sánchez Cruz, Jennifer Lorena. "Machine learning-based predictive models for digital behavioral analysis." Data and Metadata 4 (June 10, 2025): 994. https://doi.org/10.56294/dm2025994.

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Introduction: The rise of digital technology use in Ecuador has produced large volumes of data on user behavior. In this context, machine learning models provide an effective way to analyze and predict digital behavior patterns, supporting informed decision-making in fields such as marketing, education, and public policy.Methods: A quantitative, non-experimental, cross-sectional methodology was used. A Random Forest model was applied to a simulated dataset based on parameters from the National Institute of Statistics and Censuses (INEC). The analysis focused on variables such as age, internet

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Fu, Bo, Ben Steichen, and Wenlu Zhang. "Towards Adaptive Ontology Visualization — Predicting User Success from Behavioral Data." International Journal of Semantic Computing 13, no. 04 (2019): 431–52. http://dx.doi.org/10.1142/s1793351x1940018x.

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Ontology visualization plays an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability studies of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavioral data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment, as opposed to direct input that informs the underlying visualization system. Although findings from usabili

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Satria, Teguh, and Achmad Nurmandi. "Behavioral Patterns of Social Media Users toward Policy: A Scientometric Analysis." Policy & Governance Review 8, no. 2 (2024): 192. http://dx.doi.org/10.30589/pgr.v8i2.902.

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This research explores the behavioural patterns of social media users in response to policies, bridging a gap in the literature by providing in-depth insights into the interaction between user behaviour and public policies relating to social media. An important contribution of this research lies in its holistic approach to understanding how social media users respond to various public policies and their impact on their behaviour. This research aims to explore the influence of the interaction between social media and public policy, understanding how user behaviour on these platforms affects res

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