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The (Un)Knowledgeable, the (Un)Skilled? Undertaking Chat-GPT Users’ Benefit-Risk-Coping Paradox in Higher Education Focusing on an Integrated, UTAUT and PMT

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International Journal of Human-Computer Interaction 2025年第9期41卷 5663-5693页

作者： Fu, Chung-Jen Silalahi, Andri Dayarana K. Huang, Shu-Chin Phuong, Do Thi Thanh Eunike, Ixora Javanisa Yu, Zhou-Houg Department of Business Administration College of Management Chaoyang University of Technology Taichung Taiwan Department of Marketing and Logistics Management College of Management Chaoyang University of Technology Taichung Taiwan Department of Distribution Management College of Management National Chin Yi University of Technology Taichung Taiwan Management Program Graduate School University of HKBP Nommensen Medan Indonesia Department of Leisure Recreation and Tourism Management College of Management Chaoyang University of Technology Taichung Taiwan

The advent of Chat-GPT, an AI-driven technology, is reshaping various sectors, particularly higher education. This study, merging the Unified Theory of Acceptance and Use of technology (UTAUT) with the Protection Motivation Theory (PMT), explores the relationship between technology adoption and protection motivation in higher education, addressing the concept of "the (un)knowledgeable, the (un)skilled." Through Structural Equation Modeling, findings from Indonesian participants (N = 445), including students and lecturers, reveal that perceived benefits such as task efficiency, hedonic motivation, performance expectancy, and effort expectancy predominantly motivate Chat-GPT use. Risk perception leans more on perceived vulnerability than severity, with insignificant impact from response cost. Response efficacy and self-efficacy significantly determine intention. Insight from moderating effect of educational level suggest that doctoral-level users demonstrate increased Chat-GPT usage, and gender plays a role in response efficacy and task efficiency. Both educational level and gender actively influence users’ intentions and actual usage behavior. These insights guide educators, policymakers, and institutions in ethically integrating AI, managing evolving risk perceptions, and empowering users in the dynamic AI landscape of higher education. Policymakers (e.g., university boards) are urged to dexterity an ethical framework and authorize in user efficacy to ensure equitable access and benefits from Chat-GPT in education. © 2024 Taylor & Francis Group, LLC.

关键词： Risk perception

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The Implementation of ISO 37301:2021 on Compliance management System (CMS) to Improve Product Quality for Customer Satisfaction 1

The Implementation of ISO 37301:2021 on Compliance Managemen...

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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Widodo, Rochmad Agung Suef, Mokh Affandi, Achmad Technology Management Doctoral Program Interdisciplinary Surabaya Indonesia

ISBN: (纸本)9798350365191

The purpose of this research is to guide the implementation of the International Organization for Standardization (ISO) 37301:2021 on Compliance management System (CMS) effectively through compliance contract management to improve the product quality of an Indonesian manufacture-company for achieving its consumer/customer's satisfaction. Compliance to contractual requirements to achieve customer satisfaction should not only rely per se on the contract requirements as stated in the contract, but also consider customer's concerns on product quality. It has become the finding of this academic research that by integrating the Kano Model into contractual compliance management in the Company, the Company's product quality improved and attracted its customer. Such improvement is resulted from the ability of the Company in its CMS strategy by interpreting the provisions of the contract within the perspective of Kano's satisfaction quadrants. © 2024 IEEE.

关键词： Sales

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Artificial intelligence self-efficacy: Scale development and validation

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Education and Information Technologies 2024年第4期29卷 4785-4808页

作者： Wang, Yu-Yin Chuang, Yu-Wei Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Bachelor Program in Business and Management National Yunlin University of Science and Technology Yunlin Taiwan

With the development of artificial intelligence (AI) applications, it has become critical for scholars, educators and practitioners to understand an individual’s perceived self-efficacy regarding the use of AI technologies/products. Understanding users’ subsequent behaviors toward the advancement of AI technology is also critical. Despite the growing focus on AI, a suitable scale for measuring AI self-efficacy (AISE) has yet to be developed. Current scales for measuring AISE (i.e., technology self-efficacy scales) are considered inapplicable because they neglect to evaluate perceptions of specific AI characteristics (e.g., AI-based configuration or anthropomorphic design). Given the limitations of existing self-evaluation and diagnostic instruments, the aim of this research is to investigate the construct of AISE, and develop and validate an AISE scale (AISES) for measuring an individual’s perceived self-efficacy in regard to the use of AI technologies/products, in accordance with established exploratory and confirmatory scale development procedures. Specifically, a literature review is employed to generate initial items. An exploratory factor analysis is then performed for item purification purposes. At this stage, potential elements of AISE are extracted. Subsequently, factor extraction and confirmatory factor analysis are used to verify the construct structure of AISE. An analysis of 314 responses indicates that the AISE construct contains four factors: assistance, anthropomorphic interaction, comfort with AI, and technological skills. The scale is comprised of 22 items, and is found to have good fit, reliability, convergent validity, discriminant validity, content validity, and criterion-related validity. Moreover, nomological validity is built by the positive correlation between the AISE construct and motivated learning behaviors. This paper is the pioneer in developing and validating a scale to measure AISE. The findings extend existing knowledge of AISE and c

关键词： Adoption Artificial intelligence Artificial intelligence self-efficacy Motivated learning behavior Scale development

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EVisionary: A Prediction Platform for Electric Vehicle Charging Capacity based on the Impact Analysis of Climate Factors 7

EVisionary: A Prediction Platform for Electric Vehicle Charg...

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7th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Li, David Chunhu Ku, Pei-Chun Tai, Wei Lan, Yu-Ching Kingsang, Joener Lusechemad Ming Chuan University Information Technology and Management Program Taoyuan City Taiwan

ISBN: (纸本)9798350339161

This paper presents EVisionary, a system platform that analyzes the impact of climate factors on electric vehicle charging demand and predicts the charging capacity of electric vehicles. The platform utilizes big data statistical analysis methods and implements seven machine learning and deep learning time series prediction models to forecast short-to-medium-term electric vehicles' charging demand. A case study of the electric vehicle charging data set in Dundee, Scotland is used to explore the relationship between climate factors and charging demand. Performance comparisons of the prediction models are conducted, and the platform enables users to set data analysis and forecast parameters for valuable analysis and forecast results. © 2024 IEEE.

关键词： Electric vehicles

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Recommendation of Resource Allocation Decision Based on Bipartite Graph Network Structure

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 128-139页

作者： Pei, Wen Pan, Wen-An Huang, Jui-Chan College of Management Chung Hua University Hsinchu30012 Taiwan Ph.D Program of Management Chung Hua University Hsinchu30012 Taiwan Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung807618 Taiwan

In resource allocation decisions in business, fully understanding customers’ needs and preferences helps to maximise benefits. As a result, in the modern business environment, the design of customized recommendation systems has gained a lot of attention. To this end, the study designs a recommendation algorithm for resource allocation decision based on improved two-part graph network structure. In this algorithm, an improved K-means clustering algorithm is introduced to deeply mine potential information. The calculation of similarity between users is also optimised to assist the target user to find the real neighbouring users. The findings demonstrate that, in comparison to the other algorithms, the resource allocation recommendation algorithm based on improved bipartite graph suggested in the study has a greater hit rate. The hit rate of the suggested algorithm can reach 32.5% when the recommendation list length is 10, which is a 21.5% improvement over the collaborative filtering algorithm. The suggested algorithm’s popularity is only 39.1, which is 69.3 less than the collaborative filtering algorithm when the suggestion list length is 10. Furthermore, the suggested algorithm for resource allocation decision-making created by the research has a greater recommendation accuracy, more personalization, and diversity, as seen by the proposed algorithm’s mean Hamming distance of 0.976. Through an improved bipartite graph network, the algorithm can fully analyze the historical preference information of users, effectively capture the complex relationship between users and products, generate personalized recommendation lists, and improve user satisfaction and purchase conversion rates. It provides an effective recommendation role for resource allocation decisions in modern business and helps to create greater economic benefits. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Collaborative filtering

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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Information and Public Opinion Analysis Platform Based on Social Media 2nd

Information and Public Opinion Analysis Platform Based on So...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Gao, Han-Ting Yang, Shang-Yi Chang, Chun-Han Kuo, Jung Chen, Guan-Yu Huang, Jheng-Jia Lo, Nai-Wei Management Undergraduate Program National Taiwan University of Science and Technology Taipei Taiwan Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9789819777853

In the modern age of rapid global information circulation, public opinion on social media reflects differences in people's opinions on a certain issue. These opinions can include emotions, likes and dislikes, etc. Once an incident happens, many social media gather a lot of discussions. Such situations let us to analyze the public's perception and preferences through public opinion analysis. Moreover, people cannot live without food, thus our team takes gourmet restaurants as an entry point to develop a public opinion analysis platform for gourmet restaurants. This study uses Python web crawler technology to capture data from public websites, such as PTT, Dcard, and Google Maps user comments. Then we extract public opinion data in the last year, clean and sort the data, and combine the sentiment analysis tool SnowNLP, analyze the positive and negative emotions of users’ comments in various restaurants. And we screen out the top popular restaurants in the form of scores, build a website entry function so that users can query and view the names, ratings of various restaurants, and emotional scores sorted out by our team. We hope this paper can be used as a reference to assist individual decision-making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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A Knowledge management Framework for Presales Operations in High-technology Companies

A Knowledge Management Framework for Presales Operations in ...

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Abdelaziz, Mohammed Khalil, Tarek Awny, Mohamed Mamdouh Nile University Management of Technology Program Sheikh Zayed Egypt

ISBN: (纸本)9781665463720

High-technology companies usually hire presales teams to help customers make purchase decisions. The presales team communicates with customers and proposes solutions meeting their needs. Accordingly, the presales team needs to develop the required technology and business knowledge to conduct the presales activities successfully. Presales teams with proper knowledge management can respond efficiently to customers. This paper aims to design a Knowledge management Framework for presales activities. To collect data, a literature review in addition to interviewing multiple presales employees was conducted by the author. The framework proposed by the author has five main components, namely the presales repository, stakeholders, knowledge management processes, technology infrastructure, and organizational support. Moreover, a road map for implementing the proposed framework is also presented. The significance of the framework has been addressed by discussing the perceived benefits versus the common challenges facing presales teams. The proposed framework and road map have been validated using expert judgment. © 2023 IEEE.

关键词： Knowledge management

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Data Visualization System to Examine the Market Price Trend of Agricultural Products 8

Data Visualization System to Examine the Market Price Trend ...

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8th International Conference on Information technology, InCIT 2024

作者： Maneerat, Paralee Chootongchai, Suparoek Sripatum University School of Information Technology Bangkok Thailand Chulalongkorn University Technology Management and Innopreneurship Program Bangkok Thailand

ISBN: (纸本)9798350366303

This study aimed to evaluate user satisfaction with a data visualization system designed to analyze agricultural product price trends for China trade. The system was developed using public data and Microsoft Power BI, employing the Extract, Transform, Load (ETL) process. By incorporating China's economic data and agricultural product prices, the system supports entrepreneurs in making informed trading decisions. The research assessed the system's effectiveness and usability, gauging its potential impact on China's agricultural trading market. This comprehensive approach to developing and evaluating a data visualization system for agricultural product price trends ultimately seeks to enhance trading efficiency with China. The study's findings provide valuable insights into the system's potential to improve decision-making processes in the context of China-focused agricultural trade. © 2024 IEEE.

关键词： Agricultural economics

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Efficient Utilization of Metaheuristic Techniques for Profit Maximization in Combinatorial Production Planning: A Comparative Study 3

Efficient Utilization of Metaheuristic Techniques for Profit...

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3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mukherjee, Arijit Mukherjee, Surojit Future Institute of Technology Deptartment of Electrical Engineering Kolkata India Great Lakes Institute of Management Post-Graduate Program in Management Chennai India

ISBN: (纸本)9798350313703

Efficient production planning requires well-structured models compatible with metaheuristic and other optimization techniques. This study compares three established metaheuristic methods - sanitized-teaching-learning-based optimization (s-TLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations - for profit-maximizing production planning. The focus is on a single-level problem involving complex constraints, non-linear costs, budget and raw material limitations. The paper presents two solution strategies: a penalty-based and a correction-based approach. Analyzing 300 unique instances of the problem reveals that s-TLBO implemented with correction-based approach consistently outperforms the other methods in devising optimal production plans for two Saudi Arabian petrochemical industries with 24 products, 54 processes, and three raw materials for plant 1 and two raw materials for plant 2. © 2024 IEEE.

关键词： Combinatorial optimization

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