This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a struct...This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment.Consequently,we discovered that“lack of reliability”and“poor customer service”tend to appear in negative reviews.Whereas,the terms“convenience,”“user-friendly interface,”“simple process,”and“secure system”tend to appear in positive reviews.On the basis of information system success theory,we categorized the topics“convenience,”“user-friendly interface,”and“simple process,”as system quality.In addition,“poor customer service”was categorized as service quality.Furthermore,based on the previous studies of trust and security,“lack of reliability”and“secure system”were categorized as trust and security,respectively.These outcomes indicate that users are satisfied when they perceive that system quality and security of specific mobile payments are great.On the contrary,users are dissatisfied when they feel that service quality and reliability of specific mobile payments is lacking.Overall,our research implies that a novel structural topic model is an effective method to explore mobile payment user experience.展开更多
[目的/意义]科研资助是科学研究工作中有效的激励政策,分析与揭示科研资助影响作用,对促进国家科技发展具有重要的积极作用。[方法/过程]基于Web of Science收录的我国计算机与人工智能领域的科研论文,按照有/无科研资助对其进行划分,...[目的/意义]科研资助是科学研究工作中有效的激励政策,分析与揭示科研资助影响作用,对促进国家科技发展具有重要的积极作用。[方法/过程]基于Web of Science收录的我国计算机与人工智能领域的科研论文,按照有/无科研资助对其进行划分,使用结构主题模型(STM)重点将科研资助对主题内容与主题契合度的影响进行分析。[结果/结论]研究发现,科研资助能够有效促进科技成果数量的增加,科研资助能够影响主题偏好和具体主题内容,科研资助与时间的交互作用会对主题的契合度产生积极的影响。展开更多
Purpose-This study aims to reveal the technological development trajectories within the fuzzy field,addressing the long-standing gap between the extensive application of fuzzy technologies and the lack of a comprehens...Purpose-This study aims to reveal the technological development trajectories within the fuzzy field,addressing the long-standing gap between the extensive application of fuzzy technologies and the lack of a comprehensive,data-driven understanding of their developmental logic and evolution.Design/methodology/approach-An integrated methodological framework combining structural topic modeling(STM)and main path analysis(MPA)is developed.A total of 43,905 patents related to fuzzy technologies were collected from the Derwent Innovation Index.STM is applied to identify 12 representative topics within the fuzzy technology field,followed by the construction of topic-specific citation networks.MPA is then used to extract the core development paths across these topics,capturing structural dynamics and knowledge diffusion.This integration enables a multidimensional exploration of topic structures and technology trajectories,providing a frame of reference for analyzing other emerging technologies.Findings-The combined STM-MPA approach effectively identifies the classification and developmental trajectories of fuzzy-related technologies.Results highlight topic-specific knowledge flows and inter-topic linkages,offering new insights into the internal evolution and external integration of fuzzy technologies.The study demonstrates how different subfields of fuzzy technologies have progressed and interacted over time.Originality/value-This study is among the first to systematically explore the development of fuzzy technologies using large-scale patent data and a hybrid analytical framework.By integrating topic modeling with citation analysis,it captures both topic patterns and development paths.The approach enhances existing methods in technology analysis and offers new insights for innovation research,policy design and enterprise strategy.展开更多
The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this...The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many authors have so far concentrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial probit estimation by using an adaptation of the diagonal orthant multinomial probit in the topic models context, resulting in the ability of our topic modeling scheme to handle corpuses with a large number of latent topics. An additional and very important benefit of our method lies in the fact that unlike with the logistic normal model whose non-conjugacy leads to the need for sophisticated sampling schemes, our approach exploits the natural conjugacy inherent in the auxiliary formulation of the probit model to achieve greater simplicity. The application of our proposed scheme to a well-known Associated Press corpus not only helps discover a large number of meaningful topics but also reveals the capturing of compellingly intuitive correlations among certain topics. Besides, our proposed approach lends itself to even further scalability thanks to various existing high performance algorithms and architectures capable of handling millions of documents.展开更多
基金This work was supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(NRF-2020R1A2C1014957).
文摘This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment.Consequently,we discovered that“lack of reliability”and“poor customer service”tend to appear in negative reviews.Whereas,the terms“convenience,”“user-friendly interface,”“simple process,”and“secure system”tend to appear in positive reviews.On the basis of information system success theory,we categorized the topics“convenience,”“user-friendly interface,”and“simple process,”as system quality.In addition,“poor customer service”was categorized as service quality.Furthermore,based on the previous studies of trust and security,“lack of reliability”and“secure system”were categorized as trust and security,respectively.These outcomes indicate that users are satisfied when they perceive that system quality and security of specific mobile payments are great.On the contrary,users are dissatisfied when they feel that service quality and reliability of specific mobile payments is lacking.Overall,our research implies that a novel structural topic model is an effective method to explore mobile payment user experience.
文摘[目的/意义]科研资助是科学研究工作中有效的激励政策,分析与揭示科研资助影响作用,对促进国家科技发展具有重要的积极作用。[方法/过程]基于Web of Science收录的我国计算机与人工智能领域的科研论文,按照有/无科研资助对其进行划分,使用结构主题模型(STM)重点将科研资助对主题内容与主题契合度的影响进行分析。[结果/结论]研究发现,科研资助能够有效促进科技成果数量的增加,科研资助能够影响主题偏好和具体主题内容,科研资助与时间的交互作用会对主题的契合度产生积极的影响。
基金supported by the National Statistical Science Research Project of China(No.2024LY021).
文摘Purpose-This study aims to reveal the technological development trajectories within the fuzzy field,addressing the long-standing gap between the extensive application of fuzzy technologies and the lack of a comprehensive,data-driven understanding of their developmental logic and evolution.Design/methodology/approach-An integrated methodological framework combining structural topic modeling(STM)and main path analysis(MPA)is developed.A total of 43,905 patents related to fuzzy technologies were collected from the Derwent Innovation Index.STM is applied to identify 12 representative topics within the fuzzy technology field,followed by the construction of topic-specific citation networks.MPA is then used to extract the core development paths across these topics,capturing structural dynamics and knowledge diffusion.This integration enables a multidimensional exploration of topic structures and technology trajectories,providing a frame of reference for analyzing other emerging technologies.Findings-The combined STM-MPA approach effectively identifies the classification and developmental trajectories of fuzzy-related technologies.Results highlight topic-specific knowledge flows and inter-topic linkages,offering new insights into the internal evolution and external integration of fuzzy technologies.The study demonstrates how different subfields of fuzzy technologies have progressed and interacted over time.Originality/value-This study is among the first to systematically explore the development of fuzzy technologies using large-scale patent data and a hybrid analytical framework.By integrating topic modeling with citation analysis,it captures both topic patterns and development paths.The approach enhances existing methods in technology analysis and offers new insights for innovation research,policy design and enterprise strategy.
文摘The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many authors have so far concentrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces. We herein circumvent the inefficiency of multinomial probit estimation by using an adaptation of the diagonal orthant multinomial probit in the topic models context, resulting in the ability of our topic modeling scheme to handle corpuses with a large number of latent topics. An additional and very important benefit of our method lies in the fact that unlike with the logistic normal model whose non-conjugacy leads to the need for sophisticated sampling schemes, our approach exploits the natural conjugacy inherent in the auxiliary formulation of the probit model to achieve greater simplicity. The application of our proposed scheme to a well-known Associated Press corpus not only helps discover a large number of meaningful topics but also reveals the capturing of compellingly intuitive correlations among certain topics. Besides, our proposed approach lends itself to even further scalability thanks to various existing high performance algorithms and architectures capable of handling millions of documents.
