Review
Computer Science, Information Systems
Huanhuan Cao, Jinhu Jiang, Xianjun Geng
Summary: This study developed a game-theory model to explore the interactions between online reviews and free version strategies. The results showed that firms can adopt four different strategies to respond to the availability of online reviews, depending on the relative strength of network effect, cannibalization effect, and dispersed effect. Furthermore, the study found that even when offering a free version is the best strategy, online reviews and the free version strategy can be both complementary and substitutive.
INFORMATION & MANAGEMENT
(2022)
Article
Business
Ina Garnefeld, Tabea Krah, Eva Boehm, Dwayne D. Gremler
Summary: Online reviews have significant impacts on firm success, but product testing programs may not necessarily lead to higher quality reviews or better product ratings. Offering product testing programs only benefits the firm in certain circumstances.
JOURNAL OF THE ACADEMY OF MARKETING SCIENCE
(2021)
Review
Green & Sustainable Science & Technology
Yingxue Xia, Hong-Youl Ha
Summary: This study investigates the effects of online reviews on customer satisfaction, trust, and intent to write a review during restaurant revisit stages. The findings suggest that the impact of online reviews diminishes over time and the relationship between trust and intent to write a review shows a mixed pattern. The study also provides guidelines for improving theoretical and practical insights in the field of consumption experience stages.
Article
Management
Dongwook Shin, Stefano Vaccari, Assaf Zeevi
Summary: This paper investigates how the pricing policy of a revenue-maximizing monopolist is influenced by the social learning dynamics of customers who use online reviews to estimate the quality of the product. The study formulates the problem using two different review models and derives a fluid model for the system dynamics. The paper provides key structural insights into the interactions between optimal pricing policies and review dynamics.
MANAGEMENT SCIENCE
(2022)
Article
Psychology, Mathematical
Felix Henninger, Yury Shevchenko, Ulf K. Mertens, Pascal J. Kieslich, Benjamin E. Hilbig
Summary: lab.js is a free, open-source experiment builder that allows researchers to easily build studies for both online and laboratory data collection through a visual interface without programming. It provides high accuracy and precision in measuring presentation and response times, while also allowing customization of study appearance and behavior using html, css, and JavaScript code if needed. Experiments constructed with lab.js can be run locally or published online with ease, integrating with popular data collection platforms for transparent replications and facilitating open, cumulative science.
BEHAVIOR RESEARCH METHODS
(2022)
Review
Business
Fang Wang, Zhao Du, Shan Wang
Summary: Online customer reviews have multiple dimensions of information, including sensory, cognitive, affective, and social information. This research examines the importance of these dimensions by analyzing their diagnostic value to prospective customers. An empirical study on Amazon reviews confirms the significant effects of all four dimensions on the diagnostic value, which are contingent on contextual conditions. This study provides insights into understanding customers' pre-purchase information needs and experience journeys by understanding the rich content in online reviews and other electronic word-of-mouth content.
JOURNAL OF BUSINESS RESEARCH
(2023)
Article
Computer Science, Software Engineering
Sihan Xu, Ya Gao, Lingling Fan, Zheli Liu, Yang Liu, Hua Ji
Summary: Open-source software licenses determine the conditions for reusing, distributing, and modifying software. Custom licenses allow developers to create their own licenses with more flexible descriptions. To avoid financial and legal risks, it is crucial to ensure license compatibility when using third-party packages or code with licenses. LiDetector, a proposed tool, can extract and interpret OSS licenses, including custom licenses, and detect license incompatibility. It outperforms existing methods in terms of precision and accuracy, and reveals a high percentage of projects suffering from license incompatibility.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2023)
Article
Computer Science, Information Systems
Mengsi Cai, Yuejin Tan, Bingfeng Ge, Yajie Dou, Ge Huang, Yonghao Du
Summary: The analysis of user requirements is crucial for product evolution and customization, a proposed approach can automatically extract, classify, and rank product requirements from online reviews, and identify differences among users.
IEEE SYSTEMS JOURNAL
(2022)
Article
Computer Science, Software Engineering
Jesus M. Gonzalez-Barahona, Sergio Montes-Leon, Gregorio Robles, Stefano Zacchiroli
Summary: This study aims to compile a dataset containing as many documents as possible that contain the text of software licenses or references to the license terms, and characterize the dataset for further research or practical purposes related to license analysis.
EMPIRICAL SOFTWARE ENGINEERING
(2023)
Review
Business
Bohao Ma, Yiik Diew Wong, Chee-Chong Teo, Ziyan Wang
Summary: This research uses large-scale user-generated content to decipher consumers' quality perceptions in the online food delivery sector. By applying machine learning algorithms, key service topics related to the consumer experience are identified. Ultimately, our findings provide crucial theoretical and practical implications for practitioners and researchers in this field.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2024)
Review
Computer Science, Interdisciplinary Applications
Yao Zhang, Cui Zhao, Zhe Liang
Summary: The study shows that in a competitive e-market, retailers' price and frill decisions are influenced by online reviews. Offering frills to consumers can increase profits when there is a small quality difference and low frill effectiveness. However, in a competitive context, retailers may focus more on promoting superior products rather than offering more frills.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Review
Management
Efthymia Symitsi, Panagiotis Stamolampros, George Daskalakis, Nikolaos Korfiatis
Summary: This study investigates the informational value of online reviews posted by employees, discovering that employee online reviews provide incremental predictive gains for decision support systems' information value and competitive advantage for firms and managers.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Review
Computer Science, Artificial Intelligence
David M. Goldberg, Alan S. Abrahams
Summary: In recent years, online reviews have become an important way for consumers to express their opinions and feedback. However, the unstructured and voluminous nature of textual data makes it challenging for companies to effectively utilize this feedback. This study proposes a method for prioritizing online reviews by using text mining tools, focusing on identifying the most useful reviews pertaining to innovation opportunities for firms. The results demonstrate the effectiveness of the proposed technique in improving upon existing methods, and senior managers at a large manufacturing firm also validate the usefulness of the selected attribute types in online reviews.
DECISION SUPPORT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Min Zhang, Brandon Fan, Ning Zhang, Wenjun Wang, Weiguo Fan
Summary: Online customer reviews play a crucial role in product innovation, but current research lacks focus on extracting innovation ideas from reviews. This study introduces a deep learning-based approach that effectively identifies sentences containing innovation ideas from online reviews.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Business
Maria Petrescu, Haya Ajjan, Dana L. Harrison
Summary: This study aims to analyze methods for identifying deceptive online consumer reviews, evaluate insights from automated approaches using individual and aggregated review data, and formulate a review interpretation framework for deception detection. The findings demonstrate the interchangeable characteristics of different automated text analysis methods and highlight their complementary aspects. Employing an integrative multi-method model at both the individual and aggregate level provides more comprehensive insights regarding review information, sentiment, relevance, context, and cognitive aspects.
JOURNAL OF BUSINESS RESEARCH
(2023)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)
