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学者姓名：唐中君
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Abstract ：
The diversity and dynamics of quality index information bring challenges to quality assessment of experience products. This paper proposes a three-stage quality assessment method based on grounded theory, association analysis, combined weighting, sentiment analysis and cloud model. Firstly, based on online reviews, the true quality indicators of animation are recognized by grounded theory, and the relationships among quality indicators are identified by association analysis. Secondly, by introducing the principle of gaming, the combined weighting based on network opinion leader and network opinion follower is proposed. Finally, an animation comprehensive quality evaluation model based on cloud model and sentiment analysis is proposed. The feasibility and practicability of the method are verified on multiple animation datasets, and the quality levels of different sales products are obtained, providing a direction for animation quality improvement. Meanwhile, this paper further demonstrates the method's superiority by comparing it with other methods.
Keyword ：
Combined weighting Combined weighting Cloud model Cloud model Sentiment analysis Sentiment analysis Quality evaluation Quality evaluation Grounded theory Grounded theory
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| GB/T 7714 | Chen, Qianqian , Tang, Zhongjun , He, Duokui et al. A three-stage quality evaluation method for experience products: taking animation as an example [J]. | MULTIMEDIA SYSTEMS , 2024 , 30 (4) . |
| MLA | Chen, Qianqian et al. "A three-stage quality evaluation method for experience products: taking animation as an example" . | MULTIMEDIA SYSTEMS 30 . 4 (2024) . |
| APA | Chen, Qianqian , Tang, Zhongjun , He, Duokui , Zhao, Dongyuan , Wang, Jing . A three-stage quality evaluation method for experience products: taking animation as an example . | MULTIMEDIA SYSTEMS , 2024 , 30 (4) . |
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Abstract ：
In the dynamic entertainment industry, predicting a movie's opening box office revenue remains critical for filmmakers and studios. To address this challenge, we present a novel Cross-modal transformer and a Hierarchical Fusion Neural Network (CHFNN) model tailored to predict movie box office earnings based on multimodal features extracted from movie trailers, posters, and reviews. The Cross-modal Transformer component of the CHFNN model captures intricate inter-modal relationships by performing a cross-modal fusion of the extracted features. It employs self- attention mechanisms to dynamically weigh the importance of each modality's information. This allows the model to learn to focus on the most relevant information from trailers, posters, and reviews, adapting to the unique characteristics of each movie. The Hierarchical Fusion Neural Network within CHFNN further refines the fused features, enabling a deeper understanding of the inherent hierarchical structure of multimodal data. By hierarchically combining the cross- modal features, our model learns to capture both global and local interactions, enhancing its predictive capacity. We evaluate the performance of the CHFNN model on a comprehensive Internet Movie Dataset by obtaining metadata for 50,186 movies from the 1990s to 2022, which includes movie trailers, posters, and review data. Our results demonstrate that the CHFNN model outperforms existing models in prediction accuracy, achieving 95.80% prediction accuracy. The CHFNN model provides state-of-the-art predictive power and offers interpretability through attention mechanisms, allowing insights into the factors contributing to a movie's box office success.
Keyword ：
movie trailers movie trailers movie reviews movie reviews movie posters movie posters cross-modal transformers cross-modal transformers predictions predictions box-office box-office
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| GB/T 7714 | Madongo, Canaan T. , Tang, Zhongjun , Hassan, Jahanzeb . A Cross-Modal Transformer Based Model for Box-office Revenue Prediction [J]. | JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY , 2024 , 15 (7) : 822-837 . |
| MLA | Madongo, Canaan T. et al. "A Cross-Modal Transformer Based Model for Box-office Revenue Prediction" . | JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY 15 . 7 (2024) : 822-837 . |
| APA | Madongo, Canaan T. , Tang, Zhongjun , Hassan, Jahanzeb . A Cross-Modal Transformer Based Model for Box-office Revenue Prediction . | JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY , 2024 , 15 (7) , 822-837 . |
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Abstract ：
Forecasting opening box-office earnings has become an emerging demand, affecting filmmakers' financial decisions and promotional efforts by advertising studios that create trailers. Decision-makers have a complex and challenging task due to a large amount of data and several complex considerations. Based on deep multimodal visual features derived from trailer content and a cross-input neighborhood feature fusion, an innovative Deep Multimodal Predictive Cross-Input Neural Network model (DMPCNN) is proposed for predicting opening movie box-office revenue. DMPCNN is a fully-connected recurrent neural network with two architectures: A Visual Feature Extraction Model (ResNet+LSTM) block for extracting and learning mid-level temporal visual content and Cross-Input Neural Network fusion for uncovering and fusing high-level spatial features in trailers to predict movie revenue. The ResNet+LSTM block focuses on learning various trailer segments, while the Cross-Input Neural Network simultaneously learns and combines features from movie trailers and metadata and corresponding similarity metrics. DMPCNN aided in developing a decision support system that incorporates useful revenue-related trailer features. We evaluated DMPCNN's performance on the Internet Movie Dataset by obtaining metadata for 50,186 movies from the 1990s to 2022 and comparing it with different state-of-the-art frameworks. The erudite features in trailers and the predicted results outperformed baseline models, achieving 81% feature precision and 84.40% accuracy.
Keyword ：
cross-input neural network cross-input neural network recurrent neural networks recurrent neural networks movie trailers movie trailers multimodal features multimodal features long short-term memory long short-term memory box-office box-office
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| GB/T 7714 | Madongo, Canaan T. , Tang, Zhongjun , Hassan, Jahanzeb . Movie Box-Office Revenue Prediction Model by Mining Deep Features from Trailers Using Recurrent Neural Networks [J]. | JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY , 2024 , 15 (6) : 764-783 . |
| MLA | Madongo, Canaan T. et al. "Movie Box-Office Revenue Prediction Model by Mining Deep Features from Trailers Using Recurrent Neural Networks" . | JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY 15 . 6 (2024) : 764-783 . |
| APA | Madongo, Canaan T. , Tang, Zhongjun , Hassan, Jahanzeb . Movie Box-Office Revenue Prediction Model by Mining Deep Features from Trailers Using Recurrent Neural Networks . | JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY , 2024 , 15 (6) , 764-783 . |
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Abstract ：
Switching from current technology to disruptive technology entails advantages, sacrifices, and a variety of other considerations that are not encountered in the case of 5G technology. Therefore, this paper investigates consumers' intention to shift to 5G in the light of disruptive technology innovation. An extended value-based adoption model (VAM) was used to examine the user's switching intentions (SIs) from 4G (Existing) technology to 5G (Disruptive) technology. The proposed model was examined on data set from 361 Chinese users using the partial least squares-based structural equation modeling (PLS-SEM) technique. The outcomes illustrate the significant correlation between perceived switching benefits (PSBs) and perceived switching sacrifices (PSSs) on perceived sustainability values (SVs), which further impacts users' SIs. Furthermore, the findings show that users perceived agility (PA) and absorptive capability (AC) moderates and encourages SIs. These findings have important implications for theory and practice. The outcomes of this research will be helpful to telecoms firms in developing consumer retention strategies. Some limitations and future research directions are also explored for scholars.
Keyword ：
Sustainability values (SVs) Sustainability values (SVs) Switching intentions (SIs) Switching intentions (SIs) Value-based adoption model (VAM) Value-based adoption model (VAM) Disruptive 5G technology Disruptive 5G technology Perceived agility (PA) Perceived agility (PA) Absorptive capability (AC) Absorptive capability (AC)
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| GB/T 7714 | Tang Zhongjun , Shah, Sayed Kifayat , Ahmad, Mohammad et al. Modeling Consumer's Switching Intentions Regarding 5G Technology in China [J]. | INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT , 2022 . |
| MLA | Tang Zhongjun et al. "Modeling Consumer's Switching Intentions Regarding 5G Technology in China" . | INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT (2022) . |
| APA | Tang Zhongjun , Shah, Sayed Kifayat , Ahmad, Mohammad , Mustafa, Sohaib . Modeling Consumer's Switching Intentions Regarding 5G Technology in China . | INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT , 2022 . |
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Abstract ：
We build game theoretical models to investigate whether a multinational firm should open a live streaming shopping sales channel on an overseas e-commerce platform, where the multinational firm's overseas online retail division and third-party e-retailer exist, and if so, how to adjust its online channel structure to maximize its post-tax profit. We assume the headquarter of the multinational firm and e-commerce platform are located in countries with different corporate income tax rates. The multinational firm could sell products through its overseas online retail division (channel D), the third-party e-retailer (channel R) and live streaming shopping sales channel (channel L). We consider four possible channel structures on the e-commerce platform based on the three sales channels: Scenarios DR, DRL, DL and RL. We incorporate channel substitutability and tax difference into our models, and analyze the impact of opening a live streaming shopping sales channel on the operating decisions of supply chain members and supply chain performance, and derive the optimal online channel structure for the multinational firm. We show that, opening a live streaming shopping sales channel could benefit the multinational firm in both post-tax profit and sum of order/sales quantity, but it will hurt the profit of the multinational firm's overseas online retail division and third-party e-retailer. We further show that, the optimal online channel structure on the e-commerce platform for the multinational firm is Scenario DRL or Scenario DL, depending on the interaction of channel substitutability and tax difference. In addition, we analyze the impact of extra average operating cost of opening a new sales channel on the multinational firm's online channel structure. These insights may help multinational firms configure their online channel structures on e-commerce platforms when engaging in live streaming shopping.
Keyword ：
Tax difference Tax difference Channel substitutability Channel substitutability E -commerce E -commerce Online channel structure Online channel structure Live streaming shopping Live streaming shopping
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| GB/T 7714 | Zhang, Tao , Tang, Zhongjun , Han, Zhongya . Optimal online channel structure for multinational firms considering live streaming shopping [J]. | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2022 , 56 . |
| MLA | Zhang, Tao et al. "Optimal online channel structure for multinational firms considering live streaming shopping" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 56 (2022) . |
| APA | Zhang, Tao , Tang, Zhongjun , Han, Zhongya . Optimal online channel structure for multinational firms considering live streaming shopping . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2022 , 56 . |
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Abstract ：
Consumer innovativeness is a significant personality attribute that refers to a person's proclivity to acquire and utilize new items more rapidly and frequently than others. Although previous research has revealed a relationship between consumer innovativeness and the intention to buy new technology products, little is known about the determinants such as visibility and guidance affordances, environmental awareness, and safety concerns that underpin this relationship. Using the diffusion of innovation (DOI) theory through the PLS-SEM approach, this study analyzed the data of 341 Chinese consumers to explore the prospects mentioned above. The empirical results show that visibility and guidance affordances encourage consumer innovativeness. The results further reveal that environmental awareness and product safety concerns mediate the consumer innovativeness and purchase intention relationship. This model will contribute to the literature by improving predictive ability over previous models. Therefore, managers and policy-makers who wish to make constructive changes in the intentions of technology consumers are encouraged to ruminate on the extrapolations of this article.
Keyword ：
safety concerns safety concerns environmental awareness environmental awareness consumer's innovativeness consumer's innovativeness affordances affordances 5G Technology 5G Technology
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| GB/T 7714 | Shah, Sayed Kifayat , Tang, Zhongjun , Gavurova, Beata et al. Modeling consumer's innovativeness and purchase intention relationship regarding 5G technology in China [J]. | FRONTIERS IN ENVIRONMENTAL SCIENCE , 2022 , 10 . |
| MLA | Shah, Sayed Kifayat et al. "Modeling consumer's innovativeness and purchase intention relationship regarding 5G technology in China" . | FRONTIERS IN ENVIRONMENTAL SCIENCE 10 (2022) . |
| APA | Shah, Sayed Kifayat , Tang, Zhongjun , Gavurova, Beata , Olah, Judit , Acevedo-Duque, Angel . Modeling consumer's innovativeness and purchase intention relationship regarding 5G technology in China . | FRONTIERS IN ENVIRONMENTAL SCIENCE , 2022 , 10 . |
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Abstract ：
Companies use celebrity accounts on microblogs to promote product information. Predicting the popularity of product information before publication is important for celebrity account selection and microblog account management. Previous studies mainly considered the characteristics of information content, context, and information sources while ignoring the impact of the heterogeneity of user retweeting decision-making processes on the popularity of information. In this study, we analyze the retweeting decision processes of two types of users with different information sources and build a two-stage process model of product information diffusion. Based on this model, we explore the interest decline rate of users and propose two improved Bass models: the exponential- and power-function improved models. The experiment results and model comparisons show that the exponential-function improved model outperforms the Bass, Gompertz and power-function improved models, and is suitable for the pre-release prediction of product information popularity. The interest decline rate of users in retweeting product information follows an exponential function, and product information diffusion on microblogs is mainly driven by the celebrity effect. Our research reveals the mechanism of the interest attenuation effect, the celebrity effect, and product information quality affecting product information diffusion on microblogs, which can aid further research on product information popularity.
Keyword ：
Forecasting Forecasting Product information popularity Product information popularity Two-stage process model Two-stage process model Improved bass model Improved bass model
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| GB/T 7714 | Han, Zhongya , Tang, Zhongjun , He, Bo . Improved Bass model for predicting the popularity of product information posted on microblogs [J]. | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE , 2022 , 176 . |
| MLA | Han, Zhongya et al. "Improved Bass model for predicting the popularity of product information posted on microblogs" . | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 176 (2022) . |
| APA | Han, Zhongya , Tang, Zhongjun , He, Bo . Improved Bass model for predicting the popularity of product information posted on microblogs . | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE , 2022 , 176 . |
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Abstract ：
Purpose Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM). Design/methodology/approach Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017. Findings The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns - to predict TSV interval of a new EPSLC before production. Originality/value Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.
Keyword ：
Sales prediction Sales prediction Interval prediction Interval prediction Code category system Code category system Experiential product with short life cycle Experiential product with short life cycle Pattern network graph Pattern network graph Attribute relationship pattern Attribute relationship pattern
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| GB/T 7714 | Tang, Zhongjun , Wang, Tingting , Cui, Junfu et al. Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns [J]. | MANAGEMENT DECISION , 2021 , 59 (10) : 2528-2548 . |
| MLA | Tang, Zhongjun et al. "Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns" . | MANAGEMENT DECISION 59 . 10 (2021) : 2528-2548 . |
| APA | Tang, Zhongjun , Wang, Tingting , Cui, Junfu , Han, Zhongya , He, Bo . Predicting total sales volume interval of an experiential product with short life cycle before production: similarity comparison in attribute relationship patterns . | MANAGEMENT DECISION , 2021 , 59 (10) , 2528-2548 . |
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Abstract ：
为获得改进的分类算法BP_Adaboost,利用思维进化算法(MEA)和列文伯格-马夸尔特算法(LM)结合改进的BP神经网络作为弱分类器,由改进的弱分类器集成得到MEA-LM-BP_Adaboost算法.提出了基于MEA-LM-BP_Adaboost算法的首轮融资时总票房分类预测方法,该方法包括变量选取及操作化处理、网络参数优化、MEA改进弱分类器、LM算法改进弱分类器、MEA-LM-BP_Adaboost算法的流程设计、待预测电影验证6个部分.选用2013～2018年的245部国产电影作为样本验证该预测方法和模型,测试集分类准确率可达73.3％.最后在模型准确率、稳定性、K折交叉验证3方面进行模型整体性能比较,结果表明本文提出的模型整体性能最好.
Keyword ：
总票房分类预测 总票房分类预测 BP_Adaboost算法 BP_Adaboost算法 思维进化算法 思维进化算法 列文伯格-马夸尔特算法 列文伯格-马夸尔特算法
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| GB/T 7714 | 唐中君 , 王美月 , 周欣浩 et al. BP_Adaboost算法的改进及在首轮融资时总票房分类预测中的应用 [J]. | 科技促进发展 , 2021 , 17 (6) : 1158-1168 . |
| MLA | 唐中君 et al. "BP_Adaboost算法的改进及在首轮融资时总票房分类预测中的应用" . | 科技促进发展 17 . 6 (2021) : 1158-1168 . |
| APA | 唐中君 , 王美月 , 周欣浩 , 杨崇耀 . BP_Adaboost算法的改进及在首轮融资时总票房分类预测中的应用 . | 科技促进发展 , 2021 , 17 (6) , 1158-1168 . |
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Abstract ：
提出并验证考虑消费动机和动态竞争的电影日需求预测模型.考虑非粉丝及粉丝型的消费动机,构建电影消费两阶段过程模型;融合该模型和Bass模型,考虑竞争导致市场潜量的动态性,考虑映前被关注度、口碑、节假日对票房的影响,提出电影日需求预测模型.利用2016 ～2017年上映的电影数据验证该模型,并与Bass模型对比分析.结果 显示,该模型预测效果优于Bass模型.因考虑竞争导致的动态市场潜量,考虑粉丝型消费者由续集效应及改编效应导致的动态市场潜量提升,该模型能显著提高预测准确度.利用映前被关注度和电影口碑数据,该模型能实现映前及上映早期的预测.该模型可推广至存在消费动机不同、市场动态竞争的其它短生命周期体验品的需求预测,是对Bass模型的改进.
Keyword ：
消费动机 消费动机 动态竞争 动态竞争 电影日需求预测 电影日需求预测 两阶段过程模型 两阶段过程模型 Bass模型 Bass模型
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| GB/T 7714 | 唐中君 , 周亚丽 . 考虑消费动机和动态竞争的电影日需求预测模型 [J]. | 运筹与管理 , 2021 , 30 (6) : 172-180 . |
| MLA | 唐中君 et al. "考虑消费动机和动态竞争的电影日需求预测模型" . | 运筹与管理 30 . 6 (2021) : 172-180 . |
| APA | 唐中君 , 周亚丽 . 考虑消费动机和动态竞争的电影日需求预测模型 . | 运筹与管理 , 2021 , 30 (6) , 172-180 . |
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