A trademark is an essential symbol of a company, consisting of a semantically rich image under ordinary circumstances. The popularity of a company can be measured by the frequency of its trademark being used. Therefore, efficiently retrieving trademark images would directly contribute to the detection of popular companies. However, most mainstream retrieval methods are not especially pertinent to trademark image retrieval. To solve this problem, a combination of the ResNet50 network and Autoencoder with local sensitive hashing (LSH) is used to conduct full cross-checking, which significantly improves the effectiveness of trademark image retrieval. Meanwhile, image super-resolution-based sparse coding is also proposed to achieve high-precision trademark image retrieval and its effect is particularly significant for challenging trademark images. Finally, the authors conduct extensive experiments on a high-quality database to demonstrate the substantial effectiveness of the proposed methods.
{"title":"Cross-Checking-Based Trademark Image Retrieval for Hot Company Detection","authors":"Hao Wu, Zhiyi Zhang, Zhilin Zhu","doi":"10.4018/joeuc.335455","DOIUrl":"https://doi.org/10.4018/joeuc.335455","url":null,"abstract":"A trademark is an essential symbol of a company, consisting of a semantically rich image under ordinary circumstances. The popularity of a company can be measured by the frequency of its trademark being used. Therefore, efficiently retrieving trademark images would directly contribute to the detection of popular companies. However, most mainstream retrieval methods are not especially pertinent to trademark image retrieval. To solve this problem, a combination of the ResNet50 network and Autoencoder with local sensitive hashing (LSH) is used to conduct full cross-checking, which significantly improves the effectiveness of trademark image retrieval. Meanwhile, image super-resolution-based sparse coding is also proposed to achieve high-precision trademark image retrieval and its effect is particularly significant for challenging trademark images. Finally, the authors conduct extensive experiments on a high-quality database to demonstrate the substantial effectiveness of the proposed methods.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":" 17","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a deep learning-based analytical model to conduct an in-depth study of the relationship between consumer trust, perceived benefits, and purchase intention. This model combines natural language processing and sentiment analysis, using the BERT-LSTNet-Softmax model to extract textual features in reviews and perform temporal predictions of consumer sentiment and purchase intention. Experimental results show that this model achieves excellent performance in the e-commerce field and provides a powerful tool for in-depth understanding of consumer purchasing decisions. This research promotes the application of deep learning technology in the field of e-commerce, helps to improve the accuracy of consumer purchase intentions, and provides more support for the development of the e-commerce market and consumer decision-making.
{"title":"E-Commerce Review Sentiment Analysis and Purchase Intention Prediction Based on Deep Learning Technology","authors":"Xiaoye Ma, Yanyan Li, Muhammad Asif","doi":"10.4018/joeuc.335122","DOIUrl":"https://doi.org/10.4018/joeuc.335122","url":null,"abstract":"This study proposes a deep learning-based analytical model to conduct an in-depth study of the relationship between consumer trust, perceived benefits, and purchase intention. This model combines natural language processing and sentiment analysis, using the BERT-LSTNet-Softmax model to extract textual features in reviews and perform temporal predictions of consumer sentiment and purchase intention. Experimental results show that this model achieves excellent performance in the e-commerce field and provides a powerful tool for in-depth understanding of consumer purchasing decisions. This research promotes the application of deep learning technology in the field of e-commerce, helps to improve the accuracy of consumer purchase intentions, and provides more support for the development of the e-commerce market and consumer decision-making.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"47 S222","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peijin Li, Xinyi Peng, Chonghui Zhang, T. Baležentis
When compared to traditional indicators, text information can capture market sentiment, investor confidence, and public opinion more effectively. Meanwhile, the mixed-frequency dynamic factor model (MF-DFM) can capture current changes. In this study, the authors constructed a financial cycle measurement and nowcasting framework by incorporating text information into factors derived from MF-DFM. The findings reveal that, first, the financial cycle indicator (FCI) provides a more detailed and forward-looking perspective on major events. Second, it can serve as an effective “early warning system” by cross-referencing economic indicators. Third, financial cycles exhibit five short cycles, with contraction periods being longer than expansion phases and expansion amplitudes surpassing contractions. Lastly, the analysis suggests a potential turning point in the second half of 2023. This research represents a valuable attempt to integrate big data for more sensitive, timely, and accurate monitoring of financial dynamics.
{"title":"Financial Cycle With Text Information Embedding Based on LDA Measurement and Nowcasting","authors":"Peijin Li, Xinyi Peng, Chonghui Zhang, T. Baležentis","doi":"10.4018/joeuc.335082","DOIUrl":"https://doi.org/10.4018/joeuc.335082","url":null,"abstract":"When compared to traditional indicators, text information can capture market sentiment, investor confidence, and public opinion more effectively. Meanwhile, the mixed-frequency dynamic factor model (MF-DFM) can capture current changes. In this study, the authors constructed a financial cycle measurement and nowcasting framework by incorporating text information into factors derived from MF-DFM. The findings reveal that, first, the financial cycle indicator (FCI) provides a more detailed and forward-looking perspective on major events. Second, it can serve as an effective “early warning system” by cross-referencing economic indicators. Third, financial cycles exhibit five short cycles, with contraction periods being longer than expansion phases and expansion amplitudes surpassing contractions. Lastly, the analysis suggests a potential turning point in the second half of 2023. This research represents a valuable attempt to integrate big data for more sensitive, timely, and accurate monitoring of financial dynamics.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"6 14","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Innovation management involves planning, organizing, and controlling innovation within an organization, while venture capital evaluation assesses investment opportunities in startups and early-stage companies. Both fields require effective decision-making and data analysis. This study aims to enhance innovation management and venture capital evaluation by combining CNN and GRU using deep learning. The approach consists of two steps. First, the authors build a deep learning model that fuses CNN and GRU to analyze diverse data sources like text, finance, market trends, and social media sentiment. Second, they optimize the model using the gorilla troop optimization (GTO) algorithm, inspired by gorilla behavior. GTO efficiently explores the solution space to find optimal or near-optimal solutions. The authors compare the fused CNN-GRU model with traditional methods and evaluate the GTO algorithm's performance. The results demonstrate improvements in innovation management and venture capital evaluation.
{"title":"Enhancing Innovation Management and Venture Capital Evaluation via Advanced Deep Learning Techniques","authors":"Quan Chen, Baoli Lu","doi":"10.4018/joeuc.335081","DOIUrl":"https://doi.org/10.4018/joeuc.335081","url":null,"abstract":"Innovation management involves planning, organizing, and controlling innovation within an organization, while venture capital evaluation assesses investment opportunities in startups and early-stage companies. Both fields require effective decision-making and data analysis. This study aims to enhance innovation management and venture capital evaluation by combining CNN and GRU using deep learning. The approach consists of two steps. First, the authors build a deep learning model that fuses CNN and GRU to analyze diverse data sources like text, finance, market trends, and social media sentiment. Second, they optimize the model using the gorilla troop optimization (GTO) algorithm, inspired by gorilla behavior. GTO efficiently explores the solution space to find optimal or near-optimal solutions. The authors compare the fused CNN-GRU model with traditional methods and evaluate the GTO algorithm's performance. The results demonstrate improvements in innovation management and venture capital evaluation.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"87 12","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weihui Han, Tianshuo Zhang, Jamal Khan, Lujian Wang, Chao Tu
This study investigates the effect of digital finance on Chinese OFDI using Probit and Logit models on A-share-listed Chinese enterprises and representative OFDI data from 2011 to 2020. It shows that digital finance has a heterogeneous impact on Chinese OFDI both in probability and scale depending on the enterprise digitalization level. That is, digital finance has a positive (negative) effect on the OFDI of high (low) digital enterprises. Mechanism analysis reveals that the digital divide, which causes credit resources to be squeezed and increased financing constraints for these enterprises, is the main cause of the negative impact of digital finance on the OFDI of low-digital enterprises while the negative impact of digital finance on the OFDI of low-digital enterprises is limited to greenfield investments and highly competitive industries. The findings highlight the importance of encouraging enterprise digital transformation when developing digital finance policies to effectively leverage the potential of digital finance to drive Chinese firms' OFDI.
{"title":"Going Global in the Digital Era","authors":"Weihui Han, Tianshuo Zhang, Jamal Khan, Lujian Wang, Chao Tu","doi":"10.4018/joeuc.334707","DOIUrl":"https://doi.org/10.4018/joeuc.334707","url":null,"abstract":"This study investigates the effect of digital finance on Chinese OFDI using Probit and Logit models on A-share-listed Chinese enterprises and representative OFDI data from 2011 to 2020. It shows that digital finance has a heterogeneous impact on Chinese OFDI both in probability and scale depending on the enterprise digitalization level. That is, digital finance has a positive (negative) effect on the OFDI of high (low) digital enterprises. Mechanism analysis reveals that the digital divide, which causes credit resources to be squeezed and increased financing constraints for these enterprises, is the main cause of the negative impact of digital finance on the OFDI of low-digital enterprises while the negative impact of digital finance on the OFDI of low-digital enterprises is limited to greenfield investments and highly competitive industries. The findings highlight the importance of encouraging enterprise digital transformation when developing digital finance policies to effectively leverage the potential of digital finance to drive Chinese firms' OFDI.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"5 16","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of artificial intelligence and deep learning, image-text matching has gradually become an important research topic in cross-modal fields. Achieving correct image-text matching requires a strong understanding of the correspondence between visual and textual information. In recent years, deep learning-based image-text matching methods have achieved significant success. However, image-text matching requires a deep understanding of intra-modal information and the exploration of fine-grained alignment between image regions and textual words. How to integrate these two aspects into a single model remains a challenge. Additionally, reducing the internal complexity of the model and effectively constructing and utilizing prior knowledge are also areas worth exploring, therefore addressing the issues of excessive computational complexity in existing fine-grained matching methods and the lack of multi-perspective matching.
{"title":"An Image-Text Matching Method for Multi-Modal Robots","authors":"Ke Zheng, Zhou Li","doi":"10.4018/joeuc.334701","DOIUrl":"https://doi.org/10.4018/joeuc.334701","url":null,"abstract":"With the rapid development of artificial intelligence and deep learning, image-text matching has gradually become an important research topic in cross-modal fields. Achieving correct image-text matching requires a strong understanding of the correspondence between visual and textual information. In recent years, deep learning-based image-text matching methods have achieved significant success. However, image-text matching requires a deep understanding of intra-modal information and the exploration of fine-grained alignment between image regions and textual words. How to integrate these two aspects into a single model remains a challenge. Additionally, reducing the internal complexity of the model and effectively constructing and utilizing prior knowledge are also areas worth exploring, therefore addressing the issues of excessive computational complexity in existing fine-grained matching methods and the lack of multi-perspective matching.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"40 28","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138588547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Green supply chain management is crucial for sustainable enterprises. Achieving it hinges on creating a greener supply chain through AI-driven data analysis. This enables precise market alignment, optimized management, and sustainable development. This study explores the link between digital transformation and green supply chain management. It leverages AI, specifically the XGBoost algorithm, to gauge sample contributions to market demand. It extracts multi-dimensional features in green supply chain management using NSCNN and CSCNN, combining them with the Stacking ensemble learning algorithm to form a new predictive model. This model, SNN-Stacking ensemble learning, outperforms traditional models, aiding resource planning, enhancing supply chain transparency, and promoting sustainable development by reducing environmental risks and resource waste. This research underscores the potential of digital technology in green supply chain management.
{"title":"Predicting Green Supply Chain Impact With SNN-Stacking Model in Digital Transformation Context","authors":"Te Li, Praveen Kumar Donta","doi":"10.4018/joeuc.334109","DOIUrl":"https://doi.org/10.4018/joeuc.334109","url":null,"abstract":"Green supply chain management is crucial for sustainable enterprises. Achieving it hinges on creating a greener supply chain through AI-driven data analysis. This enables precise market alignment, optimized management, and sustainable development. This study explores the link between digital transformation and green supply chain management. It leverages AI, specifically the XGBoost algorithm, to gauge sample contributions to market demand. It extracts multi-dimensional features in green supply chain management using NSCNN and CSCNN, combining them with the Stacking ensemble learning algorithm to form a new predictive model. This model, SNN-Stacking ensemble learning, outperforms traditional models, aiding resource planning, enhancing supply chain transparency, and promoting sustainable development by reducing environmental risks and resource waste. This research underscores the potential of digital technology in green supply chain management.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"42 11 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139219477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qingjin Wang, Yang Gao, Qinqin Cao, Zhengrui Li, Ruohan Wang
The digital transformation of small and medium-sized enterprises (SMEs) is an important component of China's digital construction. Although previous studies have made significant progress in examining the effects of SME digital transformation on efficiency and performance improvement, they have overlooked the exploration of its antecedents, especially the unclear role of the comprehensive impact of various antecedents. Based on the TOE framework, this study conducted antecedent configuration analysis through a questionnaire survey of 172 SMEs and research methods such as fsQCA and NCA. The authors found that: (1) The success of SME digital transformation is not driven by a single factor, but rather the result of the adaptive linkage of various antecedent variables, which has the characteristic of multiple concurrencies, and (2) there are 11 configurations of conditions for SME digital transformation, which can be classified into four paths. This study enriches the research on antecedent factors in SME digital transformation and provides references for SMEs' digital transformation practice.
{"title":"What Kind of Configuration Can Facilitate the Digital Transformation?","authors":"Qingjin Wang, Yang Gao, Qinqin Cao, Zhengrui Li, Ruohan Wang","doi":"10.4018/joeuc.334110","DOIUrl":"https://doi.org/10.4018/joeuc.334110","url":null,"abstract":"The digital transformation of small and medium-sized enterprises (SMEs) is an important component of China's digital construction. Although previous studies have made significant progress in examining the effects of SME digital transformation on efficiency and performance improvement, they have overlooked the exploration of its antecedents, especially the unclear role of the comprehensive impact of various antecedents. Based on the TOE framework, this study conducted antecedent configuration analysis through a questionnaire survey of 172 SMEs and research methods such as fsQCA and NCA. The authors found that: (1) The success of SME digital transformation is not driven by a single factor, but rather the result of the adaptive linkage of various antecedent variables, which has the characteristic of multiple concurrencies, and (2) there are 11 configurations of conditions for SME digital transformation, which can be classified into four paths. This study enriches the research on antecedent factors in SME digital transformation and provides references for SMEs' digital transformation practice.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"46 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139217701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To promote the market substitution of digital innovation technology and green products and alleviate the pressure of energy and environment, a three-party evolutionary game model of innovation diffusion based on core manufacturers, affiliated enterprises and the government is established in the article to explore the adjustment of the three-party game strategy of core manufacturers and affiliated enterprises under government supervision, and then analyze the optimal stability conditions that are conducive to the innovation diffusion of digital innovation technology. It is found that the core manufacturers would choose the diffusion strategy of digital innovation technology, which would take the initiative to adopt the diffused digital innovation technology due to cost considerations, without easily choosing independent research and development (hereafter R&D strategy); the reward and punishment mechanism made by the government for core manufacturers' digital innovation technology diffusion could effectively promote technology diffusion in the market.
{"title":"Analysis of Enterprise Data-Driven Innovation Diffusion Supervision System Based on the Perspective of Green Supply Chain","authors":"Wenjun Pan, Lin Miao, Zhenxing Lin","doi":"10.4018/joeuc.333894","DOIUrl":"https://doi.org/10.4018/joeuc.333894","url":null,"abstract":"To promote the market substitution of digital innovation technology and green products and alleviate the pressure of energy and environment, a three-party evolutionary game model of innovation diffusion based on core manufacturers, affiliated enterprises and the government is established in the article to explore the adjustment of the three-party game strategy of core manufacturers and affiliated enterprises under government supervision, and then analyze the optimal stability conditions that are conducive to the innovation diffusion of digital innovation technology. It is found that the core manufacturers would choose the diffusion strategy of digital innovation technology, which would take the initiative to adopt the diffused digital innovation technology due to cost considerations, without easily choosing independent research and development (hereafter R&D strategy); the reward and punishment mechanism made by the government for core manufacturers' digital innovation technology diffusion could effectively promote technology diffusion in the market.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"178 ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a wide connection between linguistics and artificial intelligence (AI), including the multimodal language matching. Multi-modal robots possess the capability to process various sensory modalities, including vision, auditory, language, and touch, offering extensive prospects for applications across various domains. Despite significant advancements in perception and interaction, the task of visual-language matching remains a challenging one for multi-modal robots. Existing methods often struggle to achieve accurate matching when dealing with complex multi-modal data, leading to potential misinterpretation or incomplete understanding of information. Additionally, the heterogeneity among different sensory modalities adds complexity to the matching process. To address these challenges, we propose an approach called vision-language matching with semantically aligned embeddings (VLMS), aimed at improving the visual-language matching performance of multi-modal robots.
{"title":"Breaking Boundaries Between Linguistics and Artificial Intelligence","authors":"Jinhai Wang, Yi Tie, Xia Jiang, Yilin Xu","doi":"10.4018/joeuc.334013","DOIUrl":"https://doi.org/10.4018/joeuc.334013","url":null,"abstract":"There is a wide connection between linguistics and artificial intelligence (AI), including the multimodal language matching. Multi-modal robots possess the capability to process various sensory modalities, including vision, auditory, language, and touch, offering extensive prospects for applications across various domains. Despite significant advancements in perception and interaction, the task of visual-language matching remains a challenging one for multi-modal robots. Existing methods often struggle to achieve accurate matching when dealing with complex multi-modal data, leading to potential misinterpretation or incomplete understanding of information. Additionally, the heterogeneity among different sensory modalities adds complexity to the matching process. To address these challenges, we propose an approach called vision-language matching with semantically aligned embeddings (VLMS), aimed at improving the visual-language matching performance of multi-modal robots.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"12 5","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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