Yongshan Zhang, Zhiyun Jiang, Cong Peng, Xiumei Zhu, Gang Wang
The significance of financial risk lies in its impact on economic stability and individual/institutional financial security. Effective risk management is crucial for market confidence and crisis prevention. Current methods for multivariate time series anomaly detection have limitations in adaptability and generalization. To address this, we propose an innovative approach integrating contrastive learning and Generative Adversarial Networks (GANs). We use geometric distribution masking for data augmentation to enhance dataset diversity. Within the GAN framework, we train a Transformer-based autoencoder to capture normal point distributions. We include contrastive loss in the discriminator to ensure robust generalization. Rigorous experiments on four real-world datasets show that our method effectively mitigates overfitting and outperforms state-of-the-art approaches. This enhances anomaly identification in risk management, paving the way for deep learning in finance, and offering insights for future research and practical use.
金融风险的重要性在于其对经济稳定和个人/机构金融安全的影响。有效的风险管理对于市场信心和危机预防至关重要。目前的多变量时间序列异常检测方法在适应性和泛化方面存在局限性。为解决这一问题，我们提出了一种将对比学习和生成对抗网络（GANs）相结合的创新方法。我们使用几何分布掩码进行数据扩增，以增强数据集的多样性。在 GAN 框架内，我们训练基于变换器的自动编码器来捕捉正态点分布。我们在判别器中加入了对比损失，以确保强大的泛化能力。在四个真实世界数据集上进行的严格实验表明，我们的方法能有效缓解过拟合，并优于最先进的方法。这增强了风险管理中的异常识别能力，为金融领域的深度学习铺平了道路，并为未来的研究和实际应用提供了启示。
{"title":"Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk Assessment","authors":"Yongshan Zhang, Zhiyun Jiang, Cong Peng, Xiumei Zhu, Gang Wang","doi":"10.4018/joeuc.342094","DOIUrl":"https://doi.org/10.4018/joeuc.342094","url":null,"abstract":"The significance of financial risk lies in its impact on economic stability and individual/institutional financial security. Effective risk management is crucial for market confidence and crisis prevention. Current methods for multivariate time series anomaly detection have limitations in adaptability and generalization. To address this, we propose an innovative approach integrating contrastive learning and Generative Adversarial Networks (GANs). We use geometric distribution masking for data augmentation to enhance dataset diversity. Within the GAN framework, we train a Transformer-based autoencoder to capture normal point distributions. We include contrastive loss in the discriminator to ensure robust generalization. Rigorous experiments on four real-world datasets show that our method effectively mitigates overfitting and outperforms state-of-the-art approaches. This enhances anomaly identification in risk management, paving the way for deep learning in finance, and offering insights for future research and practical use.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"11 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Faced with challenges in sales predicting research, this article combines the capabilities of deep learning algorithms in handling complex tasks and unstructured data. Through analyzing consumer behavior, it selects factors influencing sales, including images, prices and discounts, and historical sales, as input variables for the model. Three different types of neural network models-fully connected neural networks, convolutional neural networks, and recurrent neural networks-are employed to process structured data, image data, and sales sequence data, respectively. This forms a deep neural network for feature representation. Subsequently, based on the outputs of these three types of deep neural networks, a fully connected neural network is employed to train the sales prediction model. Ultimately, experimental results demonstrate that the proposed sales prediction method outperforms exponential regression and shallow neural networks in terms of accuracy.
{"title":"Analyzing E-Commerce Market Data Using Deep Learning Techniques to Predict Industry Trends","authors":"Wei Qian, Yijie Wang","doi":"10.4018/joeuc.342093","DOIUrl":"https://doi.org/10.4018/joeuc.342093","url":null,"abstract":"Faced with challenges in sales predicting research, this article combines the capabilities of deep learning algorithms in handling complex tasks and unstructured data. Through analyzing consumer behavior, it selects factors influencing sales, including images, prices and discounts, and historical sales, as input variables for the model. Three different types of neural network models-fully connected neural networks, convolutional neural networks, and recurrent neural networks-are employed to process structured data, image data, and sales sequence data, respectively. This forms a deep neural network for feature representation. Subsequently, based on the outputs of these three types of deep neural networks, a fully connected neural network is employed to train the sales prediction model. Ultimately, experimental results demonstrate that the proposed sales prediction method outperforms exponential regression and shallow neural networks in terms of accuracy.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"53 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid advancement of financial technology, an increasing number of related advertisements have received widespread attention. User engagement detection during the advertisement viewing process directly reflects the effectiveness of the advertising video. Therefore, detecting user engagement during the advertisement viewing process has become a crucial issue. However, traditional engagement detection methods often require significant computational resources, significantly reducing their practicality. To address this issue, the authors propose a method to effectively detect user engagement by fully integrating multiple relatively practical models. Specifically, the authors extract key frame images from user face video and perform super-resolution reconstruction of them. Then image pyramid matching is used to achieve user engagement detection. Finally, the authors establish a reasonable database and conduct sufficient experiments based on it. Experimental results demonstrate that this proposed method has realistic engagement detection accuracy, and the design of multiple steps is also valid.
{"title":"User Engagement Detection-Based Financial Technology Advertising Video Effectiveness Evaluation","authors":"Qun Gao","doi":"10.4018/joeuc.340931","DOIUrl":"https://doi.org/10.4018/joeuc.340931","url":null,"abstract":"With the rapid advancement of financial technology, an increasing number of related advertisements have received widespread attention. User engagement detection during the advertisement viewing process directly reflects the effectiveness of the advertising video. Therefore, detecting user engagement during the advertisement viewing process has become a crucial issue. However, traditional engagement detection methods often require significant computational resources, significantly reducing their practicality. To address this issue, the authors propose a method to effectively detect user engagement by fully integrating multiple relatively practical models. Specifically, the authors extract key frame images from user face video and perform super-resolution reconstruction of them. Then image pyramid matching is used to achieve user engagement detection. Finally, the authors establish a reasonable database and conduct sufficient experiments based on it. Experimental results demonstrate that this proposed method has realistic engagement detection accuracy, and the design of multiple steps is also valid.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"115 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.
{"title":"The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer","authors":"Hang Zhang, Wenzheng Qu, Huizhen Long, Min Chen","doi":"10.4018/joeuc.340932","DOIUrl":"https://doi.org/10.4018/joeuc.340932","url":null,"abstract":"With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"113 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lingyu Hu, Xianglu Hua, Lianqing Zhang, Jie Zhou, Yubo Tu
Disruption events highlight the importance of supply chain resilience (SCR) and leave managers wondering what characteristics can help firms survive and recover. This study employs the knowledge-based theory to investigate factors contributing to SCR. Using data collected from 220 manufacturing firms in China, this study empirically examines the proposed research model. Results indicate KM processes (i.e., creation, sharing, utilization) significantly influence SCR, with collaborative innovation capability (CIC) mediating the relationship between KM and SCR. Interestingly, social media use positively moderates the relationship between knowledge sharing/utilization and CIC, while this effect is absent for the relationship between knowledge creation and CIC. These findings enrich the existing literature on knowledge management and supply chain management, offering managerial insights for effective knowledge strategies and resilience improvement.
{"title":"How Does Knowledge Management Matter for Supply Chain Resilience?","authors":"Lingyu Hu, Xianglu Hua, Lianqing Zhang, Jie Zhou, Yubo Tu","doi":"10.4018/joeuc.340721","DOIUrl":"https://doi.org/10.4018/joeuc.340721","url":null,"abstract":"Disruption events highlight the importance of supply chain resilience (SCR) and leave managers wondering what characteristics can help firms survive and recover. This study employs the knowledge-based theory to investigate factors contributing to SCR. Using data collected from 220 manufacturing firms in China, this study empirically examines the proposed research model. Results indicate KM processes (i.e., creation, sharing, utilization) significantly influence SCR, with collaborative innovation capability (CIC) mediating the relationship between KM and SCR. Interestingly, social media use positively moderates the relationship between knowledge sharing/utilization and CIC, while this effect is absent for the relationship between knowledge creation and CIC. These findings enrich the existing literature on knowledge management and supply chain management, offering managerial insights for effective knowledge strategies and resilience improvement.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"8 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the potential application of deep learning techniques in stock market prediction and investment decision-making. The authors used multi-temporary stock data (MTS) for effective multi-scale feature extraction in reverse cross attention (RCA), combined with improved whale optimization algorithm (IWOA) to select the optimal parameters for the bidirectional long short-term memory network (BiLSTM) and constructed an innovative RCA-BiLSTM stock intelligent trend prediction model. At the same time, a complete RCA-BiLSTM-DQN stock intelligent prediction and investment model was established by combining the deep Q network (DQN) investment strategy. The research results indicate that the model has excellent sequence modeling and decision learning capabilities, which can capture the nonlinear characteristics and complex correlations of the market and provide more accurate prediction results. It can continuously improve the robustness and stability of the model through adaptive learning and automatic optimization.
{"title":"Deep Learning-Based Stock Market Prediction and Investment Model for Financial Management","authors":"Yijing Huang, Vinay Vakharia","doi":"10.4018/joeuc.340383","DOIUrl":"https://doi.org/10.4018/joeuc.340383","url":null,"abstract":"This study explores the potential application of deep learning techniques in stock market prediction and investment decision-making. The authors used multi-temporary stock data (MTS) for effective multi-scale feature extraction in reverse cross attention (RCA), combined with improved whale optimization algorithm (IWOA) to select the optimal parameters for the bidirectional long short-term memory network (BiLSTM) and constructed an innovative RCA-BiLSTM stock intelligent trend prediction model. At the same time, a complete RCA-BiLSTM-DQN stock intelligent prediction and investment model was established by combining the deep Q network (DQN) investment strategy. The research results indicate that the model has excellent sequence modeling and decision learning capabilities, which can capture the nonlinear characteristics and complex correlations of the market and provide more accurate prediction results. It can continuously improve the robustness and stability of the model through adaptive learning and automatic optimization.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"5 3‐4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the face of intensifying global climate change, carbon neutrality has emerged as a pivotal strategy to curb greenhouse gas emissions and confront the complexities associated with climate challenges. However, achieving carbon neutrality poses a formidable challenge: the identification and mitigation of anomalies within the carbon sequestration process. These anomalies can result in unintended carbon dioxide leakage, emissions, or system failures, thus jeopardizing the feasibility and resilience of carbon neutrality initiatives. This research introduces the ResNet-BIGRU-TPA network, an innovative model that integrates deep learning techniques with time series attention mechanisms. The primary focus centers on addressing the intricate task of anomaly detection within the realm of carbon offsetting, specifically aiming to enhance precision in identifying a wide array of complex anomalous events. Through rigorous experimental validation across four diverse datasets, the model has exhibited exceptional performance.
{"title":"Collaborative Application of Deep Learning Models for Enhanced Accuracy and Prediction in Carbon Neutrality Anomaly Detection","authors":"Yi Wang, Tianyu Wang, Wanyu Wang, Yiru Hou","doi":"10.4018/joeuc.340385","DOIUrl":"https://doi.org/10.4018/joeuc.340385","url":null,"abstract":"In the face of intensifying global climate change, carbon neutrality has emerged as a pivotal strategy to curb greenhouse gas emissions and confront the complexities associated with climate challenges. However, achieving carbon neutrality poses a formidable challenge: the identification and mitigation of anomalies within the carbon sequestration process. These anomalies can result in unintended carbon dioxide leakage, emissions, or system failures, thus jeopardizing the feasibility and resilience of carbon neutrality initiatives. This research introduces the ResNet-BIGRU-TPA network, an innovative model that integrates deep learning techniques with time series attention mechanisms. The primary focus centers on addressing the intricate task of anomaly detection within the realm of carbon offsetting, specifically aiming to enhance precision in identifying a wide array of complex anomalous events. Through rigorous experimental validation across four diverse datasets, the model has exhibited exceptional performance.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"51 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual search technology, because of its convenience and high efficiency, is widely used by major tourism e-commerce platforms in product search functions. This study introduces an innovative visual search engine model, namely CLIP-ItP, aiming to thoroughly explore the application potential of visual search in tourism e-commerce. The model is an extension of the CLIP (contrastive language-image pre-training) framework and is developed through three pivotal stages. Firstly, by training an image feature extractor and a linear model, the visual search engine labels images, establishing an experimental visual search engine. Secondly, CLIP-ItP jointly trains multiple text and image encoders, facilitating the integration of multimodal data, including product image labels, categories, names, and attributes. Finally, leveraging user-uploaded images and jointly selected product attributes, CLIP-ItP provides personalized top-k product recommendations.
{"title":"A Novel Deep Learning-Based Visual Search Engine in Digital Marketing for Tourism E-Commerce Platforms","authors":"Yingli Wu, Qiuyan Liu","doi":"10.4018/joeuc.340386","DOIUrl":"https://doi.org/10.4018/joeuc.340386","url":null,"abstract":"Visual search technology, because of its convenience and high efficiency, is widely used by major tourism e-commerce platforms in product search functions. This study introduces an innovative visual search engine model, namely CLIP-ItP, aiming to thoroughly explore the application potential of visual search in tourism e-commerce. The model is an extension of the CLIP (contrastive language-image pre-training) framework and is developed through three pivotal stages. Firstly, by training an image feature extractor and a linear model, the visual search engine labels images, establishing an experimental visual search engine. Secondly, CLIP-ItP jointly trains multiple text and image encoders, facilitating the integration of multimodal data, including product image labels, categories, names, and attributes. Finally, leveraging user-uploaded images and jointly selected product attributes, CLIP-ItP provides personalized top-k product recommendations.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"381 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Driven by the wave of digitalization, the booming development of the e-commerce industry urgently requires in-depth analysis of user shopping behavior to improve service experience. In view of the limitations of traditional models in dealing with complex shopping scenarios, this study innovatively proposes a deep learning model: the VATA model (a combination of variational autoencoder, transformer, and attention mechanism). Through this model, the authors strive to classify and analyze user shopping behavior more accurately and intelligently. Variational autoencoder (VAE) can learn the potential representation of user personalized historical data, capture the implicit characteristics of shopping behavior, and improve the ability to deal with actual shopping situations. Transformer models can more comprehensively capture the dependencies between shopping behaviors and understand shopping. The overall structure of behavior plays an important role.
{"title":"Deep Learning and User Consumption Trends Classification and Analysis Based on Shopping Behavior","authors":"Yishu Liu, Jia Hou, Wei Zhao","doi":"10.4018/joeuc.340038","DOIUrl":"https://doi.org/10.4018/joeuc.340038","url":null,"abstract":"Driven by the wave of digitalization, the booming development of the e-commerce industry urgently requires in-depth analysis of user shopping behavior to improve service experience. In view of the limitations of traditional models in dealing with complex shopping scenarios, this study innovatively proposes a deep learning model: the VATA model (a combination of variational autoencoder, transformer, and attention mechanism). Through this model, the authors strive to classify and analyze user shopping behavior more accurately and intelligently. Variational autoencoder (VAE) can learn the potential representation of user personalized historical data, capture the implicit characteristics of shopping behavior, and improve the ability to deal with actual shopping situations. Transformer models can more comprehensively capture the dependencies between shopping behaviors and understand shopping. The overall structure of behavior plays an important role.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"248 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daiheng Li, Mingyue Liu, Yun Zhao, Yuzhu Li, Tao Zhang, Wenjia Zhang, Dongrui Xia, Bo Lv
With the rapid development of artificial intelligence technology, algorithmic management is increasingly prevalent in enterprises. Despite the considerable scholarly attention given to the impact of algorithmic management, a research gap remains regarding its influence on employee creativity. To address this gap, the authors developed a theoretical model using ability-motivation-opportunity (AMO) theory. This model aims to investigate the direct impacts of algorithmic management (opportunity) on employee creativity (performance) while also considering the mediating roles played by knowledge combination capability (ability) and achievement goal (motivation). Using a sample of 327 paired leader-employee data from an information technology service company, the findings reveal that algorithmic management has a negative effect on employee creativity. Furthermore, the results demonstrate that algorithmic management negatively influences employee creativity through its impact on knowledge combination capability and achievement goal.
{"title":"Why Does Algorithmic Management Undermine Employee Creativity?","authors":"Daiheng Li, Mingyue Liu, Yun Zhao, Yuzhu Li, Tao Zhang, Wenjia Zhang, Dongrui Xia, Bo Lv","doi":"10.4018/joeuc.340037","DOIUrl":"https://doi.org/10.4018/joeuc.340037","url":null,"abstract":"With the rapid development of artificial intelligence technology, algorithmic management is increasingly prevalent in enterprises. Despite the considerable scholarly attention given to the impact of algorithmic management, a research gap remains regarding its influence on employee creativity. To address this gap, the authors developed a theoretical model using ability-motivation-opportunity (AMO) theory. This model aims to investigate the direct impacts of algorithmic management (opportunity) on employee creativity (performance) while also considering the mediating roles played by knowledge combination capability (ability) and achievement goal (motivation). Using a sample of 327 paired leader-employee data from an information technology service company, the findings reveal that algorithmic management has a negative effect on employee creativity. Furthermore, the results demonstrate that algorithmic management negatively influences employee creativity through its impact on knowledge combination capability and achievement goal.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"300 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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