Journal of Business and Management Studies最新文献_第3页

Deep Learning for Enterprise Decision-Making: A Comprehensive Study in Stock Market Analytics 企业决策的深度学习：股市分析综合研究

Journal of Business and Management Studies Pub Date : 2024-04-20 DOI: 10.32996/jbms.2024.6.2.15

Sarder Abdulla Al Shiam, Md Mahdi Hasan, Md Boktiar Nayeem, M. Tazwar Hossian Choudhury, Proshanta Kumar Bhowmik, Sarmin Akter Shochona, Ahmed Ali Linkon, Md Murshid Reja Sweet, Md Rasibul Islam

{"title":"Deep Learning for Enterprise Decision-Making: A Comprehensive Study in Stock Market Analytics","authors":"Sarder Abdulla Al Shiam, Md Mahdi Hasan, Md Boktiar Nayeem, M. Tazwar Hossian Choudhury, Proshanta Kumar Bhowmik, Sarmin Akter Shochona, Ahmed Ali Linkon, Md Murshid Reja Sweet, Md Rasibul Islam","doi":"10.32996/jbms.2024.6.2.15","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.15","url":null,"abstract":"This study explores the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs), on organizational decision-making in the stock market. Utilizing CNN architectures like VGG16, ResNet50, and InceptionV3, the research emphasizes the significance of leveraging deep learning for improved business intelligence and management. It highlights the superiority of CNN models over traditional algorithms, with VGG16 achieving an accuracy rate of 90.45%. The study underscores the potential of deep learning in extracting valuable insights from complex data, leading to a shift in optimizing organizational processes. Additionally, it stresses the importance of investing in infrastructure and expertise for successful CNN integration, alongside addressing ethical and privacy concerns. Through a dive into real-time mathematical concepts, the study provides insights into CNN functionality and offers comparisons between different architectures, aiding in specialized applications such as stock market trends.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":" 998","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681904","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}

引用次数: 0

Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective 在预测性销售预测中解决季节性和趋势检测问题：机器学习视角

Journal of Business and Management Studies Pub Date : 2024-04-11 DOI: 10.32996/jbms.2024.6.2.10

MD Rokiobul Hasan

{"title":"Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective","authors":"MD Rokiobul Hasan","doi":"10.32996/jbms.2024.6.2.10","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.10","url":null,"abstract":"Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714243","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}

引用次数: 0

Human Resource Green Practices Towards Sustainability: The Case of Foxconn Company in China 实现可持续发展的人力资源绿色实践：中国富士康公司案例

Journal of Business and Management Studies Pub Date : 2024-04-11 DOI: 10.32996/jbms.2024.6.2.11

Liu Chen

{"title":"Human Resource Green Practices Towards Sustainability: The Case of Foxconn Company in China","authors":"Liu Chen","doi":"10.32996/jbms.2024.6.2.11","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.11","url":null,"abstract":"This paper mainly studies whether Foxconn's green human resource management can bring benefits to Foxconn and puts forward suggestions on the problems existing in the implementation of green human resource management in Foxconn. The data for this chapter comes from Foxconn employees in Yantai, Shandong Province. Foxconn's HR green practices include green recruitment, green training, green performance, green compensation and green participation. After analysis, it is concluded that the education level of Foxconn employees is generally low, which is not conducive to the implementation of green human resources practices. Foxconn did not give full play to the leading role of supervisors in green participation practices. This study believes that enterprises should play the role of supervisors when implementing human resource green practices, and supervisors should give support and guidance when employees participate in environmental protection activities. Foxconn should focus on improving the education level of its employees. The research results of this paper are helpful for enterprises to achieve the goal of green development, help enterprises to further understand the green practice strategy of human resources, make the development of enterprises meet the requirements of green environmental protection, and improve the competitive advantage of enterprises.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713092","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}

引用次数: 0

Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs 释放深度学习：利用 CNN 改变电子商务利润预测

Journal of Business and Management Studies Pub Date : 2024-04-11 DOI: 10.32996/jbms.2024.6.2.12

Norun Nabi, Md Amran Hossen Pabel, Mohammad Anisur Rahman, Abu Sufian Mozumder, Md Al-Imran, Murshid Reja Sweet, Md Zahidul Islam, Mohammed Nazmul, Islam Miah, Refat Naznin, Mohammad Kawsur Sharif

引用次数: 1

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis 用于股市预测的深度学习模型：综合比较分析

Journal of Business and Management Studies Pub Date : 2024-04-02 DOI: 10.32996/jbms.2024.6.2.9

Md Salim Chowdhury, Norun Nabi, Md Nasir Uddin Rana, Mujiba Shaima, Hammed Esa, Anik Mitra, Md Abu Sufian Mozumder, Irin Akter Liza, Murshid Reja Sweet, Refat Naznin, Md Murshid

{"title":"Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis","authors":"Md Salim Chowdhury, Norun Nabi, Md Nasir Uddin Rana, Mujiba Shaima, Hammed Esa, Anik Mitra, Md Abu Sufian Mozumder, Irin Akter Liza, Murshid Reja Sweet, Refat Naznin, Md Murshid","doi":"10.32996/jbms.2024.6.2.9","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.9","url":null,"abstract":"This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751831","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}

引用次数: 1

The Role of Management Information Systems in the History of Mental Health Care for Prisoners in the USA 管理信息系统在美国囚犯心理健康护理历史中的作用

Journal of Business and Management Studies Pub Date : 2024-03-30 DOI: 10.32996/jbms.2024.6.2.7

Md Jahangir Alom

引用次数: 0

An Empirical Investigation into the Leadership Traits of Prime Minister Narendra Modi: A Conceptual Framework 纳伦德拉-莫迪总理领导特质的实证调查：概念框架

Journal of Business and Management Studies Pub Date : 2024-03-30 DOI: 10.32996/jbms.2024.6.2.8

Prem Lal Joshi

{"title":"An Empirical Investigation into the Leadership Traits of Prime Minister Narendra Modi: A Conceptual Framework","authors":"Prem Lal Joshi","doi":"10.32996/jbms.2024.6.2.8","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.8","url":null,"abstract":"The purpose of this empirical research is i) to find out what unique traits make PM Modi an inspiring leader, ii) to assess if respondents' perceptions differ significantly, and iii) to create a conceptual framework of leadership traits so that we can better comprehend PM Modi's leadership styles. The study includes 19 leadership traits relevant to PM Modi's leadership styles, extracted from previous articles and expert discussions. This study collected quantitative data from LinkedIn connections using a random sampling procedure. A self-designed questionnaire was sent to 700 people from various backgrounds to rate their agreement or disagreement with 19 leadership traits. The study was conducted in March and February 2024, with 29% of the responses (203) available for analysis. The study reveals that the top ten leadership traits in ranking order include strong 'network building, ''self-motivation,’ ‘global perspective,’ ‘visionary leadership,’ ‘determination and result-orientation,’ ‘public direction communication skills,’ ‘proactive approach,’ ‘being organized (detailed-oriented),’ ‘integrity in the workplace,’ and ‘creative thoughts and thinking.’ The results of the Mann-Whitney test revealed differences in respondents' assessments of PM Modi's leadership abilities in India and other countries. There are significant differences in the perceptions of educators and other groups regarding the five leadership traits that may be linked to the lack of trust in Indian society and culture. Additionally, the factor analysis produced a five-factor model: visionary and transformative; humanistic and value-driven; decisive and result-oriented; social influencer and opinion leader; and flexibility, adaptability, and dynamic. It appears that PM Modi's personality and leadership style are a perfect fit for the extended version of the Greatman hypothesis, which is the trait theory.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"52 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140361815","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}

引用次数: 0

A Comprehensive Exploration of Outlier Detection in Unstructured Data for Enhanced Business Intelligence Using Machine Learning 利用机器学习全面探索非结构化数据中的异常值检测，以增强商业智能

Journal of Business and Management Studies Pub Date : 2024-02-27 DOI: 10.32996/jbms.2024.6.1.17

Aishwarya Roy puja, Rasel Mahmud Jewel, Md Salim Chowdhury, Ahmed Ali Linkon, Malay sarkar, Rumana Shahid, Md Al-Imran, Irin Akter Liza, Md Ariful Islam Sarkar

{"title":"A Comprehensive Exploration of Outlier Detection in Unstructured Data for Enhanced Business Intelligence Using Machine Learning","authors":"Aishwarya Roy puja, Rasel Mahmud Jewel, Md Salim Chowdhury, Ahmed Ali Linkon, Malay sarkar, Rumana Shahid, Md Al-Imran, Irin Akter Liza, Md Ariful Islam Sarkar","doi":"10.32996/jbms.2024.6.1.17","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.1.17","url":null,"abstract":"Due to the rapid growth of online data, it is evident that social informatics faces a significant obstacle. The task of effectively utilizing this abundance of information for business intelligence purposes and extracting valuable insights from it across diverse and heterogeneous platforms presents a daunting challenge. Coordinating AI with business knowledge stands apart as an essential worry in the ongoing scene. Customarily, exceptions were many times excused as boisterous information, bringing about the deficiency of relevant data. This paper highlights the need to rethink how outliers are handled and shed light on the primary research challenges in this mining subfield. It presents a thorough scientific categorization of different Business Knowledge strategies and diagrams their ongoing application areas. Also, the paper talks about future exploration bearings and proposals to overcome any barrier concerning oddities in information examination, consequently empowering more successful business methodologies. This work plans to improve the usage of tremendous web-based information hotspots for better business insight results.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"45 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427215","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}

引用次数: 1

Revolutionizing Organizational Decision-Making for Stock Market: A Machine Learning Approach with CNNs in Business Intelligence and Management 彻底改变股票市场的组织决策：商业智能与管理中的 CNN 机器学习方法

Journal of Business and Management Studies Pub Date : 2024-02-13 DOI: 10.32996/jbms.2024.6.1.16

Malay sarkar, Rasel Mahmud Jewel, Md Salim Chowdhury, Md Al-Imran, Rumana Shahid Sawalmeh, Aishwarya Roy puja, Rejon Kumar Ray, Sandip Kumar Ghosh

{"title":"Revolutionizing Organizational Decision-Making for Stock Market: A Machine Learning Approach with CNNs in Business Intelligence and Management","authors":"Malay sarkar, Rasel Mahmud Jewel, Md Salim Chowdhury, Md Al-Imran, Rumana Shahid Sawalmeh, Aishwarya Roy puja, Rejon Kumar Ray, Sandip Kumar Ghosh","doi":"10.32996/jbms.2024.6.1.16","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.1.16","url":null,"abstract":"This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence. The study follows a comprehensive methodology, emphasizing the importance of high-quality datasets in leveraging deep learning for enhanced decision-making. Results demonstrate the superior performance of CNN models over traditional algorithms, with CNN (VGG16) achieving an accuracy rate of 89.45%. The findings underscore the potential of deep learning in extracting meaningful insights from complex data, offering a paradigm shift in optimizing various organizational processes. The article concludes by emphasizing the significance of investing in infrastructure and expertise for successful CNN integration, ensuring ethical considerations, and addressing data privacy concerns. This research contributes to the growing discourse on the application of deep learning in organizational management, providing a valuable resource for businesses navigating the dynamic landscape of the global market.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"133 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780714","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}

引用次数: 0

Revolutionizing Organizational Decision-Making for Stock Market: A Machine Learning Approach with CNNs in Business Intelligence and Management 彻底改变股票市场的组织决策：商业智能与管理中的 CNN 机器学习方法

Journal of Business and Management Studies Pub Date : 2024-02-13 DOI: 10.32996/jbms.2024.6.1.16

Malay sarkar, Rasel Mahmud Jewel, Md Salim Chowdhury, Md Al-Imran, Rumana Shahid Sawalmeh, Aishwarya Roy puja, Rejon Kumar Ray, Sandip Kumar Ghosh

{"title":"Revolutionizing Organizational Decision-Making for Stock Market: A Machine Learning Approach with CNNs in Business Intelligence and Management","authors":"Malay sarkar, Rasel Mahmud Jewel, Md Salim Chowdhury, Md Al-Imran, Rumana Shahid Sawalmeh, Aishwarya Roy puja, Rejon Kumar Ray, Sandip Kumar Ghosh","doi":"10.32996/jbms.2024.6.1.16","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.1.16","url":null,"abstract":"This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence. The study follows a comprehensive methodology, emphasizing the importance of high-quality datasets in leveraging deep learning for enhanced decision-making. Results demonstrate the superior performance of CNN models over traditional algorithms, with CNN (VGG16) achieving an accuracy rate of 89.45%. The findings underscore the potential of deep learning in extracting meaningful insights from complex data, offering a paradigm shift in optimizing various organizational processes. The article concludes by emphasizing the significance of investing in infrastructure and expertise for successful CNN integration, ensuring ethical considerations, and addressing data privacy concerns. This research contributes to the growing discourse on the application of deep learning in organizational management, providing a valuable resource for businesses navigating the dynamic landscape of the global market.","PeriodicalId":505050,"journal":{"name":"Journal of Business and Management Studies","volume":"84 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840607","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}

引用次数: 0