Journal of Business and Management Studies最新文献

{"title":"Self-Leadership in a Remote Work Environment: Emerging Trends and Implications for Occupational Well-Being","authors":"Charles Nwoko, Khashayar Yazdani","doi":"10.32996/jbms.2024.6.3.5","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.3.5","url":null,"abstract":"When individuals are given the freedom to work remotely, self-discipline and self-motivation become more crucial. Remote work can present challenges to self-leadership as employees are required to independently prioritise their work, make decisions, and hold themselves accountable for meeting deadlines. In this study, structural equation modelling was used to analyse data from 206 employees with remote work experience. The study found that remote work characteristics have implications for occupational well-being and that they influence the effectiveness of leadership and the perception of work roles. It is important that managers ensure employees working remotely enjoy flexible work hours, autonomy, communication and collaboration for improved occupational well-being. This study contributes novel insights into self-leadership and psychological empowerment within the remote work context, emphasising their interconnectedness and implications for occupational well-being.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":" 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994028","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}

{"title":"Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior","authors":"Md Nasir Uddin Rana, Sarder Abdulla Al Shiam, Sarmin Akter Shochona, Md Rasibul Islam, Md Asrafuzzaman, Proshanta Kumar Bhowmik, Refat Naznin, Sandip Kumar Ghosh, Md Ariful Islam Sarkar, Md Asaduzzaman","doi":"10.32996/jbms.2024.6.3.3","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.3.3","url":null,"abstract":"This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep learning models, particularly ResNet50, outperform traditional ones, with an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure and expertise is crucial for successful deep learning integration, offering a competitive edge in banking decision-making.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003829","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}

{"title":"Transformative Impact of Deep Learning in Stock Market Decision-Making: A Comparative Study of Convolutional Neural Networks","authors":"Hammed Esa, Mohammad Anisur Rahman, Md Abu Sufian Mozumder, Nisha Gurung, Mohammed Nazmul Islam Miah, Md Murshid Reja Sweet, Mohammad Kawsur Sharif, Md Rasibul Islam, Md Nasiruddin, MD SANOWAR HOSSAIN SABUJ","doi":"10.32996/jbms.2024.6.3.4","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.3.4","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 (VGG19) 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":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"78 S344","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003337","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}

{"title":"Challenges and Recommendations for the Pension Tourism Market in the Era of Internet Plus","authors":"Jiawen Shi, Xianghui Kong","doi":"10.32996/jbms.2024.6.3.2","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.3.2","url":null,"abstract":"With the rise of \"Internet Plus\", the integration of the pension industry and the tourism market is facing unprecedented challenges while also presenting boundless opportunities. This study aims to explore the underlying motivations, analyze the difficulties encountered in the development of the pension tourism market, and put forward countermeasures. Moreover, the operation mode and promotion mode of the pension tourism market in the era of the \"Internet Plus\" are analyzed in this research through the combination of supply-demand analysis, literature review, and case analysis. The result shows that the changes in Internet technology and market demand pose challenges to the traditional pension tourism market; however, they also provide new opportunities for industry integration and market segmentation. Our research conclusion emphasizes that the industry needs to cultivate talent, introduce intelligent management, strengthen personalized services, simplify service processes, and protect information security to adapt to the changes in the Internet era so as to achieve sustainable development.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"94 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002325","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}

{"title":"Strategic Employee Performance Analysis in the USA: Deploying Machine Learning Algorithms Intelligently","authors":"N. Gurung, Sumon Gazi, Md zahidul Islam","doi":"10.32996/jbms.2024.6.3.1","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.3.1","url":null,"abstract":"Strategic employee performance assessment assists organizations in steering productivity, affirming employee satisfaction, and accomplishing strategic organizational goals. Machine learning algorithms provide several benefits over mainstream techniques in assessing employee performance. This research paper aimed to explore the deployment of machine learning algorithms in assessing employee performance. The prime objective of employee performance analysis is to assess an employee's achievement during a specific time frame. The dataset for this research revolved around the leadership team of a global retailer's specific store level in the USA, extending over 18 months. The dataset for this study was subjected to Python programming software for intensive and comprehensive data analysis as well as for visualization purposes. From the experiment design, it was evident that XG-Boost seems to be the best-performing model overall. In particular, it had the greatest AUC for both holdout and training data (0.86 and 0.88, respectively), and it has a relatively low runtime (16 minutes) and maximum memory utilization (12%). By contrast, Random Forest displayed an average AUC for training data (0.79) but a lesser AUC for holdout data (0.51), which indicates that it may be overfitting the training data; besides, it had a longer runtime than XG-Boost.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"343 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011762","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}

{"title":"Credit Risk Prediction Using Explainable AI","authors":"Sarder Abdulla, Al Shiam, ✉. M. M. Hasan, Md Jubair Pantho, Sarmin Akter Shochona, Md Boktiar Nayeem, M. Tazwar, Hossain Choudhury, Tuan Ngoc Nguyen","doi":"10.32996/jbms.2024.6.2.6","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.6","url":null,"abstract":"Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234774","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}

{"title":"Using Machine Learning Techniques to Forecast Mehram Company's Sales: A Case Study","authors":"Mahsa Soltaninejad, Reyhaneh Aghazadeh, Samin Shaghaghi, Majid Zarei","doi":"10.32996/jbms.2024.6.2.4","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.2.4","url":null,"abstract":"Sales forecasting, situated at the intersection of art and science, is critical for inspiring managers toward achieving profitable outcomes. Its precision sustains production levels and capital and plays a pivotal role in the company's and its leaders' overall success and career progression. In the context of Mahram Food Industries, the challenge arises from diverse investor perspectives and the impactful nature of numerous variables. To address this, a new sales forecasting algorithm has been introduced to enhance accuracy. The aim is to predict future sales through a comprehensive approach, leveraging technical analysis, time series modeling, machine learning, neural networks, and random forest techniques. The research methodology integrates various advanced techniques to improve sales forecasting for Mahram Food Industries. Technical analysis, time series modeling, machine learning, neural networks, and random forest methods are combined to create a robust framework. The focus is on predicting sales for a future period within the artificial intelligence-based machine learning domain. The study employs metrics such as Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE) to evaluate and compare the performance of the proposed neural network against traditional methods like multiple variable regression and time series modeling. The study's results highlight the superior performance of the neural network in sales forecasting for Mahram Food Industries. The Mean Absolute Deviation (MAD) for the neural network is 28.33, outperforming multiple variable regression (28.54) and time series modeling (29.45). Additionally, the neural network demonstrates a better MAD Percentage (MADP) with a value of 10.2%, surpassing the values associated with multiple variable regression (10.35%) and time series modeling (10.30%). The Mean Squared Error (MSE) further confirms the neural network's superiority with a value of 6452 compared to 6472 and 7865 for multiple variable regression and time series modeling, respectively. In conclusion, the study showcases the effectiveness of integrating advanced techniques, particularly the neural network, in enhancing the accuracy of sales forecasting for Mahram Food Industries. The comprehensive approach, combining technical analysis, time series modeling, machine learning, neural networks, and random forest, is a valuable strategy for predicting future sales. The superior performance of the neural network, as evidenced by lower MAD, MADP, and MSE values, suggests its potential for guiding informed decision-making in goal setting, hiring, budgeting, and other critical aspects of business management.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"7 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259974","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}

{"title":"Enhancing Retail Success: A Comprehensive Analysis of Visual Merchandising Influence on Customer Engagement and Purchase Behavior in Philippine Local Retail Businesses","authors":"Cris Saranza, Yuri Pendon, Glenn Andrin","doi":"10.32996/jbms.2024.6.1.1","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.1.1","url":null,"abstract":"This study employed a quantitative approach to investigate the influence of visual merchandising practices on customer engagement and purchase behavior in retail stores in Claver, Surigao del Norte. By utilizing a descriptive survey design with 385 customer respondents, the findings revealed that retailers inconsistently observed visual merchandising practices. Despite this, the study identified a significant and high positive influence of these practices on customer engagement and purchase behavior. Notably, through multiple regression analysis, it was found that only window displays significantly predicted both customer engagement and purchase behavior, although the collective influence of visual merchandising practices remained substantial. In essence, the research emphasized the integral role of visual merchandising in encouraging customer engagement and guiding their purchase behavior, highlighting the specific relevance of window displays in this retail context.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380622","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}

{"title":"Exploration of Factors Influencing Intention to Leave: Indonesian Professional Working in Qatar Case","authors":"Yudi Siswadi, Aurik Gustomo","doi":"10.32996/jbms.2024.6.1.2","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.1.2","url":null,"abstract":"This study explores job and life satisfaction factors influencing intention to leave, including life satisfaction as moderation role for Indonesian professionals working and living in Qatar. The quantitative job and life satisfaction questionnaire was used to test research hypotheses using Structural Equation Mode. Herzberg's Two-Factor for job satisfaction and Clayton Alderfer's ERG Theory for life satisfaction contain 26 factors with seven Likert scale. Motivators include reward, promotion, work nature, and personal growth, while hygiene factors include pay, working conditions, supervisors, coworkers, workloads, operating conditions, and job security. Existence shows health, environment, housing, and finances. Friends, family, community, leisure, and social status reflect relatedness. Spirituality, culture, and family education indicate growth. 292 participants, with 92.1% above 40 years old and having lived in Qatar for more than 10 years (77.5%) at the same job position (43.8%), participated in the study. The model shows that hygiene factors moderately negatively correlate with job satisfaction, while motivators strongly positively correlate. Selected factors addressed most factors of job satisfaction, excluding operating conditions and job security. Existence needs strongly positively correlate with life satisfaction, while growth needs are not significantly correlated. An unexpected negative correlation exists between relatedness and life satisfaction. Selected factors under existence, relatedness, and growth only explain 1.5% of life satisfaction variation, indicating that other factors are also important but are not taken into account. Job and life satisfaction negatively correlate with intention to leave, while life satisfaction significantly adversely moderates the relationship between both of them, suggesting that attempting to make people happy may help them stay at their jobs when they're unhappy. Other findings show that people are highly satisfied with current pay and financial stability but moderately satisfied with promotion, reward, and future pay increases and financial security. Therefore, improving future finances can make people happier and keep them from quitting their jobs.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380602","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}

{"title":"Innovative Approaches and Entrepreneurial Intentions: Analyzing Indonesia's Youth through the Theory of Planned Behavior","authors":"Ellyana Ayu Pramesti, Rini Kuswati","doi":"10.32996/jbms.2024.6.1.3","DOIUrl":"https://doi.org/10.32996/jbms.2024.6.1.3","url":null,"abstract":"This study aims to explore how innovation, subjective norms, and perceived behavior control impact entrepreneurial intention. It investigates the influence of innovation on attitudes and examines whether attitudes mediate the relationship between innovation and entrepreneurial intentions. The research follows a deductive quantitative approach, utilizing surveys as the primary research design. Analysis of both the outer and inner models was conducted using SmartPLS 3.0 software. The sample size of 140 respondents was selected through purposive sampling. Validity constructs and reliability tests were employed to assess the instruments used for testing. The findings affirm the positive impact of innovation, attitudes, subjective norms, and perceived behavior control on entrepreneurial intentions. Specifically, innovation significantly and positively affects entrepreneurial intentions directly, while subjective norms and perceived behavior control also directly contribute positively to entrepreneurial intentions. Attitudes play a role as a partial mediator between innovation and entrepreneurial intentions. Based on these empirical results, managerial implications suggest enhancing entrepreneurial innovation alongside focusing on subjective norms and perceived behavior control to bolster entrepreneurial intention.","PeriodicalId":250160,"journal":{"name":"Journal of Business and Management Studies","volume":"11 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380177","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}