Journal of Management Analytics最新文献_第2页

Dynamic control of a firm’s process innovation with knowledge accumulation in a vertically differentiated monopoly 在纵向差异化垄断中通过知识积累动态控制企业的流程创新

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2024-02-06 DOI: 10.1080/23270012.2024.2304540

Shoude Li

引用次数: 0

Mental health in organizations from a healthcare analytics framework: taxonomic model, trends, and impact of COVID-19 从医疗保健分析框架看组织中的心理健康：COVID-19 的分类模型、趋势和影响

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2024-02-06 DOI: 10.1080/23270012.2023.2301709

Jorge Iván Pérez Rave, Carlos Mario Zapata Jaramillo, Gloria Patricia Jaramillo Álvarez

引用次数: 0

On some new fuzzy entropy measure of Pythagorean fuzzy sets for decision-making based on an extended TOPSIS approach 论基于扩展 TOPSIS 方法的毕达哥拉斯模糊集决策的一些新模糊熵量

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2024-01-31 DOI: 10.1080/23270012.2024.2301748

H. D. Arora, Anjali Naithani

引用次数: 0

EPQ model with the effect of inflation and reliability for partial trade credit under fuzzy and cloudy fuzzy environment 模糊和多云模糊环境下部分贸易信贷的具有通货膨胀和可靠性影响的 EPQ 模型

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-12-27 DOI: 10.1080/23270012.2023.2291835

Supriya Tiwari, Kunal Shah, Kajal Bhimani

引用次数: 0

Evolutionary game analysis of information service quality control of e-commerce platforms under information ecology 信息生态下电子商务平台信息服务质量控制的进化博弈分析

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-12-27 DOI: 10.1080/23270012.2023.2291836

Xiaojun Xu, Lu Wang, Xiaoli Wang

引用次数: 0

MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators MCDM技术使用单值嗜中性三角加权聚合算子

2区管理学

Journal of Management Analytics Pub Date : 2023-10-08 DOI: 10.1080/23270012.2023.2264294

Jun Ye, Shigui Du, Rui Yong

{"title":"MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators","authors":"Jun Ye, Shigui Du, Rui Yong","doi":"10.1080/23270012.2023.2264294","DOIUrl":"https://doi.org/10.1080/23270012.2023.2264294","url":null,"abstract":"AbstractMotivated based on the trigonometric t-norm and t-conorm, the aims of this article are to present the trigonometric t-norm and t-conorm operational laws of SvNNs and then to propose the SvNN trigonometric weighted average and geometric aggregation operators for the modelling of a multiple criteria decision making (MCDM) technique in an inconsistent and indeterminate circumstance. To realize the aims, this paper first proposes the trigonometric t-norm and t-conorm operational laws of SvNNs, which contain the hybrid operations of the tangent and arctangent functions and the cotangent and inverse cotangent functions, and presents the SvNN trigonometric weighted average and geometric operators and their properties. Next, a MCDM technique is proposed in view of the presented two aggregation operators in the circumstance of SvNNs. In the end, an actual case of the choice issue of slope treatment schemes is provided to indicate the practicability and effectivity of the proposed MCDM technique.Keywords: Single-valued neutrosophic numbertrigonometric t-norm and t-conormtrigonometric weighted aggregation operatordecision making Data availabilityAll data are included in this study.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Commodity layout in supermarkets: using the integration of the comprehensive related value method and genetic algorithm 超市商品布局:综合相关值法与遗传算法的结合

2区管理学

Journal of Management Analytics Pub Date : 2023-09-21 DOI: 10.1080/23270012.2023.2258376

Chenxia Jin, Fachao Li, Yuqing Xia, Sohail S. Chaudhry

{"title":"Commodity layout in supermarkets: using the integration of the comprehensive related value method and genetic algorithm","authors":"Chenxia Jin, Fachao Li, Yuqing Xia, Sohail S. Chaudhry","doi":"10.1080/23270012.2023.2258376","DOIUrl":"https://doi.org/10.1080/23270012.2023.2258376","url":null,"abstract":"AbstractThe existing shelf layout methods do not explicitly consider the attention and relevancy of the commodity systematically and thus have failed to capture the invalid associations, resulting in poor sales impact and customer satisfaction. For such shortcomings, in this paper, we propose a mathematical programming approach for shelf layout problems based on comprehensive related value. First, we introduce the concepts of related value considering both attention and relevancy; second, we give the concept of adjacent utility value and the freedom of placement, and further analyze the impact of the same commodity on surrounding commodities due to different placement positions; third, we establish a new comprehensive related value-based commodity layout optimization model (CRV-CL) and provide the solution steps integrating with a genetic algorithm. Finally, we analyze the characteristics of CRV-CL through a specific case. The simulation results indicate the overall relevancy after applying the CRV-CL model.Keywords: Shelf layoutcomprehensive related valuefreedom of placementadjacent utility valuegenetic algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not contain any studies with human participants or animals performed by any of the authors.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant (72101082); the Natural Science Foundation of Hebei Province under Grant (F2021208011). The research of Sohail S. Chaudhry was partially supported through a research sabbatical leave from Villanova University.","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136154828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using a novel ensemble learning framework to detect financial reporting misconduct 使用一种新颖的集成学习框架来检测财务报告不当行为

2区管理学

Journal of Management Analytics Pub Date : 2023-09-14 DOI: 10.1080/23270012.2023.2258372

Siqi Pan, Qiang Ye, Wen Shi

{"title":"Using a novel ensemble learning framework to detect financial reporting misconduct","authors":"Siqi Pan, Qiang Ye, Wen Shi","doi":"10.1080/23270012.2023.2258372","DOIUrl":"https://doi.org/10.1080/23270012.2023.2258372","url":null,"abstract":"AbstractOur research focuses on detecting financial reporting misconduct and derives a comprehensive misconduct sample using AAERs and intentional restatements. We develop a novel ensemble learning method, Multi-LightGBM, for highly imbalanced classification learning. We adopt a human-machine cooperation feature selection method, which can mitigate the limitation of incomplete theories, enhance the model performance, and guide researchers to develop new theories. We propose a cost-based measure, expected benefits of classification, to evaluate the economic performance of a model. The out-of-sample tests show that Multi-LightGBM, coupled with the features we selected, outperforms other predictive models. The finding that introducing intentional material restatements into our predictive model does not reduce the effectiveness of capturing AAERs has important implications for research on AAERs detection. Moreover, we can identify more misconduct firms with fewer resources by the misconduct sample relative to the standalone AAERs sample, which is quite beneficial for most model users.Keywords: financial reporting misconductensemble learningfeature selectionLightGBM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Natural Science Foundation of China under [grant numbers 72071038, 72121001].","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134970106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence applications in finance: a survey 人工智能在金融中的应用:一项调查

2区管理学

Journal of Management Analytics Pub Date : 2023-08-10 DOI: 10.1080/23270012.2023.2244503

Xuemei Li, Alexander Sigov, Leonid Ratkin, Leonid A. Ivanov, Ling Li

{"title":"Artificial intelligence applications in finance: a survey","authors":"Xuemei Li, Alexander Sigov, Leonid Ratkin, Leonid A. Ivanov, Ling Li","doi":"10.1080/23270012.2023.2244503","DOIUrl":"https://doi.org/10.1080/23270012.2023.2244503","url":null,"abstract":"AbstractFinance is in our daily life. We invest, borrow, lend, budget, and save money. Finance also provides guidelines for corporation and government spending and revenue collection. Traditional statistical solutions such as regression, PCA, and CFA have been widely used in financial forecasting and analysis. With the increasing interest in artificial intelligence in recent years, this paper reviews the Artificial Intelligence (AI) techniques in the finance domain systematically and attempts to identify the current AI technologies used, major applications, challenges, and trends in Finance. It explores AI-related articles in Finance in IEEE Xplore and EI compendex databases. Findings suggest AI has been engaged in Finance in financial forecasting, financial protection, and financial analysis and decision-making areas. Financial forecasting is one of the main sub-fields of Finance affected by AI technology. Major AI technology used is the supervised learning. Deep learning has gained popular in recent years. AI could be used to address some emerging topics.Keywords: machine learning; artificial intelligencefinance Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135553759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An inventory analysis in a multi-echelon supply chain system under asymmetry fuzzy demand: a fmincon optimization 不对称模糊需求下多级供应链系统库存分析:fmincon优化

IF 5.9 2区管理学

Journal of Management Analytics Pub Date : 2023-07-31 DOI: 10.1080/23270012.2023.2239818

B. Karthick

引用次数: 0