Journal of Business Analytics最新文献_第2页

Role of External Factors in Adoption of HR Analytics: Does Statistical Background, Gender and Age Matters? 外部因素在采用人力资源分析中的作用:统计背景、性别和年龄重要吗?

Journal of Business Analytics Pub Date : 2023-07-04 DOI: 10.1080/2573234x.2023.2231966

Ghulam Muhammad, Muddassir Siddiqui, Rizwana Rasheed, Heena Shabbir, Rabia Falak Sher

引用次数: 0

An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective 可持续和弹性案例研究的交互式分析方法:机器学习视角

Journal of Business Analytics Pub Date : 2023-04-26 DOI: 10.1080/2573234X.2023.2202691

S. M. Mousavi, Kiarash Sadeghi R., L. Lee

{"title":"An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective","authors":"S. M. Mousavi, Kiarash Sadeghi R., L. Lee","doi":"10.1080/2573234X.2023.2202691","DOIUrl":"https://doi.org/10.1080/2573234X.2023.2202691","url":null,"abstract":"ABSTRACT Sustainable development is a problem-solving method that simultaneously accounts for the economic, environmental, and social impacts of actions. Decision-makers have recently recognised the need for sustainable development. Multiobjective optimisation is the most reliable technique to solve multiple sustainable development goals. However, there needs to be more research examining the role of interactive methods in multiobjective optimisation problems. To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. To show the method’s applicability, this paper uses the proposed algorithm in three sustainable and resilient case studies. The selected cases are the river pollution problem, the urban transit network design problem, and the resilience problem. Moreover, the proposed algorithm is compared with two other algorithms for validation purposes. The results reveal that the proposed algorithm outperforms non-interactive algorithms by providing superior solutions.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74491683","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

Data analytics management capability and strategies for interorganisational collaborations: a survey research 组织间合作的数据分析管理能力与策略:一项调查研究

Journal of Business Analytics Pub Date : 2023-04-23 DOI: 10.1080/2573234X.2023.2204159

Mohammad Daneshvar Kakhki, Utkarsh Shrivastava

引用次数: 0

COVID-19, Russia-Ukraine war and interconnectedness between stock and crypto markets: a wavelet-based analysis 2019冠状病毒病、俄乌战争以及股票和加密市场之间的相互联系:基于小波的分析

Journal of Business Analytics Pub Date : 2023-03-26 DOI: 10.1080/2573234X.2023.2193224

Wajdi Frikha, M. Brahim, A. Jeribi, Amine Lahiani

引用次数: 3

Topic modelling applied on innovation studies of Flemish companies 主题模型在佛兰德公司创新研究中的应用

Journal of Business Analytics Pub Date : 2023-03-03 DOI: 10.1080/2573234X.2023.2186274

Annelien Crijns, Victor Vanhullebusch, Manon Reusens, Michael Reusens, B. Baesens

引用次数: 1

More information is not better: examining appropriate information for estimating sales performance in concept testing 信息越多越好:在概念测试中检查适当的信息以估计销售业绩

Journal of Business Analytics Pub Date : 2023-01-16 DOI: 10.1080/2573234X.2023.2167670

Takumi Kato, Susumu Kamei, Takumi Ootsubo, Yosuke Ichiki

引用次数: 0

A trans-national comparison of stock market movements and related social media chatter during the COVID-19 pandemic COVID-19大流行期间股市走势和相关社交媒体讨论的跨国比较

Journal of Business Analytics Pub Date : 2022-12-13 DOI: 10.1080/2573234X.2022.2155257

Sathyendra Singh Chauhan, Karthik Srinivasan, T. Sharma

{"title":"A trans-national comparison of stock market movements and related social media chatter during the COVID-19 pandemic","authors":"Sathyendra Singh Chauhan, Karthik Srinivasan, T. Sharma","doi":"10.1080/2573234X.2022.2155257","DOIUrl":"https://doi.org/10.1080/2573234X.2022.2155257","url":null,"abstract":"ABSTRACT The outbreak of the SARS-CoV-2 (COVID-19) pandemic first identified in 2019 has had long-term ramifications across global financial markets. We have seen stock markets across countries falling to historical lows and then recovering back during the pandemic. Prior research has established that human emotions can significantly influence financial markets. In particular, social media discussions or online Word-of-mouth (OWoM) minutely reflect public emotions and opinions associated with global market volatility. In this study, we use a quantitative approach to explore the relationship between discussions in twitter, a popular micro-blogging online platform and stock market performance across different countries, in order to understand the disaster-triggered behavioural responses of common investors across the globe. We analyse the association of national stock-indices, sentiment polarity and discussion subjectivity in Covid-19-related tweets originating in India, US, Italy, UK, Australia, Nigeria and South Africa during February 2020– January 2021 period. Using a combination of multiple analytics methods, our study examines: (i) linear and lagged association between OWoM and market performance; and (ii) heterogeneity in the OWoM-market relationship across the seven countries. Our results show weak but statistically significant correlation between OWoM subjectivity and polarity and stock market returns across countries. Our findings also show differential temporal association of OWoM and market returns across countries. Our study shows stock market connectedness between pairs of countries, some simultaneously varying while others varying with a time lag, and the strength of such connectedness increases during global disasters such as the COVID-19 pandemic.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82717892","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

Mapping business analytics skillsets with industries: empirical evidence from online job advertisements 将商业分析技能映射到行业:来自在线招聘广告的经验证据

Journal of Business Analytics Pub Date : 2022-10-22 DOI: 10.1080/2573234X.2022.2136541

Hong Qin, Kai S. Koong, Haoyu Wen, Lai C. Liu

引用次数: 0

Predicting customers’ cross-buying decisions: a two-stage machine learning approach 预测客户的交叉购买决策:两阶段机器学习方法

Journal of Business Analytics Pub Date : 2022-09-30 DOI: 10.1080/2573234X.2022.2128447

M. Kilinç, Robert Rohrhirsch

引用次数: 0

AGGFORCLUS: A hybrid methodology integrating forecasting with clustering to assess mitigation plans and contagion risk in pandemic outbreaks: the COVID-19 Case Study AGGFORCLUS:将预测与聚类相结合的混合方法，用于评估大流行疫情中的缓解计划和传染风险:COVID-19案例研究

Journal of Business Analytics Pub Date : 2022-09-22 DOI: 10.1080/2573234X.2022.2122881

Milton Soto-Ferrari, Alejandro Carrasco-Pena, Diana Prieto

{"title":"AGGFORCLUS: A hybrid methodology integrating forecasting with clustering to assess mitigation plans and contagion risk in pandemic outbreaks: the COVID-19 Case Study","authors":"Milton Soto-Ferrari, Alejandro Carrasco-Pena, Diana Prieto","doi":"10.1080/2573234X.2022.2122881","DOIUrl":"https://doi.org/10.1080/2573234X.2022.2122881","url":null,"abstract":"ABSTRACT The COVID-19 pandemic showed governments’ unpreparedness as decision-makers hastily created restrictions and policies to contain its spread. Identifying prospective areas with a higher contagion risk can reduce mitigation planning uncertainty. This research proposes a risk assessment metric called AGGFORCLUS that integrates time-series forecasting and clustering to convey joint information on predicted caseload growth and variability, thereby providing an educated yet visually simple view of the risk status. In AGGFORCLUS, the development is sectioned into three phases. Phase I forecasts confirmed cases using a mixture of five different forecasting methods. Phase II develops the identified best model forecasts for an extended ten-day horizon, including their prediction intervals. In Phase III, we calculate average growth metrics for predictions and use them to cluster series by their multidimensional average growth. We present the results for various countries framed into a nine-quadrant risk-grouped associated measure linked to the expected cumulative caseload progress and uncertainty.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91090062","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