Journal of Business Analytics最新文献_第2页

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":"54 1","pages":"203 - 216"},"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

{"title":"Mapping business analytics skillsets with industries: empirical evidence from online job advertisements","authors":"Hong Qin, Kai S. Koong, Haoyu Wen, Lai C. Liu","doi":"10.1080/2573234X.2022.2136541","DOIUrl":"https://doi.org/10.1080/2573234X.2022.2136541","url":null,"abstract":"ABSTRACT As a large accumulation of data is captured and contained, organisations find that the invaluable information can be used to improve company performance, leverage competitive advantages, and create business values. Using business analytics (BA) job advertisements collected from a recruiting website, this study identified knowledge domains and skillsets of BA professionals. Additionally, it examined the relative importance of these BA skills in different industries such as Financial and Information Technology services. The results of Text mining analysis indicate that data modelling, statistical software, visualisation, forecasting, and database are the top ranked BA technical skills. In addition, process skills such as communication, project management, and financial techniques are crucial. The association rules analysis recognises the relative importance of BA skillsets across different industries. The findings contribute to the employability and professional development of new graduates; additionally, they provide insights to BA academic curriculum design and human resources management.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"01 1","pages":"167 - 179"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86105893","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

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":"1 1","pages":"217 - 242"},"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

From star rating to sentiment rating: using textual content of online reviews to develop more effective reputation systems for peer-to-peer accommodation platforms 从星级评价到情感评价:利用在线评论的文本内容，为点对点住宿平台开发更有效的信誉系统

Journal of Business Analytics Pub Date : 2022-09-13 DOI: 10.1080/2573234X.2022.2122880

H. Zolbanin, Donald Wynn

{"title":"From star rating to sentiment rating: using textual content of online reviews to develop more effective reputation systems for peer-to-peer accommodation platforms","authors":"H. Zolbanin, Donald Wynn","doi":"10.1080/2573234X.2022.2122880","DOIUrl":"https://doi.org/10.1080/2573234X.2022.2122880","url":null,"abstract":"ABSTRACT Star ratings on P2P accommodation platforms are highly positive. Such biases have led many users to utilise selective processing strategies to evaluate the textual content of online reviews. However, when many reviews are available for a product or a service, these strategies would be suboptimal at best, posing several challenges to the users of peer-to-peer (P2P) accommodation platforms. To enable the guests to perform more informed evaluations and overcome the challenges that the skewed distribution of star ratings creates for decision-making, we employ content analysis tools to derive an aggregated sentiment score for each listing. Using this score, we define a new measure, called “sentiment rating”, that compares a listing with other similar listings based on their textual reviews. Our choice-based conjoint experiment suggests that unlike users’ initial perception about the function of star rating as the most salient factor in evaluating P2P listings, users actually attribute more importance to sentiment ratings of P2P accommodations. Therefore, a text-based summary of online reviews would indeed help users in evaluating alternatives on a P2P platform and in decision making. We argue that a text-based quantitative summary of user reviews could be a useful supplements to (or substitutes for) star ratings on P2P accommodation platforms and even online retailing websites.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"55 1","pages":"127 - 139"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91264882","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

Analysis of employee perception of employer brand: a comparative study across business cycles using structural topic modelling 员工对雇主品牌的认知分析:使用结构主题模型的跨商业周期比较研究

Journal of Business Analytics Pub Date : 2022-08-02 DOI: 10.1080/2573234X.2022.2104663

G. Karkhanis, Suresh Udhavdas Chandnani, Swapnajit Chakraborti

{"title":"Analysis of employee perception of employer brand: a comparative study across business cycles using structural topic modelling","authors":"G. Karkhanis, Suresh Udhavdas Chandnani, Swapnajit Chakraborti","doi":"10.1080/2573234X.2022.2104663","DOIUrl":"https://doi.org/10.1080/2573234X.2022.2104663","url":null,"abstract":"ABSTRACT Employer branding is an important measure to attract prospective employees and to motivate, engage, and retain their current employees. Employer branding is instrumental for the employer to position the organisation in the minds of current and potential employees by using a combination of economic, psychological, and functional benefits. In the current research the authors implement a set of natural language processing techniques (structural topic modelling) on the employee reviews posted on Glassdoor.com (an online platform where the employees can post reviews about their current and previous employers). The study has thematically structured the 35,075 reviews from 8 Information Technology companies, spanning 5 years from 2015 to 2019. The study compares the employer branding parameters and has identified the prominent dimensions across the expansionary (2015–2017) and contractionary (2017–2019) phases of business cycles. A significant difference in topical proportions were found across the business cycles, suggesting different priorities for different dimensions of the employer brand during expansionary and contractionary phases. The findings would serve as guidance for HR managers to understand the trends in the employee perceptions in the context of changing macro-environment situations and accordingly recalibrate their existing strategies for talent attraction and retention","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"31 1","pages":"95 - 111"},"PeriodicalIF":0.0,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84371052","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

Algorithmic enhancements to identify predictable components from users’ data and a framework to detect misinformation in social media 增强算法，从用户数据中识别可预测的组件，并建立框架，检测社交媒体中的错误信息

Journal of Business Analytics Pub Date : 2022-07-18 DOI: 10.1080/2573234X.2022.2100834

G. Dixit, A. Kushwaha

引用次数: 0