{"title":"Role of External Factors in Adoption of HR Analytics: Does Statistical Background, Gender and Age Matters?","authors":"Ghulam Muhammad, Muddassir Siddiqui, Rizwana Rasheed, Heena Shabbir, Rabia Falak Sher","doi":"10.1080/2573234x.2023.2231966","DOIUrl":"https://doi.org/10.1080/2573234x.2023.2231966","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88774696","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":"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}
{"title":"Data analytics management capability and strategies for interorganisational collaborations: a survey research","authors":"Mohammad Daneshvar Kakhki, Utkarsh Shrivastava","doi":"10.1080/2573234X.2023.2204159","DOIUrl":"https://doi.org/10.1080/2573234X.2023.2204159","url":null,"abstract":"ABSTRACT Drawing on the dynamic capabilities perspective, we propose a research model that explains how data analytics management capability (DAMC) impacts interorganisational collaboration and business performance. Our model incorporates DA strategy as a moderator of the relationship between DAMC and collaboration. We test our model with a survey of 508 practitioners. Our findings suggest that while the DA innovator strategy fosters collaboration, it does not improve performance. In contrast, a more conservative DA strategy leads to higher strategic and operational performance. Our work highlights how leveraging DAMC facilitates effective interorganisational collaborations.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86162625","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":"COVID-19, Russia-Ukraine war and interconnectedness between stock and crypto markets: a wavelet-based analysis","authors":"Wajdi Frikha, M. Brahim, A. Jeribi, Amine Lahiani","doi":"10.1080/2573234X.2023.2193224","DOIUrl":"https://doi.org/10.1080/2573234X.2023.2193224","url":null,"abstract":"ABSTRACT This paper aims to investigate the impacts of the COVID-19 pandemic and Russia-Ukraine war on the interconnectedness between the US and China stock markets, major cryptocurrency and commodity markets using the wavelet coherence approach over the period from January 1 2016 to April 18 2022. The aim is to understand how the COVID-19 pandemic and the Russia-Ukraine war have affected the hedging efficiency of volatile crypto-currencies and gold. Wavelet coherency analysis unveils perceptual differences between the short-term and longer-term market reactions. In the short-run, we find strong co-movements during the first and second waves of the pandemic. During the first wave, longer-term investors were driven by the belief of future pandemic demise. They make use of time diversification that results in positive returns. During the Russia-Ukraine war, S&P 500 leads Bitcoin, BNB, and Ripple whereas Ethereum leads S&P 500 and SSE.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81972581","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}
Annelien Crijns, Victor Vanhullebusch, Manon Reusens, Michael Reusens, B. Baesens
{"title":"Topic modelling applied on innovation studies of Flemish companies","authors":"Annelien Crijns, Victor Vanhullebusch, Manon Reusens, Michael Reusens, B. Baesens","doi":"10.1080/2573234X.2023.2186274","DOIUrl":"https://doi.org/10.1080/2573234X.2023.2186274","url":null,"abstract":"ABSTRACT Mapping innovation in companies for the purpose of official statistics is usually done through business surveys. However, this traditional approach faces several drawbacks like a lack of responses, response bias, low frequency, and high costs. Alternatively, text-based models trained on web-scraped text from company websites have been developed to complement or substitute traditional business surveys. This paper utilises web scraping and text-based models to map the business innovation in Flanders with a focus on identifying different types of innovation through topic modelling. More specifically, the scraped web texts are used to identify innovative economic sectors or topics, and to classify firms into these topics using Top2Vec and Lbl2Vec. We conclude that both models can be successfully combined to discover topics (or sectors) and classify companies into these topics which results in an additional parameter for mapping innovation in different regions.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77672254","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":"More information is not better: examining appropriate information for estimating sales performance in concept testing","authors":"Takumi Kato, Susumu Kamei, Takumi Ootsubo, Yosuke Ichiki","doi":"10.1080/2573234X.2023.2167670","DOIUrl":"https://doi.org/10.1080/2573234X.2023.2167670","url":null,"abstract":"ABSTRACT Research on the requirements for improving the quality of concept testing is scarce because of the high degree of confidentiality in new product developments. In this study, we clarified the factors that can improve sales performance estimation accuracy in concept testing. A randomised controlled trial for the Japanese personal computer market showed that presenting the product and corporate brand yielded the most accurate estimations. Other factors (design, price, and product colour) did not show significant effects. Even a good concept may not increase consumers’ purchase intention if there is lack of clarity about the product’s brand.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84757151","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}
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}
{"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":null,"pages":null},"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}
{"title":"Predicting customers’ cross-buying decisions: a two-stage machine learning approach","authors":"M. Kilinç, Robert Rohrhirsch","doi":"10.1080/2573234X.2022.2128447","DOIUrl":"https://doi.org/10.1080/2573234X.2022.2128447","url":null,"abstract":"ABSTRACT Predicting a customer’s cross-buying behaviour is a challenging problem for many organisations. In this paper, we propose a novel two-stage cross-buying prediction framework by integrating machine learning, feature engineering, and interpretation techniques. Specifically, the first stage aims to train an accurate complex black-box classification model with cross-validation and hyperparameter tuning. Then, the next stage uses the top ten most important predictors of the black-box model to obtain a simple rule-based interpretable model. We use a publicly available dataset published on the Harvard Dataverse to provide a practical case study. The results show that the rule-based model has a predictive performance as high as the complex model.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84396482","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":"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}