Predictive innovation management using data analytics and machine learning

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dc.contributor.authorKalu, Chikezie Kennedy
dc.contributor.authorDöngül, Esra Sipahi
dc.date.accessioned2024-07-03T06:27:25Z
dc.date.available2024-07-03T06:27:25Z
dc.date.issued2024
dc.departmentSağlık Bilimleri Fakültesi
dc.description.abstractInnovation is a multi-dimensional phenomenon influenced at the organisational level by internal and external factors that can determine how innovative an organisation can be, determining a firmüs business performance. This chapter measures and predicts how innovative a company can be, considering key internal factors using modern data analytics/science. Need for Study: The increasing challenge of modern business operations is affected by how quickly, sustainably, effectively, and efficiently companies can innovate to mitigate the dynamic challenges of current business environments and evolving customer needs. The ability to predict, measure, and manage innovation becomes necessary to ensure that businesses are fit for purpose. Methodology: A model was designed following the study hypotheses and statistically tested. A historical data sample from the OECD global industry dataset for eight years was used for the analysis. The ordinary least square method was used to test for model fit. Also, in machine learning engineering, predictive analysis using the multivariate linear regression analysis method was carried out. Findings: The results support the hypotheses that an organisationüs capacity to be innovative can be measured and predicted, and it is influenced by a good number of internal factors or independent variables at various degrees. Practical Implications: Managers must understand how to measure and predict innovation metrics to manage innovation better, ultimately leading to better business outcomes and performance. Also proposed are new measurement matrices for innovation management: innovation capacity (IC), business innovation value (BIV), innovation creation factor (ICF), and a practical data-driven innovation management and prediction system.
dc.identifier.doi10.1108/978-1-83753-902-420241008
dc.identifier.endpage181en_US
dc.identifier.isbn978-183753902-4, 978-183753903-1
dc.identifier.scopusqualityN/A
dc.identifier.startpage169en_US
dc.identifier.urihttps:/dx.doi.org/10.1108/978-1-83753-902-420241008
dc.identifier.urihttps://hdl.handle.net/20.500.12451/12026
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd.
dc.relation.ispartofVUCA and Other Analytics in Business Resilience
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBusiness Innovation Value
dc.subjectBusiness Performance
dc.subjectData Analytics
dc.subjectInnovation Capacity
dc.subjectInnovation Management
dc.subjectMachine Learning Engineering
dc.titlePredictive innovation management using data analytics and machine learning
dc.typeBook

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