Predictive Innovation Management Using Data Analytics and Machine Learning

Abstract

Purpose: Innovation 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 firm’s 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 organisation’s 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 (I_C), business innovation value (BIV), innovation creation factor (ICF), and a practical data-driven innovation management and prediction system.

Citation

Kalu, C.K. and Döngül, E.S. (2024), "Predictive Innovation Management Using Data Analytics and Machine Learning", Singh, D., Sood, K., Kautish, S. and Grima, S. (Ed.) VUCA and Other Analytics in Business Resilience, Part A (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, pp. 169-181. https://doi.org/10.1108/978-1-83753-902-420241008