A review of data analytics in technological forecasting

Abstract

Abstract Technological forecasting (TF) has significantly benefited from data analytics over the past decades. However, little effort has been made to present an overview of data analytics in TF and discuss its key features and contributions. Consequently, there exist duplication of efforts, inconsistency in applications and a lack of understanding of the current state-of-the-art and common methodology frameworks. This study attempts to fill this research gap by conducting a review of the work on data analytics in TF published in leading journals in the field of technology and innovation management. We first develop a process-focused morphological matrix that provides a simple yet comprehensive view and enables the full spectrum of data analytics in TF to be examined. Specifically, the matrix consists of four dimensions and 12 factors: (1) awareness of TF contexts (objective, time horizon, field and level of analysis); (2) data collection and pre-processing (data source, data item and measure); (3) data analysis and validation (approach, methodology and performance evaluation); and (4) value creation (outcome and implication). A thorough presentation of the literature is then provided after the configurations of each article are identified. Accordingly, we also examine the practical implications of the process-focused morphological matrix and suggest future research directions in the field.