Identifying new innovative services using M&A data: An integrated approach of data-driven morphological analysis

Abstract

This study suggests a concrete framework for generating new service ideas using an M&A dataset. Addressing the limitations of previous works that neglected service-specific characteristics, we suggest methods to extract service-specific keywords and phrases from the text and restructure them to provide clear evidence for new service development. Therefore, we propose a process for building data-driven quality function deployment (QFD) and data-driven morphological analysis (MA). First, M&A transactions were collected from CrunchBase, which is an open platform that provides start-up information. Service actions and service contents are then extracted from the text using natural language processing. For each extracted keyword, a clustering analysis was performed to identify the new service patterns. For clustered service actions and contents, MA is employed to generate new service ideas. This study contributes to the technology management field by first employing M&A records for the data-driven morphological matrix and suggests how to extract service actions and service contents from the text. We also suggested a new systematic way of identifying new services using an integrated approach of QFD and MA. This work is expected to help managers in new service development by providing practical guidance and tools for utilizing textual data.