Development of a New Methodology to Identity Promising Technology Areas Using M&A Information
| Autor: | Yong Sik Chang, Jinho Choi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:GE1-350
velocity Renewable Energy Sustainability and the Environment Computer science lcsh:Environmental effects of industries and plants 05 social sciences Geography Planning and Development lcsh:TJ807-830 lcsh:Renewable energy sources acceleration Management Monitoring Policy and Law Keywords M&A 050905 science studies Investment (macroeconomics) Data science IT sector lcsh:TD194-195 0502 economics and business Identity (object-oriented programming) promising areas 0509 other social sciences Decision model Database transaction 050203 business & management lcsh:Environmental sciences |
| Zdroj: | Sustainability, Vol 12, Iss 5606, p 5606 (2020) |
| ISSN: | 2071-1050 |
| Popis: | In this paper, we suggest a new methodology to identify promising technology areas by analyzing merger and acquisition (M&A) information. First, we present decision models for estimating the velocity and acceleration of M&A transactions to identify promising areas based on M&A information. Second, we identify the promising technology areas with longitudinal analyses of M&As over the entire period. Third, cross-sectional analysis is proposed to determine which technology areas are more promising through a relative comparison among technology areas within the IT sector for a specific period. The main significance of our research is that it is a prior data-based analytic method based on M&A transaction information to identify the growth of industry and technology. We hope this study will provide insights for R&D (Research&Development) policymakers and investment firms as a new approach that complements previous methods in exploring promising industry or technology areas. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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