TY - JOUR
T1 - Estimating parameters for technology investments
T2 - An application to 3d printing
AU - Schneider, Robin
AU - Hirakawa, Hitoshi
AU - Hosoda, Noboru
AU - Jin, Rong
AU - Imai, Junichi
N1 - Funding Information:
All authors are very grateful to anonymous reviewers and the Associate Editor for their helpful suggestions. The authors also thank conference participants at the Annual International Real Options Conference in 2018 and 2019 and the Annual Conference of the Japanese Association for Real Options and Strategy in 2019 for many constructive comments. This research is supported by JSPS KAKENHI Grant Number YYKKB09 and the Research Grant of Keio Leading-edge Laboratory of Science and Technology.
Publisher Copyright:
© 2021 Operations Research Society of Japan. All rights reserved.
PY - 2021/7/31
Y1 - 2021/7/31
N2 - One of the major limiting factors and criticism about the real options approach is related to issues with estimating the right input values for state variables that are critical to make the right investment decisions under uncertainty. While vast research exists that applies real options valuation to technology investments, scholars often present theoretical findings based on fictional numerical applications neglecting the process of estimating the right input variables for their models. We present a simple framework to obtain these variables for technology investments by analysing publicly available data such as bibliometrics and patents related to any technology and apply it to forecast 3D printing technology diffusion. We base our approach on the Bass model, which is a prominent technique in the area of technology forecasting and show that these methods can help to forecast technology diffusion and obtain the required input parameters for technology investment decisions. We further use our 3D printing example to demonstrate the major differences between the suggested technology diffusion model and a standard Geometric Brownian Motion (GBM) model, as it is often found in Real Options literature. We find that the GBM is often not suitable when analysing technology investments, as it can lead to wrong investment decisions.
AB - One of the major limiting factors and criticism about the real options approach is related to issues with estimating the right input values for state variables that are critical to make the right investment decisions under uncertainty. While vast research exists that applies real options valuation to technology investments, scholars often present theoretical findings based on fictional numerical applications neglecting the process of estimating the right input variables for their models. We present a simple framework to obtain these variables for technology investments by analysing publicly available data such as bibliometrics and patents related to any technology and apply it to forecast 3D printing technology diffusion. We base our approach on the Bass model, which is a prominent technique in the area of technology forecasting and show that these methods can help to forecast technology diffusion and obtain the required input parameters for technology investment decisions. We further use our 3D printing example to demonstrate the major differences between the suggested technology diffusion model and a standard Geometric Brownian Motion (GBM) model, as it is often found in Real Options literature. We find that the GBM is often not suitable when analysing technology investments, as it can lead to wrong investment decisions.
KW - Bass model
KW - Forecasting
KW - Logistic growth curve
KW - Parameter estimation
KW - Real Options Analysis
KW - Technology diffusion
KW - Technology investment
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U2 - 10.15807/jorsj.64.129
DO - 10.15807/jorsj.64.129
M3 - Article
AN - SCOPUS:85114640021
VL - 64
SP - 129
EP - 157
JO - Journal of the Operations Research Society of Japan
JF - Journal of the Operations Research Society of Japan
SN - 0453-4514
IS - 3
ER -