The digital economy represents major challenges for established corporations around the world. To succeed in this rapidly changing environment, it is no longer sufficient to compete by incremental product or process innovation. Achieving sustainable competitive advantage requires managers to exploit the disruptive potential of emerging technologies to transform business models, value chains or entire markets. We define Digital Transformation of Business Models (DTBM) as a radical Business Model Innovation (BMI) driven by digital technology. It is a necessary tool to adapt established businesses to the paradigmatic shifts induced by the digital economy. In this context, managers are facing the challenging task of identifying and making the right investment decisions. Standard project valuation models such as Discounted Cash Flow (DCF) methods do not suffice in coping with the uncertainty surrounding the strategic nature of DTBM projects. Many scholars postulate Real Option Analysis (ROA) to be applied as a sophisticated alternative for investment decision-making under uncertainty. However, existing research tells us little about how to apply ROA to BMI or DTBM. In this paper, we present a first generic approach to applying ROA to value investments in DTBM. We include two types of managerial flexibility by a analyzing a compound learning option and a Bermudan-style option to expand from a pilot project into a large-scale DTBM. We consider four sources of uncertainty and show that the value of managerial flexibility is particularly high in DTBM projects, leading to potential shifts investment decisions. This paper is aiming to highlight the importance of this area, apply existing real options models to the DTBM setting and lay the foundations for future research.

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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.

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