Optimal R&amp;D investment for a risk-averse entrepreneur

Whalley, A. Elizabeth

doi:10.1016/j.jedc.2009.11.009

Cited by 20 publications

(4 citation statements)

References 23 publications

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“…To cite a few, Bhaskaran and Krishnan (2009) incorporate R&D uncertainty into the decision‐making model and evaluate the implications of revenue sharing, cost or effort sharing, and innovation sharing contracts while entering partnerships. Whalley (2011) demonstrates that R&D uncertainty is associated with the difficulty of completing R&D, unaffected by economy‐wide uncertainty, and so, it cannot be hedged. They consider an infinite‐horizon continuous‐time model under R&D uncertainty and examine a risk‐averse enterprise's optimal decision on whether and how much to invest in R&D at each point in time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…incorporateR&D uncertainty into the decision-making model and evaluate the implications of revenue sharing, cost or effort sharing, and innovation sharing contracts while entering partnerships Whalley (2011). demonstrates that R&D uncertainty is associated with the difficulty of completing R&D, unaffected by economy-wide uncertainty, and so, it cannot be hedged.…”

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confidence: 99%

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To ally or not to ally: Enterprises' preferences for technology alliance modes in transnational cooperation

Zhou

Cai

et al. 2023

Manage Decis Econ

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When entering a foreign market, transnational enterprises often conduct research and development (R&D) activities to develop patent technologies and then produce products to meet the technology standard of the target market. Technology alliance is an efficient way to enhance the enterprise's capability of R&D. We assume that the R&D process is uncertain and then construct four models based on different alliance modes. We further study the impacts of different factors on enterprises' expected profits and then investigate enterprises' preferences for alliance modes. Our study can provide decision support for transnational enterprises going abroad and enterprises in foreign markets on their choices of technology alliance modes.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

To ally or not to ally: Enterprises' preferences for technology alliance modes in transnational cooperation

Zhou

Cai

et al. 2023

Manage Decis Econ

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“…In general, authors focus on the conception of technological innovations, their efficient models in different industries, and the aspects of value creation for the company. From a variety of optimal timing studies, different types of decision support models have been investigated by authors, such as Bar-Han & Maimon (1993), Benaroch and Kauffman (1999), Krušinskas and Vasiliauskaitė (2005), Kamarianakis and Xepapadeas (2006), Mukherji et al (2006), Ngwenyama et al (2007), Huang and Da (2007), Wickart and Madlener (2007), Pertile (2007), Wong (2010Wong ( , 2011, Moon (2010), Henderson (2010), Shibata and Nishihara (2011), Whalley (2011), Yagi and Takashima (2012, Bolton et al (2013), Wong and Yi (2013), Nishihata andShibata (2013), Feil andMusshoff (2013), Kim, Lee and Sohn (2014), Jeon and Nishihara (2014), and others.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile 5g Technology Adoption Investment Timing Decision Makers Clustering and Willingness to Invest Under Volatile Demand Changes

Adamauskas

Krušinskas

2016

CBUP

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Enterprises need to identify the optimal timing for technological change in order to increase competitiveness and increase the value of the company in an uncertain demanding environment. Investment decisions for adopting new technologies are costly and sometimes risky because technological investments are irreversible. To simulate the process, comprehensive technological adoption regarding investment timing was used in a management decision support model. The constructed model is structured as follows: 1) historical demand paths analysis; 2) application of statistical data validity tests; 3) the forecast of market parameters regarding data arrays using the geometric Brownian motion method, based on Monte Carlo simulation; 4) determination of technological life cycle using a Hodrick-Prescott filter; 5) technological adoption time-window determination; and 6) calculation of company net present values (NPV) based on change in free cash-flow. The model for mature 5G mobile markets, created and empirical tested, was performed in relation to 18 largest Europe mobile service providers, as potential decision makers operating across 33 countries. Results confirmed that selection of the technological investment time depends on companies' strategic financial decisions and financial state. The performed simulations revealed the consequence of 5G technology investment for investor roles, clustered according to financial data within a 5-year period (2010)(2011)(2012)(2013)(2014). The analyzed companies were assigned to roles of pioneers-innovators, pragmatics, followers, or laggards. Finally, it is assumed and argued that financial parameters indicate the willingness to adopt new technologies in a global technologically changing environment. JEL Classification Numbers: O16, O32, O33 DOI: http://dx

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“…Then, Pennings and Lint [15] proposed a stochastic jump amplitude model to narrow the gap between the theory and the practice of decision making on investment in an R&D project with technical uncertainty. In 2010, Whalley [23] developed a model for entrepreneurs who invest in risky assets as well as R&D projects under various risk aversion. Pennings and Sereno [16] developed a real option model incorporating both the technical and economic uncertainties to evaluate a pharmaceutical R&D project in 2011.…”

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confidence: 99%

Optimal product release time for a new high-tech startup firm under technical uncertainty

Wang

Huang

O’Regan

2023

JIMO

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<p style='text-indent:20px;'>Decision makers of new high-tech startup firms always want to choose an optimal time to launch their products which are under research and development (R&D) to obtain the maximum net income of these firms. However, existing models fail to consider the optimal release time of products for these new high-tech startup firms. In this paper, the optimal time to launch the product of the R&D project is assumed to be the first time when the product of the R&D project is released to the market. Based on this assumption, we develop a continuous-time model to find the optimal time at which the startup firm launches its product of the R&D project by considering the price of the similar product. Employing the methods of dynamic programming and variational inequalities, we also provide a closed form solution to our model. We also find that these high-tech startup firms prefer to delay their product release time when the price of the similar product is at a phase of rapid growth or the price has considerable uncertainty. Moreover, some numerical examples are provided to investigate the properties of our model.</p>

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Optimal R&D investment for a risk-averse entrepreneur

Cited by 20 publications

References 23 publications

To ally or not to ally: Enterprises' preferences for technology alliance modes in transnational cooperation

To ally or not to ally: Enterprises' preferences for technology alliance modes in transnational cooperation

Mobile 5g Technology Adoption Investment Timing Decision Makers Clustering and Willingness to Invest Under Volatile Demand Changes

Optimal product release time for a new high-tech startup firm under technical uncertainty

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