PROJECT CHOICE AND RISK IN R&D*

We introduce stochastic R&D in the Hotelling model and show that if the technical risk is sufficiently high, all firms focus on the most valuable market segment. We then endogenize technical risk by allowing firms to choose between a safe and a risky R&D technology. Firms either both target the most attractive market with at least one firm using the risky technology or they choose different niche projects and both apply the safe technology. R&D spillovers lead to more differentiated R&D projects and patent protection to less. Project coordination within an RJV implies more differentiation, and may be welfare‐improving.     

R&D in markets with network externalities

This paper studies the consequences of network externalities on R&D rivalry between an incumbent firm and a potential entrant. In the model, all differences between the R&D projects chosen in market equilibrium and the socially best projects are solely due to network externalities. From a welfare perspective, the incumbent chooses a too risky and the entrant a too certain R&D project. Rothschild and Stiglitz's mean preserving spread criterion is used as a measure of risk. Adoption of a new standard is more likely in equilibrium than in the social optimum.     

International R&D project evaluation by multinational corporations in the electronics and IT industry of Singapore

This paper explores a number of variables associated with the evaluation of international R&D projects by multinational corporations (MNCs) in the electronics and IT industry of Singapore. Empirical analysis of the data collected from R&D managerial executives suggests a series of considerations in relation to their R&D investments. It is desirable if R&D can create a potential impact on the growth of their regional and international businesses. In considering the risks associated with an R&D project, a balance approach would be appropriate when demanding a return on investment. In particular, the consistence with customer demands, the achievement of time‐based competitiveness, the training of R&D manpower and the development of conducive innovation environments are fundamental to the success of international R&D projects.     

Stakeholder analysis for R&D project management

R&D projects affect the interests of different stakeholders in different ways. Understanding the stakeholders and analysing their interests helps in the better management of R&D projects. In this paper we discuss a methodology to systematically analyse the stakeholders of R&D projects. This methodology includes Freeman's (1984) three levels of analysis: rational, process and transactional. Based on these three levels, the stakeholder management capability of an R&D project is determined. The final stage is based on Mitchell et al . (1997) approach to analysing the dynamics of stakeholders. This methodology is illustrated using a New Zealand case relating to a road pricing R&D project.     

Government support of clean technology research in West Germany: some evidence

The authors have set out to review the procedures used in the allocation of public money by the Federal German government for research into 'clean' technologies. Public sponsorship of this kind of research is necessary as industrial firms rarely see enough return for their R&D investment to warrant their carrying it out. The authors have surveyed the methods used by the government funding agency (BMFT) to identify projects and to allocate money to them. 82 projects were included in the survey in the fields of low-emission processes.
Projects were segmented in five ways: Status of the receiver; Type of technology; R&D stage (from basic through demonstration plant); Cooperative versus non-cooperative, Environmental medium. Their broad conclusions are that (a) most of the funds went to projects for the development of 'end-of-the-pipe-cum-recycling' technologies and integrated technologies, (b) where there was cooperation between a firm and a university the R&D concentrated on studying basic principles, and (c) success depends on the existence of a clear strategy for the research as a whole rather than on ad hoc monitoring against a narrowly conceived plan. The authors also summarise the problems met in ensuring that money allocated in this way is effectively used.     

A portfolio–evaluation framework for selecting R&D projects

This study aims to form the basis for constructing a framework for evaluating alternative portfolios of R&D projects. This study provides an extensive literature review on portfolio selection. Most of the existing studies deal with the portfolio selection problem by evaluating individual projects and then seeking ways to combine them for an R&D portfolio. However, the combination of individually good projects unnecessarily constitutes the optimal portfolio. In particular, this study discusses three portfolio effects: (1) the difference between the preference for the portfolio as a whole and the preference for the projects, (2) the interrelation among projects, (3) the size of portfolio selection problems. This study develops a three–phase framework for evaluating R&D portfolios and proposes a new taxonomy of the portfolio attributes (i.e. independent, interrelated, and synergistic). This study concludes with a discussion of future research, directed toward increasing the applicability of portfolio–selection approaches for managing R&D portfolios.     

R&D as an option on market introduction

Discounted cash flow methods for making R&D investment decisions cannot properly capture the option value in R&D. Since market and technology uncertainties change expectations about the viability of many new products, the value of projects is frequently adjusted during the R&D stages. Capturing the adjustment in expectations has an option value that may significantly differ from the Net Present Value of R&D projects. However, there are no historic time series for estimating the uncertainty of the value of R&D projects. As a result, the standard Black and Scholes model for financial option valuation needs to be adjusted. The aim of this paper is to report the application of a particular option pricing model for setting the budget of R&D projects. The option value of the model captures jumps or business shifts in market or technology conditions. The approach originates from applying current insight into the valuation of R&D projects to the field of multimedia research at Philips Corporate Research. This way, the gap between real option theory and R&D practice is further diminished.     

Capital Budgeting Practices for R&D: A Survey and Analysis of Business Week's R&D Scoreboard

This paper discusses results of a survey of U.S. Corporations regarding the extent of the utilization of standard capital budgeting measures of investment worth to evaluate R&D projects. This study analyzes responses by three types of R&D activity: Basic, Applied and Development research. The results indicate that discounted cash flow methods are widely utilized in the evaluation of Development projects, and the analysis of Development projects is as sophisticated as the evaluation of non-R&D projects. The use of discounted cash flow analysis techniques for Applied projects is considerably less widespread than for Development projects. However, the utilization of Net Present Value and Internal Rate of Return increases with firm size. Basic research projects are much less likely to be subjected to a formal economic analysis and are generally thought of as being “strategic” investments. This research also indicates that post-audits of Applied and Development projects are based on economic measures, whereas Basic research projects focus on physical or operational goals. Lastly, this research indicates that most R&D projects are terminated because R&D priorities have changed rather than because they are not technically feasible.     

On allocating R&D budgets among and within projects

Choosing how to allocate resources to R&D projects is challenging, due to their uncertain success rates and achievement levels. We address the issue of allocating a given R&D budget among projects, and to parallel teams within each project. The achievement level of each team is assumed to be stochastically increasing in its funding level. We consider the objectives of maximizing: 1. the weighted sum of the probabilities that best team in each project achieves a threshold; 2. the weighted sum of expected achievement levels; and 3. the weighted sum of the expected number of teams attaining their threshold. Numerical results for a particular family of distributions are provided. The nature of the objective is seen to have a substantial impact on the optimal allocation.     

The economic impact of subsidized industrial R&D in Israel

Israel offers contingent subsidies to selected industrial R&D projects, with the purpose of creating high–quality jobs, reducing the trade deficit, increasing productivity and promoting growth. In 1987–94, 1,200 firms received $1,400 million of subsidies in support of $3,500 million of R&D (in constant 1996 dollars). We estimate that this R&D generated more than $31,000 million of sales, increasing industrial employment by about 10% and contributing to the trade balance a sum slightly less than the entire private sector deficit in the current account. It added 0.3% to GDP in increased productivity, each dollar of supported R&D adding an additional $0.45 to GDP and earning the economy a direct annual return of 13.4%. Electronics, broadly defined, received roughly half the subsidies while accounting for nearly two thirds of the gains; small firms that received one sixth of the subsidies contributed over a quarter of the gains.     

Influence of task type on the relationship between communication and performance: the case of software development

The relationships between communication patterns and performance of software development projects mostly resemble those of technical services, and not development projects, in hardware R&D. The local focus of software development projects in their information requirements is emphasized by the positive influence only of the informal and mostly internal literature, while external contacts, participation in conferences, and formal and external literature were inconsequential. The implications are two-fold: a) on the conceptual level they suggest that a trade-off between coordination and innovation requirements of the task might be an important determinant of optimal communication patterns; b) on the practical level it suggests that 'software development' consists of 'software engineering' and 'software production'. Consequently, it should be recognized that as such, these activities should be managed differently—the former as R&D, the latter as manufacturing.     

The Impact of Market Structure and Learning on the Tradeoff between R&D Competition and Cooperation

This paper compares R&D competition and cooperation when firms can devote resources to a ‘safe’ investment or a risky R&D investment. When the discovery of a new product creates positive externalities on non‐discovering firms, equilibrium investment flow, ex ante investment, and welfare under R&D competition are less than or equal to what they are under research cooperation. With negative externalities, R&D cooperation results in the same or lower ex ante investment than under R&D competition, and social welfare may also be less. Our results have relevance for empirical studies of the impact of R&D cooperation on R&D outcomes.     

Right person in the right place: How the host country IPR influences the distribution of inventors in offshore R&D projects of multinational enterprises

Research summary : Prior work has shown that the strength of the intellectual property regime (IPR) in a host country influences offshore R&D to that country. Building on this work we propose that the strength of the IPR in a host country differentially influences the threat of knowledge leakage on projects that are produced for the location where the multinational firm is headquartered (home) versus the offshore location to which the R&D project is sent (host). We argue and show that when the host location has a weak IPR, fewer host inventors are involved in host R&D projects when compared to home R&D projects. We test our hypotheses using a dataset of patents held by US assignees, but coinvented in 43 host locations with differing IPR strength. Managerial summary : Multinational enterprises often cite the weak IPRs at emerging economy host destinations as a significant impediment to offshore R&D activities in those countries, despite the abundant supply of inexpensive scientific talent there. We find that the weak IPR at the host destination is a greater impediment to offshore R&D that is aimed for end use at the host market than for R&D that is aimed for end use globally or in the home market. Since IPRs are local, a weaker IPR at the host location does not protect IP that is relevant to the host market. Since the IPR at the home country is more relevant for technologies aimed at the home market, the IPR at the host country is irrelevant for such R&D projects. Copyright © 2015 John Wiley & Sons, Ltd.     

Development and application of a new methodology of evaluating industrial R&D projects

This paper proposes a new methodology of evaluating industrial R&D projects to assess the effectiveness of future R&D in terms of financial credibility, to prioritize them efficiently by clear criteria to reduce the time and burden consumed by both project leaders and management staffs. A new methodology has been developed and applied to all Sumitomo Electric Industries (SEI) R&D projects, and is recognized as a useful system for evaluating many R&D projects in a short period, such as 150 projects per month.     

Principal researcher and project manager: who should drive R&D projects?

In this paper, it is analyzed the hypothesis that in R&D the principal researcher (PR) is accepted as the coordinator or project manager (PM), carrying out the search for financing and to manage contracts, resources, cost, time, scope, risk and uncertainty, communication, stakeholders and so on, in addition to internal research activities. Thus, this study tries to verify this hypothesis through a major literature review in different types of projects developed by university, but also with a look to industry and industry-university cooperation. Two case studies are also analyzed, centered in its R&D project management maturity level. It is concluded that there is an important issue in projects’ success and in the time spent by PR in management, work for which they are under trained; while at the industry there is a greater approach to project management by the proximity of the innovation projects to other industrial projects. Following these initial findings and according to the case study results, it is proposed that R&D Projects in universities would be separated into two synergistic knowledge areas: R&D Management and Project Management. It is also recommended to allocate them to two distinct roles, where they could add value to R&D through their better knowledge and skills.     

The organizational impact on R&D: Computer aids for the disabled

R&D often reflects social concerns which are represented in the organization and implementation of scientific inquiry. An analysis of R&D projects for the disabled provides a glimpse into the link between such social concerns and its consequent organizational basis.
A content analysis of 89 R&D computer-related projects between 1979 and 1983, focussing on R&D, interest areas, funding sources, research emphasis, institutional setting and prototype production, illustrates the organizational influence involved in this type of specialized R&D.
Specifically, a model is created, arguing that public funding mechanisms influence the type and direction that research takes, while factors in the form of institutional division of resources and specialization, lead to specific research concentrations. These findings add support to the argument that the organization of R&D of computer aids is influenced by societal priorities which, through a series of constraints, affects its output.     

Partner technological heterogeneity and innovation performance of R&D alliances

This study investigates the impacts of partner technology heterogeneity on innovation performance of alliance firms both in terms of R&D output and the enhancement of partners’ innovation capability. We apply a generalized semi-parametric model on a questionnaire survey result of 413 High and New Technology Enterprises in China. In order to ensure the robustness and practicability of our result, PCA is applied to extract comprehensive information and SiZer analysis is employed to test the linearity and significance of the nonparametric functions in the model. Our results indicate that collaborations between partners with different industry technologies exert inversed U-shaped R&D output pattern and affect very little the innovation ability of focal firms. The impact of industry domain divergence is no longer significant when partner technology heterogeneity is added in the model of analysis. Partner technology heterogeneity leads to an ascending S-shaped R&D output pattern and contributes positively to innovation capabilities. One of the implications of our findings is that when choosing R&D alliance partners, firms are better off avoiding candidates from a different industry domain but opt for potential collaborators who are in the same industry but in the different technical domain, which may facilitate more effective organizational learning. Further, we argue that the reasons behind the S-shaped R&D output pattern led by technology heterogeneity being the co-existence of competition and cooperation between partners where firms collaborate in value-creation by combining diverse resources and compete for acquiring partner’s distinct technology and resources. Therefore, we suggest that, for the sustainability of collaborative innovation outcome, both cooperation and competition amongst alliance partners should be encouraged and well balanced at different stages of joint R&D projects.     

Networks and the Decision to Persist with Underperforming R&D Projects

Drawing on aspiration level theory and the networks literature, this paper develops a model toward decision makers' persistence with underperforming R&D projects based on the perceived properties of their personal network. The assumption is that positive feedback from network partners motivates persistence, and that this effect is multiplied when the decision makers' network is larger and denser, when network ties are predominantly strong, and when communication frequency within the network is high. The model is tested by a field experiment and conjoint methodology. Analysis of 1632 persistence decisions nested within 51 scientists responsible for R&D projects reveals that more positive feedback enhances persistence of underperforming R&D projects, and that this effect becomes stronger with increasing network size, network density, and communication frequency. These findings extend the project management literature by focusing on the social environment of the decision maker as one so far neglected factor in empirical studies on persistence decisions. Moreover, this study is among the first to investigate contingency relationships between feedback received from network partners and network structure. Finally, while most studies have emphasized that individuals and organizations can profit from their engagements in social networks, this paper suggests a potential dark side of networks by showing that networks can encourage decision makers to persist with their investment in an underperforming—and potentially failing—project. These results can help project managers involved in networking activities to better understand the effects of these activities on their decision policies, and thus draw better and more accurate decisions. Second, this study provides insights into how organizational strategy with respect to dissemination of R&D results and involvement of the organization's R&D managers in the scientific community influences the persistence of underperforming R&D projects, and thereby the organization's R&D expenditures. This can assist top managers to design financial controls and allocate financial resources to managers of underperforming R&D projects in line with the organization's networking strategy.     

Valuation of venture capital investments: empirical evidence

Using the valuation data of 421 US venture capital transactions and 176 initial public offerings, we test a simple binomial valuation model in modelling the risk‐return profiles of venture capital investments. We find that the model is consistent with the previous knowledge on the risk‐return profile of venture capital investments. The results also confirm the hypotheses that early‐stage ventures have higher implied risk and implied volatility of the returns than more established ones.
Additionally, we analyse the predictive power of the binomial pricing model and compare it to corresponding 'traditional' models that utilize risk‐adjusted rates of return. We construct one‐step ex post return forecasts for the sample ventures and compare the results to the actually realized returns. The findings indicate that the fit of the binomial model is better than the fit of the corresponding 'traditional' models.
The results imply that option‐based methods have empirical relevance in the pricing analysis of privately held companies and projects. Furthermore, practitioners can benefit from using these methods when analysing the risk‐return structure of private companies and R&D projects.     

Optimizing the capital rationing decision with uncertain returns

In this article, we develop a new optimization model for capital rationing with uncertain project returns. Our model maximizes the probability of meeting a predefined target return by selecting a feasible set of projects subject to budget constraints in multiple time periods. We employ a mixed-integer nonlinear algorithm recently developed in the optimization field to solve the resulting nonconvex optimization problem to optimality. Our model and solution methods are tested and validated through a comprehensive computational experiment. Several managerial insights are obtained about the impact of available budget and target return on the optimal solutions. Notably, we have found that increasing target return may not necessarily result in an increase in optimal total expected return of the selected projects. Our model and solution method offer a unified and computationally tractable approach to precisely quantify the tradeoff between project returned and risk.