Assessment of companies practices concerning the evaluation of R&D investment projects
Bibliographic record
Abstract
This study introduces an on-going research project aimed at analyzing the impact of R&D projects both from the public and private points of view. From the public perspective the social impacts and objectives of these projects, frequently supported by National or European R&D programmes, should be underlined and properly considered in the evaluation process. On the other hand, the private perspective emphasizes mainly financial and strategic returns for the companies involved in research projects. This paper addresses part of the research conducted so far, focusing in particular on the private perspective, namely on the identification of the more appropriate methods for the evaluation of research and development (R&D) investments projects by companies. Several studies indicate that the use of traditional financial methods is not the most appropriate for evaluating R&D projects (see, for example, Chan, 2001, Proctor and Canada, 1992, and Mensah and Miranti, 1989). The use of these methods consists, basically, on discounting the expected future cash flows and the adoption of several methods for measuring its financial viability (e.g. NPV, IRR). This implies that the costs and benefits associated with the investment are easily and objectively quantified. Nevertheless this is not always possible for all types of investments, particularly in the Advanced Manufacturing Technology (AMT), in Information and Communication Technologies (ICT), or in projects of R&D. For these type of investments, the estimation of financial flows and the assessment of their risks tend to be different from general tangible investments. This is particularly important in the calculation of benefits, which can be of three types: strategic, quantifiable and intangible. For example, the intangible benefits are difficult to quantify but may have a significant impact on return on investment (Adler, 2000). Moreover, has been witnessing an increasing trend for companies to include non-financial dimensions/variables (e.g. strategy, flexibility and quality) of the problem in their decision-making process on investment projects. Indeed, these non-financial aspects are particularly important in the new industrial environment in which companies today operate, where new technological developments tend to occur more rapidly than the development of methods for the evaluation of investment projects (Brownell and Merchant, 1990). One of the objectives of this paper is to present an up-to-date state-of-the-art regarding the non-financial techniques that have been proposed to evaluate investment projects in research and development (R&D). Indeed, the evaluation of such projects, although often conducted purely in the perspective of business profitability, cannot be reduced to a simple analysis of discounted cash flows, since these projects often provide strategic gains that could hardly be translated into quantifiable monetary benefits in the short term. Moreover, there are also other factors that are difficult to measure/quantify, such as: political issues, environmental impacts, knowledge, intuition, or experience. As a result, this study discusses several non-financial criteria to be considered in the evaluation of R&D projects, which have been proposed in the literature related to products manufacturing, environmental, employment, users of the results of R&D, competitiveness of technology, relevance of technology, economic benefit, social benefit, quality of technical plan, availability of resource, technical risk, development risk, commercial risk, and return of investment. A second objective is to conduct an inquiry to a sample of metallurgical companies in northern Portugal and southern Galicia in order to assess which practices have been used to evaluate R&D investments. In fact, this would allow us to gain an insight concerning both the financial and non-financial criteria used and if they use software support, as well as the importance of certain non-financial criteria in the evaluation of projects. Moreover we are trying to see if they currently use any of the multi-criteria methods. The ultimate goal of this research would be to propose an integrated methodology that can be applied to the evaluation of R&D projects, based on financial and non-financial methods using multi-criteria techniques. The purpose of multi-criteria models is to break a complex problem into simpler parts. That allows the decision-maker to structure a problem with many criteria in a visual way, through the construction of a hierarchical model that basically contains three levels: aim or objective, criteria and alternatives. Once the model is built, two by two comparisons are made between these elements (criteria-subcriteria and alternatives) and numerical values are given to the preferences assigned by individuals, therefore obtaining a summary value by aggregating this partial judgements (Rodríguez, 2008).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.112 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".