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Record W7111458137

Assessment of companies practices concerning the evaluation of R&D investment projects

2009· article· en· W7111458137 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2009
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)Identification (biology)Cash flowDiscountingPerspective (graphical)EstimationInformation technology
DOInot available

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.155
GPT teacher head0.444
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it