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

2009· article· en· W7111458137 sur OpenAlex

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Notice bibliographique

RevuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2009
Typearticle
Langueen
DomaineEngineering
ThématiqueTechnology Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInvestment (military)Identification (biology)Cash flowDiscountingPerspective (graphical)EstimationInformation technology
DOInon disponible

Résumé

récupéré en direct d'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).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,433
Score d'incertitude au seuil0,499

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,155
Tête enseignante GPT0,444
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle