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Enregistrement W206751856

Aging Population and Innovation Skills

2009· article· en· W206751856 sur OpenAlexaff
Marie Lavoie

Notice bibliographique

RevueSSRN Electronic Journal · 2009
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueRegional Development and Policy
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésWorkforceContext (archaeology)Innovation managementCommercializationPopulation ageingPopulationDependency ratioBusinessQuality (philosophy)Industrial organizationMarketingEconomicsEconomic growthSociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The article addresses the integration of “innovation” and “active aging” strategies. The goal is to develop a framework that identifies the skills pertaining to mature workers in a context of innovation. It should also provide direction for public policies and corporate practices to take advantage of the innovation potential of this workforce. The “active aging” strategy could have a positive effect on the innovation capacity of industrialized countries. This strategy refers to a process aiming to optimize the conditions to improve the quality of life during old age. Borrowing from the Schumpeterian approach and based on the third edition of the Oslo Manual, the concept of innovation is broadened from the standard definition. Three stages of technological change are identified: invention, innovation, and diffusion. As a result, the innovation process involves more than science and technology activities. It includes peripheral activities, such as commercialization and innovation management. Very little is known about the impact of the aging workforce on innovation. If there is a relationship between age and innovation, it should appear at the macroeconomic level. Countries with a higher dependency ratio should demonstrate a lower technology intensity level. Correlating these indicators (that is, dependency ratio and technological intensity (R&D/GDP)) for some OECD countries, it is found that no relationship holds. Going further and examining countries for which cohorts of 45 years and older are more educated, there is no more significant inclination to innovation either. More emphasis on innovation skills is therefore essential to understand this relationship. Moreover, despite the popular belief that skills of the mature workforce tend to decline over time, research from many disciplines acknowledges that it is not the case. The cognitive atrophy would be more likely related to the exclusion of workers from the labour market than due to the obsolescence of their skills. Two categories of skills - fluid and crystallized - seem to decline at a different pace over time. While the first category is most likely to face an age-related decline, the second is resistant to the aging process. In this paper, we are wondering if the mature workforce owns specific and exclusive innovation skills. We assume that metacognitive abilities (that is, the combination of ‘expert thinking’ and ‘complex communication’) are relevant in the case of older workers and that they could play a strategic role in the innovation process. However, innovation skills are not fully understood and much work remains to be done to identify these skills. Too often, innovation skills are confused with foundational skills. The poor state of knowledge of innovation skills makes it difficult to assess the contribution of the aging population. On the basis of the Schumpeterian approach and the novelty concept from the Oslo Manual, we disaggregated innovation activities into three categories and provided a framework enabling the identification of skills related to these activities. It allows us to address the potential contribution of aging workers in very specific contexts. The first scenario refers to the generation of scientific and technological ideas leading to invention. Depending on either the incremental or radical nature of change that induces an invention, older scientists and engineers can play a substantial role in these activities. It refers to the “new-to-the-world” level of novelty concept. For example, one can mention applied mathematical reasoning skills to solve a variety of problems in the innovation process. The adoption and adaptation of technologies developed outside a firm are core activities of the second scenario, drawing on the “new-to-the-firm” concept of novelty. The adoption of Information and Communication Technologies in a firm is a good example of innovation activities involved in this scenario. Generally, an aging population is accused of being weakly endowed with these skills. Finally, the third scenario looks at management activities, such as human resources management, intellectual property, marketing, and so on. An older population may have an advantage over younger people in these activities given the length of their experience. In any event, it appears that government and firms have a crucial role to play to encourage older workers to remain longer in the labour market. Some public policies and corporate initiatives that enable the retention of these workers in the labour market are outlined for each scenario. For example, many OECD countries reformed their pension system, one of the most popular incentives to keep the aging workforce active in the labour market. Lifelong learning strategy is another initiative developed by numerous countries that allows workers to maintain and develop skills during their careers. Incentives for employers and employees are also worth considering for encouraging workers to attend training sessions, especially older workers for whom frequent discrimination due to age exists but must be prevented. Another initiative concerns the transfer of skills from one generation to another using mentoring or professional coaching, which consists of providing guidance in solving complex problems and acquiring knowledge and skills. OECD countries do not necessarily apply the same policies and programs. However, only a concerted strategy between workers, employers, and government in a specific country can suppress the barriers and increase incentives to encourage older workers to continue working and contribute to innovation.

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.

Comment cette classification a été obtenuedéplier

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)0,001
score de la tête « metaresearch » (Gemma)0,000
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: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,116
Score d'incertitude au seuil0,330

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,011
Tête enseignante GPT0,316
Écart entre enseignants0,305 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2009
Routes d'admission1
Résumé présentoui

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