MétaCan
Menu
Back to cohort
Record W206751856

Aging Population and Innovation Skills

2009· article· en· W206751856 on OpenAlexaff
Marie Lavoie

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsWorkforceContext (archaeology)Innovation managementCommercializationPopulation ageingPopulationDependency ratioBusinessQuality (philosophy)Industrial organizationMarketingEconomicsEconomic growthSociology
DOInot available

Abstract

fetched live from 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.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.316
Teacher spread0.305 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicRegional Development and PolicyFrench-language works237,207