@article{4822,
  author = {Ngobum Chinwengozi Ibe (Mrs)},
  title = {Universal Convergence in OECD R&D Researcher Capacity: Persistence, Selective Catch-Up, and Multiple Trajectories (2015-2024)},
  journal = {Journal of Information & Systems Management},
  year = {2026},
  volume = {16},
  number = {3},
  doi = {https://doi.org/10.6025/jism/2026/16/3/126-156},
  url = {https://www.dline.info/jism/fulltext/v16n3/jismv16n3_3.pdf},
  abstract = {This study examines the convergence, persistence, catch up dynamics, and heterogeneous trajectories of
R&D researcher capacity across OECD and selected partner economies during 2015-2023. Using an integrated
analytical framework combining  convergence, absolute  convergence, the Phillips Sul log-t test, Markov
transition analysis, trajectory clustering, compound annual growth rate (CAGR) analysis, and a growth
level typology, the study investigates whether national researcher stocks move toward a common long run
equilibrium or instead follow differentiated development paths. The results decisively reject universal
convergence: cross country dispersion did not decline systematically, the  -convergence coefficient was
statistically insignificant ( = +0.000599, p = 0.867), and the Phillips Sul test rejected overall convergence
(t = -22.002). Markov transition analysis revealed strong structural persistence, with probabilities of
remaining in the same capacity state ranging from 74.4% to 94.1%. Nevertheless, the study identified selective
catch up: Poland, Türkiye, Hungary, Slovenia, and Croatia showed rapid researcher capacity growth and
formed a statistically distinct trajectory cluster. The growth level typology classified countries into four
regimes: strategic leaders, mature leaders, catch up economies, and persistent laggards. The study concludes
that R&D researcher capacity constitutes a multi regime system characterized by simultaneous persistence,
selective catch up, and multiple trajectories rather than a single convergence process. These findings carry
significant implications for differentiated science, technology, and innovation policy design.},
}