“LLMs as a New Species: Evolutionary Perspectives on Artificial Intelligence, Innovation, and Socio‑Technical Ecosystems”

Call for Papers – “LLMs as a New Species: Evolutionary Perspectives on Artificial Intelligence, Innovation, and Socio‑Technical Ecosystems”

Journal: Technovation
Publisher: Elsevier
Journal metrics: Impact Factor 10.9, CiteScore 19.5
Submission window: 1 August – 30 November 2026

This special issue reframes large language models (LLMs) as a new “species” within innovation and socio‑technical ecosystems, inviting research that adopts evolutionary and ecological perspectives to understand how LLMs emerge, adapt, and interact with firms, industries, and societies. It seeks interdisciplinary work that treats LLMs not merely as tools, but as adaptive, evolving actants that reshape organisational capabilities, innovation processes, governance, and human–AI collaboration.

Why this issue matters

• LLMs and personalised AI agents are transforming how organisations generate ideas, allocate resources, make decisions, and interact with employees and customers, yet much of the literature still focuses on performance metrics and narrow applications.

• By applying evolutionary and organisational‑ecology concepts—such as variation, selection, retention, speciation, symbiosis, and co‑evolution—this SI aims to build richer, theoretically grounded accounts of how LLMs evolve, specialise, and stabilise within complex socio‑technical systems.

• The issue encourages both constructive and critical engagement with biological metaphors: when they clarify AI dynamics, and where they risk obscuring power asymmetries, path dependence, and systemic vulnerabilities.

Core themes and research questions

Submissions may be conceptual, empirical, critical, computational, or methodological, and may address one or more of the following areas:

• Evolutionary mechanisms in LLM development and deployment

  • How can variation, selection, and retention be operationalised across data, architectures, alignment regimes, and deployment environments?

  • What forces drive the “speciation” of LLM lineages (e.g., domain‑specific models, specialised agents, vertical‑market embeddings)?

• Organisational and innovation implications

  • How do LLMs change firm‑level innovation processes, including idea generation, experimentation, and search routines?

  • How do dynamic capabilities, resource dependence, and behavioural‑theory‑of‑the‑firm frameworks need to be revised in AI‑intensive environments?

• Socio‑technical ecosystems and hybrid intelligence

  • How LLMs occupy, transform, and compete for ecological niches alongside other AI systems, digital platforms, and human actors.

  • Emergence of hybrid or distributed intelligence in teams, firms, and innovation ecosystems.

• Governance, policy, and vulnerabilities

  • How governance structures, norms, and regulations shape variation, selection, and retention in LLM ecosystems.

  • Systemic risks such as lock‑in, fragility, path dependence, and “extinction‑like” dynamics in LLM‑based infrastructures.

• Critical and methodological contributions

  • Papers that test, extend, or problematise evolutionary and ecological analogies for LLMs, with empirical or formal evidence.

  • Methodological innovations using simulation, network analysis, bibliometrics, or computational‑modelling approaches to trace LLM lineages and co‑evolutionary trajectories.

Guest editors

• Dr Miles M. Yang, Macquarie University, Australia

• Dr Richard “Rick” Hunt, Virginia Tech, USA

• Dr Ying “Candy” Lu, Macquarie University, Australia

• Dr Lara Khansa, Virginia Tech, USA

• Dr Matthias A. Tietz, University of St. Gallen, Switzerland

Submission details

• Submission platform: Elsevier Editorial Manager for Technovation:
  https://www.editorialmanager.com/technovation/default2.aspx

• When submitting, select article type “VSI: LLMs New Species”.

• Submission window: 1 August – 30 November 2026

• Anticipated publication: Mid–late 2027

• Accepted paper‑development workshop (optional, face‑to‑face or virtual): Summer 2026 (details via guest editors).

• Word‑length guidelines:

  • Full articles: 8,000–10,000 words

  • Research notes / methods papers: 4,000–6,000 words

  • Perspective / critical essays: 3,000–5,000 words

• All submissions will undergo double‑blind peer review; transparency in data, code, or materials is encouraged.

This SI is ideal for innovation, organisation‑theory, AI‑ethics, science‑&‑technology‑policy, and information systems scholars who wish to push beyond instrumentalist views of LLMs and develop more robust, evolutionary‑grounded understandings of AI‑driven innovation and socio‑technical change.

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