“Text as Knowledge for Innovation Management: Ensuring Research Relevance and Rigor with NLP and LLMs”
DETAILS
Call for Papers – Special Issue: “Text as Knowledge for Innovation Management: Ensuring Research Relevance and Rigor with NLP and LLMs”
Journal: Technovation
Publisher: Elsevier (ScienceDirect)
Impact Factor: 10.9 | CiteScore: 19.5
Submission deadline: 31 March 2027
Overview
This special issue investigates how Natural Language Processing (NLP) and Large Language Models (LLMs) can treat text as knowledge across the innovation lifecycle. From idea generation to commercialisation, organisations increasingly rely on textual data (publications, patents, reports, social media, internal documents) to detect opportunities, evaluate ideas, and guide NPD. The journal invites empirical, methodological, and conceptual work that critically examines how NLP/LLMs:
Analyse codified knowledge,
Generate new knowledge, and
Operationalise theoretical constructs from text in innovation management research and practice.
The goal is to advance both theoretical understanding and practical rigor, ensuring these tools are deployed thoughtfully across sectors and contexts.
Core Themes
1. NLP and LLMs across the innovation process
Opportunity identification & idea generation:
Using NLP on patent, social media, or open‑innovation platforms to detect emerging opportunities, weak signals, and user‑generated ideas (e.g., user‑feedback, forum posts).
LLM‑augmented brainstorming and co‑creation sessions (e.g., generative AI as idea partner).
Idea evaluation & selection:
Measuring novelty, creativity, and feasibility via semantic similarity, embeddings, or topic‑modelling.
Using LLMs for predictive assessment of business‑model viability or market potential.
Concept & solution development:
NLP‑based problem–solution matching through patent or technical‑document analysis.
Generative LLMs for rapid prototyping of digital features, user‑flows, or service designs.
Commercialisation & launch:
Sentiment and narrative analysis of customer feedback, reviews, and social‑media reactions.
Adaptive marketing and communication strategies informed by continuous text analytics.
2. Construct operationalisation and theory development
Turning text into measurable constructs (e.g., innovation leadership, creativity, team dynamics, user engagement) via:
Keyword‑based metrics,
Semantic‑similarity methods,
Topic‑modelling,
Supervised‑ML and transformer‑based embeddings.
Empirical studies that:
Test innovation theories using text‑derived variables.
Examine co‑evolution of language, innovation, and organisational behaviour across phases.
3. Methodological rigor and critical usage
When traditional NLP outperforms LLMs (e.g., keyword‑based or BERT‑style models are more transparent, efficient, or interpretable).
Bias, over‑reliance, and “hallucinations”:
Bias‑mitigation in idea evaluation, selection, and market‑simulated experiments.
Ensuring human‑centred innovation despite AI‑accelerated workflows.
Evaluation frameworks for NLP/LLM performance in innovation tasks (e.g., metrics, robustness checks, benchmark corpora).
4. Conceptual and methodological contributions
New frameworks for embedding text‑as‑knowledge into innovation management models (e.g., open‑innovation, design‑driven innovation, AI‑augmented design‑thinking).
Guidelines for:
Transparent and explainable use of LLMs in innovation.
Qualitative–quantitative mixed‑methods that combine close‑reading with large‑scale text mining.
Suggested Research Questions
Submissions may address (but are not limited to):
How can firms systematically scan textual knowledge for emerging opportunities and weak signals using NLP/LLMs?
How does integrating LLMs into creative processes redefine the notion of creativity and ideation in teams?
What bias‑mitigation or fairness strategies are needed when LLMs evaluate and rank business ideas?
Can LLM‑based simulated markets or virtual experiments test the potential of emerging ideas more efficiently?
Can NLP/LLMs bridge disciplinary silos between science, engineering, and management, enabling richer knowledge integration?
How can we ensure AI‑accelerated development remains aligned with experimentation, learning, and ethical reflection?
How do public narratives about a product (from social media, news, forums) evolve over time and shape market reception?
Can LLM‑driven launch strategies adapt in real time to sentiment and user interaction?
When are traditional NLP tools preferable to LLMs (and vice versa) for specific innovation tasks?
What evaluation standards and validation protocols are appropriate for NLP/LLM‑based innovation research?
Submission Details
Submission window:
Submission open: 1 March 2026
Submission deadline: 31 March 2027
Platform: Submit via Technovation’s Editorial Manager:
https://www.editorialmanager.com/technovation/default.aspxWhen submitting, select article type “VSI: NLP & LLMs for Innovation Management”.
Follow the journal’s Guide for Authors:
https://www.sciencedirect.com/journal/technovation/publish/guide-for-authors
Guest Editors
Dr. Vito Giordano, University of Pisa, Italy
Prof. Filippo Chiarello, University of Pisa, Italy
Dr. Ivan Zupic, Goldsmiths, University of London, UK
Prof. Catherine Beaudry, Polytechnique Montréal, Canada
Dr. Julian Just, University of Innsbruck, Austria
Prof. em. Wim Vanhaverbeke, University of Antwerp, Belgium
Why This Issue Matters
Innovation management is inherently text‑rich; yet many methods still treat documents as background, not as systematically analyzable knowledge.
NLP and LLMs enable automated extraction, structuring, and synthesis of this knowledge at scale, but also introduce risks of bias, opacity, and methodological complacency.
This special issue pushes the field toward rigorous, responsible, theory‑grounded use of text‑as‑knowledge tools, aligning technological potential with sound innovation‑management science.
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