TFSC Paper Development Workshop 2026

Call for Papers Special Issue

The field of technology management stands at a profound inflection point, where the exponential growth of unstructured data intersects with revolutionary advances in artificial intelligence. Large Language Models (LLMs) represent more than just another analytical tool in our methodological arsenal - they constitute a fundamental shift in how we engage with knowledge itself. This Special Issue specifically calls for research that applies LLMs as novel conceptual lenses and methodological tools to address core questions in technology management. We seek articles that go beyond technical application, aiming to generate substantive theoretical contributions and advance the foundational theories of our field.

For decades, technology management has grappled with the challenges of information overload, fragmented knowledge sources, and the inherent uncertainty of technological evolution (Porter & Cunningham, 2004; Porter & Detampel, 1995; Rotolo et al., 2015). Traditional methods, while valuable, often struggle to capture the nuanced narratives, emerging patterns, and subtle interdependencies embedded in scientific literature, patent landscapes, market discourse, and social conversations. LLMs offer a transformative bridge across this gap. Their capacity for deep semantic understanding (Wu et al., 2025a; Wu et al., 2025b), contextual reasoning (Hoffmann et al., 2024; Jia et al., 2025), and generative synthesis (Chen et al., 2026; Guo et al., 2026) allows us to move beyond surface-level metrics toward a more holistic, interpretive, and forward-looking engagement with technological change, which is one of the key foci of the TFSC community (Grybauskas & Cardenas-Rubio, 2024; Li et al., 2026; Park et al., 2026). At the same time, recent critical work highlights that the performance, sensitivity, and bias of general-purpose LLMs are highly task- and context-dependent, underscoring the importance of model selection, specialization, validation, and interpretability in management and policy-relevant analyses (Hajikhani & Cole, 2024).

This Special Issue is founded on a dual imperative. First, we seek to advance the practice of technology management by articulating how and under what conditions LLMs can be reliably integrated into managerial analysis and decision-making. In doing so, we aim to increase our understanding of the capability boundaries of LLMs in tasks such as identifying nascent technological opportunities, assessing competitive threats, optimizing innovation portfolios, and anticipating societal impacts with greater accuracy and speed. In particular, we encourage studies that formalize best practices for using LLMs, including task-model alignment, prompt and data design, validation strategies, and human oversight, thereby providing actionable methodological guidance for organizations seeking to deploy LLMs responsibly and effectively.

Second, we aim to stimulate theoretical innovation. The integration of LLMs into our research methodologies is not merely a technical exercise; it prompts fundamental questions about the nature of technological evolution, innovation processes, and strategic agency. By employing LLMs, we can test existing theories at new scales, discover novel constructs, and develop richer explanatory frameworks. We strongly encourage contributions that explicitly reflect on how LLM-driven findings challenge, extend, or redefine the theoretical foundations of technology management, ensuring alignment with established management research paradigms.

We seek original contributions that push beyond conventional methodological boundaries, using LLMs as a novel means to address fundamental theoretical and empirical challenges in technology management. At the same time, submissions are expected to clearly articulate the division of labor between human judgment and model agency. Authors should specify which aspects (e.g., interpretation, theory building, and decision authority) are performed by human researchers, and which aspects (e.g., analytical or generative functions) are delegated to LLMs, thereby positioning these models as empowering instruments rather than purely autonomous agents.

We invite submissions organized around the following four overarching perspectives, which reconceptualize the role of LLMs in technology management research:

1. LLMs as catalysts for theory development: How can LLMs help synthesize large-scale literature, identify critical research gaps, generate novel hypotheses, or inspire new conceptual frameworks that advance theory in technology management? Illustrative themes include:
   • LLM-driven identification of disruptive technologies and weak signals to extend theories of technological change.
   • Automated construction of technology roadmaps and science-technology linkages to refine theories of innovation pathways.
   • Synthesizing multi-source data to assess technology readiness and societal acceptance, informing theories of technology adoption.
2. LLMs as analytical engines for management insight: How can LLMs process multi-modal data (patents, papers, financial reports, news, social media) to uncover hidden patterns, linkages, and dynamics that test or refine existing management theories? Illustrative themes include:
   • Mapping innovation ecosystems, actor networks, and knowledge flows to advance theories of architectural and collaborative innovation.
   • Tracking R&D competition and strategic positioning to inform theories of competitive dynamics and resource allocation.
   • Conducting automated competitive intelligence and semantic portfolio analysis to extend theories of managerial cognition and strategic attention.
3. LLMs as interactive foresight & simulation platforms: How can the generative capacity of LLMs be leveraged to construct dynamic scenarios, simulate socio-techno-economic interactions, or assist in strategic gaming and risk assessment? Illustrative themes include:
   • Building interactive scenarios for technology foresight and risk assessment.
   • Simulating the diffusion of technologies and their multi-dimensional societal impacts.
   • Augmenting R&D processes (e.g., idea generation, solution design) to inform theories of organizational learning and creativity.
4. Critical reflection on efficacy & ethics in management contexts: How can insights derived from LLMs be validated, reproduced, and integrated responsibly into management theory and practice? What are the implications for governance, bias, and decision-making? Illustrative themes include:
   • Novel approaches for validating, evaluating, and interpreting LLM outputs in management study.
   • Integrating LLMs with traditional methods (e.g., econometrics, network analysis, simulation, etc.) for robust, multi-method theoretical insight.
   • Critical studies on the limitations, biases, ethical implications, and governance frameworks for using LLMs in theory development and strategic decision-making.

PDW Submissions

We welcome researchers developing papers for Technological Forecasting and Social Change (TFSC) at any early stage of development. Submissions on all topics relevant to TFSC are encouraged, with particular focus on papers aligned with the Special Issue on Large Language Models as Methodological Innovators: Advancing Theory and Practice in Technology Management. To participate, please submit an extended abstract up to 10 pages in align with the following requirements.

Submit Your Extended Abstract

Key Dates

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