Aug 2026· Strategic Management Journal· 0 citations· 91 references
TL;DR
Studying the Firefox browser add‐on ecosystem, it is found that after ChatGPT's release, the number of new add‐ons increased by 34%, driven by both existing and first‐time developers, however, developers simultaneously reduced improvements to their existing add‐ons by 20%, redirecting effort toward new projects.
Abstract
We study the impact of generative artificial intelligence (GAI) tools on product‐level innovation outcomes in the context of software products. Specifically, we illustrate how GAI can alter the direction of innovation by shifting the activities of developers away from generational innovation and toward original innovation, which may be new to the market but not necessarily more novel than previous innovations. We argue that this shift is driven by GAI's ability to facilitate tasks in both the ideation and implementation of software products, which enables some developers to create software products that were previously beyond their reach, while allowing others to reallocate their effort with respect to different activities. Our analyses in the context of browser add‐ons provide empirical evidence for these arguments. We discuss implications for innovation research and the generalizability of our findings to other domains.
Generative AI tools are changing how organizations innovate. Studying the Firefox browser add‐on ecosystem, we find that after ChatGPT's release, the number of new add‐ons increased by 34%, driven by both existing and first‐time developers. However, developers simultaneously reduced improvements to their existing add‐ons by 20%, redirecting effort toward new projects. While new add‐ons drew on a broader set of knowledge domains, they were not substantially different from what already existed. This suggests that generative AI helps developers efficiently combine existing knowledge rather than produce truly novel ideas. For platform managers and business leaders, these findings highlight that although Generative AI can democratize product creation and accelerate new product launches, it may require new strategies to maintain product quality and encourage genuine novelty.
The use of Generative Artificial Intelligence (GenAI) is deeply impacting all Knowledge Management (KM) processes. In particular, due to its ability to generate new content in different forms, the new technology is deemed capable of deeply transforming the knowledge creation process, which is considered the highest and most impactful stage of KM processes. Despite this, a comprehensive understanding of how companies can leverage GenAI to create organizational knowledge is lacking, both empirically and theoretically. Regarding the latter, scholars have recently underlined that prior research has yet to focus on the transformation of the SECI model and Ba theory, the most widely used conceptual frameworks for interpreting the organizational knowledge creation process, in the era of human-intelligence interaction. However, in the last two years, some studies have examined whether the SECI model needs to be revised in light of GenAI. Based on a review of the 19 systematically identified articles on Scopus, the present paper identifies, discusses, and compares the three different conceptual approaches adopted by scholars in dealing with the topic in question: a) applying the SECI model in its original version; b) adapting the original SECI model with small adjustments; c) developing a new SECI-based model. The paper compares the three approaches, highlighting how they assume different notions of the role of GenAI in the knowledge creation process and the types of knowledge involved. The academic and practical implications that arise from the study are discussed in the conclusions.
E. Scarso, K. Kirchner· European Conference on Knowl...· 0 citations
This paper systematically reviews the technological evolution and core capability characteristics of GAI and reveals the micro‑mechanisms by which GAI enables management innovation across three dimensions: knowledge recombination and abductive reasoning, simulation and counterfactual reasoning, and dynamic resource orchestration.
Zixuan Li· Business Management Perspect...· 0 citations
Generative artificial intelligence (GenAI) is a transformative shift in how organizations can navigate innovation processes and stay competitive in dynamic markets. This paper explores the synergy between innovation management theory and the capabilities of generative AI, offering a holistic perspective of its strategic challenges and opportunities to business leaders. Based on the concepts of dynamic capabilities theory, open innovation and recent empirical studies on the adoption of GenAI, we propose an integrated theoretical framework that conceptualizes GenAI as an enabler and disruptor of the existing innovation processes. Three specific areas of strategic challenges emerge from our analysis: organizational and cultural barriers, such as adaptation and resistance to change by the workforce; technical and governance issues, such as data quality and risk of AI hallucination; and ethical and regulatory issues, such as IP and algorithmic bias. At the same time, we pose three key strategic questions: how can innovation cycles be accelerated with automated ideation and prototyping? How can innovation be made accessible for everyone, by democratizing the process? And how can new business models emerge with content generated by AI? Finally, the paper offers practical managerial suggestions and a research agenda for scholars. The present work is a theoretically informed, but practically relevant, study at the intersection of artificial intelligence and strategic management, providing invaluable guidance for the innovation landscape in the era of GenAI.
Rhythm Mittal, Kunal Saxena· Journal of Emerging Technolo...· 0 citations
This investigation paves the way for a comprehensive understanding of how AI is perceived by those who directly manage the introduction of these tools into traditional software development workflows, revealing a road map for future endeavors for the software development community.
Xin Zhao, Brian Vu, Sitesh Pattanaik· AIware· 0 citations
Artificial intelligence (AI) has moved within a single decade from a specialised computational method to a general-purpose research instrument that now touches almost every stage of the scholarly lifecycle, from problem formulation and literature synthesis to experimentation, analysis, writing and peer review. This paper asks three connected questions: where AI produces genuine innovation in research rather than incremental automation; where it delivers measurable efficiency; and what conditions of responsible use must hold for those gains to be epistemically and ethically legitimate. The study adopts a structured narrative review of peer-reviewed literature, policy instruments and editorial guidance published between 2016 and 2026, and synthesises the evidence through thematic coding into a conceptual model. We find that innovation gains cluster in domains where AI compresses very large combinatorial search spaces—protein structure prediction, molecular and materials screening, and simulation surrogates—while efficiency gains are widest in language-intensive and data-curation work, where they are also most weakly audited. The dominant risks are epistemic rather than merely procedural: fabricated citations, homogenisation of research questions, and an illusion of explanatory depth in which fluent output is mistaken for understanding. We argue that disclosure statements alone are an insufficient governance response, and propose the Innovation–Efficiency–Responsibility (IER) framework, comprising three pillars, twelve operational levers and a five-level institutional maturity scale. Implications are drawn for universities, funders, publishers and policymakers, with particular attention to resource-constrained institutions in India and comparable settings.
Ritesh Kandari, Dr. Vinod Kumar Kanakapura Channan, Dr. Amita Garg, Ravi Ranjan· International Journal of Adv...· 0 citations
Contemporary product development is currently undergoing a profound paradigm shift triggered by rapid advances in the field of artificial intelligence (AI). In an era where dynamism and speed determine market success, the pressing question arises regarding the true potential of this technology for generating innovation and for the field of entrepreneurship. This study examines this transformation process through a pragmatic research approach: the observation of so-called Generation Z. As “digital natives,” these individuals have grown up with digital technologies and should, in theory, exhibit the highest adoption rate for AI tools. As part of a case study at the Munich University of Applied Sciences, Generation Z students were tasked with innovation projects to determine, through a combination of behavioral observation, results analysis, and subsequent surveys, to what extent AI is capable of supporting the process from initial idea to visionary innovation. The focus here was deliberately on revolutionary rather than evolutionary approaches, on generating visionary innovations that go beyond incremental improvements. The results make it clear that AI’s greatest value currently lies in visual inspiration and the communication of complex visions of the future, while original creative work continues to require synergistic interaction between humans and machines.
