Journal of Product Innovation Management最新文献

Given the limited research on the factors and mechanisms underlying artificial intelligence (AI) authenticity, we examine its use in fostering breakthrough knowledge and enhancing customer engagement. We devised a robust model grounded in mind perception and social exchange theories, with a focus on the outcomes of AI authenticity. Tested across 452 virtual health home stations, the findings reveal that both performance expectation and effort expectation serve as mediators between AI authenticity and customer engagement. This research provides managers with comprehensive insights into the defining attributes and operational mechanics of AI authenticity, thereby highlighting its critical importance in boosting customer engagement.

Our first editorial (De Luca et al. 2025) outlined new initiatives to further develop and grow the Journal of Product Innovation Management (JPIM). Two of those initiatives relate to the (further) development and promotion of different article formats compared to the regular manuscripts that can be submitted to JPIM: Catalyst and Review articles. In this short editorial, we would like to provide more information about these article categories for the benefit of both new and existing JPIM authors and readers.

The key intent of the Catalyst and Review articles is to provide different pathways through which authors can contribute state-of-the-art thinking to the journal and advance our knowledge of innovation management theory and practice in a timely manner (i.e., via a more direct and timely review process). These new initiatives contribute to the scholarly mission we set out as the new Co-Editors-in-Chief (Co-EiCs) to continue to strengthen JPIM's position as a global top-tier research journal for cutting-edge, interdisciplinary, socially impactful, and ethically conducted research in the field of innovation management (De Luca et al. 2025).

First, we discuss the repositioning of the Catalyst category. The Catalyst article category was introduced in 2019 by former Co-EiCs Charles Noble and Jelena Spanjol (Noble and Spanjol 2019). The ultimate aim of the Catalyst category has been and will remain to supplement traditional research articles by means of featuring carefully selected essays intended to inspire and stimulate new and leading-edge thinking on innovation management and to ensure the timely dissemination of this new thinking. Catalyst articles were envisioned as a platform for both scholars and experienced practitioners, thus calling for a dialogue among a plurality of voices inside and outside academia.

From the start of the initiative in 2019 (JPIM, Vol. 36, Issue 4) until the end of 2024, 15 Catalyst essays have been published, which, in total, have accumulated over 1800 citations (Google Scholar citations, June 2025). The topics covered are diverse and include, for example, how science fiction can support innovation (Michaud and Appio 2022), the potential of AI for design (Verganti et al. 2020) and new product development (Bouschery et al. 2023), and how knowledge and experience by Indigenous and tribal peoples may redefine the innovation landscape (Vassallo et al. 2023).

Considering the interest and impact of the published Catalyst papers as new Co-EiCs, we will continue with this category. In doing so, we reposition the Catalyst category by adding further emphasis on novel thinking as a necessary and distinguishing feature of these articles. With the Catalyst category, we aim to publish essays that are interesting and pro

New technologies like blockchain allow firms to decentralize core functions, forcing managers to reconsider the trade-off between closed, proprietary control and open strategies that involve external contributors. While proponents often advocate for full decentralization, we argue this view overlooks important economic trade-offs. We propose that the better strategy is selective decentralization: a disciplined approach to choosing where to centralize for efficiency and where to decentralize for innovation. We propose a three-level framework—Infrastructure, Decision-Making, and Operational Control—to guide this choice, helping managers analyze the specific costs and benefits at each layer. We apply this framework to the strategic adoption of Artificial Intelligence (AI), where the technology's powerful pull toward centralization provides a stark test case. Our analysis shows that an “open source AI” strategy—decentralizing operations to foster innovation while keeping infrastructure centralized for efficiency—is more pragmatic than full decentralization. Selective decentralization therefore emerges as a key managerial capability for capturing blockchain's benefits without sacrificing scale efficiencies.

Financial institutions are increasingly employing artificial intelligence (AI) solutions to optimize their financial advice and services for consumers. However, consumers have demonstrated reluctance toward adopting AI technology goods, and the intermediary psychological mechanism of adoption intention in the financial service context is unclear. Using the theoretical lens of technology affordances and constraints, this article proposes the concept of consumer technology vulnerability (CTV) as the mediating mechanism in the affordance–adoption process of AI financial advisors (AFAs). Meanwhile, consumer innovativeness and self-efficacy are investigated as individual traits that moderate perceptions and psychological impacts of AI affordances. Specifically, the study first conceptualizes AI affordances in a product innovation context by reviewing the burgeoning literature on AI to date. This is followed by a US-based survey (N = 616), which shows the positive indirect effects of information optimization, customizability, and human-likeness on AFA adoption intention through CTV. Self-efficacy and consumer innovativeness are found to enhance the positive effects of AI affordances on AFA adoption intention through CTV but diminish the impact of human-likeness on CTV. These findings highlight, for the first time, the mediating role of CTV in new technology adoption. This will help technology innovators and financial institutions to identify how consumers perceive and adopt different AI affordances, and therefore to better incorporate AI characteristics into financial product innovations.