Journal of Product Innovation Management最新文献

Artificial intelligence (AI) is disrupting innovation. However, our understanding of firm-level consequences remains limited. While firms are starting to develop a strategic AI orientation (i.e., goals and strategic directions), we neither know how firms establish a strategic AI orientation nor whether it suffices to increase firms' technological innovation. We explore these questions in two studies. In Study I, we conduct 42 interviews with AI managers in large firms. Using the attention-based view to structure the qualitative insights, we build deductive hypotheses on the relationship between AI orientation and technological innovation. Study II tests our hypotheses quantitatively, using natural language processing to develop a text-based measure of firms' strategic AI orientation. Applying this measure to S&P 500 firms between 2012 and 2021, we find that strategic AI orientation relates positively to firms' technological innovation, also across technology domains. CEOs' IT-related education strengthens this link. These insights contribute to AI-innovation research. First, we validate and refine the construct strategic AI orientation and its mechanism that links it to technological innovation. Second, we establish a positive AI-innovation relationship from a strategic perspective, enhancing the external validity of research in this domain. Overall, this article offers a starting point for strategic AI research.

Artificial intelligence (AI) is disrupting innovation. However, our understanding of firm-level consequences remains limited. While firms are starting to develop a strategic AI orientation (i.e., goals and strategic directions), we neither know how firms establish a strategic AI orientation nor whether it suffices to increase firms' technological innovation. We explore these questions in two studies. In Study I, we conduct 42 interviews with AI managers in large firms. Using the attention-based view to structure the qualitative insights, we build deductive hypotheses on the relationship between AI orientation and technological innovation. Study II tests our hypotheses quantitatively, using natural language processing to develop a text-based measure of firms' strategic AI orientation. Applying this measure to S&P 500 firms between 2012 and 2021, we find that strategic AI orientation relates positively to firms' technological innovation, also across technology domains. CEOs' IT-related education strengthens this link. These insights contribute to AI-innovation research. First, we validate and refine the construct strategic AI orientation and its mechanism that links it to technological innovation. Second, we establish a positive AI-innovation relationship from a strategic perspective, enhancing the external validity of research in this domain. Overall, this article offers a starting point for strategic AI research.

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.

Intrapreneurs—employees participating in internal corporate venturing (ICV) programs—are vital in nurturing entrepreneurship within organizations. While existing literature has extensively studied ICV at the organizational level, research on individual-level processes remains limited, particularly regarding intrapreneurs' identity-related conflicts over time. However, to design programs and interventions that unlock employees' creative potential and promote innovation, it is crucial to understand these individual dynamics. We conducted an 18-month longitudinal study of 21 intrapreneurs within an ICV program, developing a process model and theory of intrapreneurs' identity work. We find that aspiring intrapreneurs construct an “idealized entrepreneur identity” that fundamentally conflicts with their employee identity. To maintain a coherent sense of self, they either safeguard their emerging entrepreneur identity by denying corporate dependence and eventually opposing the organization or they realign with their employee role, forsaking their intrapreneurial aspirations. These findings challenge the implicit assumption of a distinct intrapreneur identity, demonstrating how and why intrapreneurs struggle to construct a coherent and positively valued identity as intrapreneurs. We term this phenomenon the intrapreneur identity illusion and highlight its detrimental effects for both individuals and organizations. Our research contributes to understanding the individual-level foundations of corporate entrepreneurship and offers broader implications for innovation management.

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.

Artificial intelligence (AI) technologies have gained significance for all types of firms, including family firms. However, unlike other business contexts, family firms face unique challenges in adopting complex and generative technologies, such as AI. This is particularly evident when examining the heterogeneous nature of family firms distinguished by passive (i.e., family ownership) and active (i.e., family management) family involvement. Specifically, we adopt a social capital perspective and combine inductive qualitative and deductive quantitative research into a mixed-methods design. In the first step, we conduct a multiple case study of eight firms using rich data from 125 interviews and archival documents. While the case evidence shows that strong external network ties with suppliers, customers, and competitors help overcome the challenges related to AI technologies, we reveal opposing impacts of passive and active family involvement. Interestingly, our qualitative insights emphasize a negative moderation effect for passive family ownership exacerbating the challenges of AI and a positive moderation effect for active family management facilitating AI adoption. In the second step, we conduct a quantitative test of this conceptual model. On the basis of large-scale data from 1444 firms, we find empirical support that increasing strengths of external network ties with suppliers, customers, and competitors drive AI adoption. Moreover, we find support for the opposing roles of passive family ownership as a negative moderator and active family management as a positive moderator in the relationship between supplier ties on the one hand and AI adoption on the other. Our study contributes toward a nuanced understanding of the idiosyncratic challenges of AI adoption faced by different types of family firms and the role of social capital in this regard and, more broadly, to research on innovation and technology adoption in family firms.