Journal of Product Innovation Management最新文献

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.

The avalanche of available unstructured text data makes it ever more challenging for innovation practitioners (and academics) to extract meaningful insights from such data. Topic modeling can support these efforts and help spur innovation. The current study reviewed 1099 innovation management articles to identify and compare the most frequently used probabilistic topic modeling approaches for innovation. In an effort to contextualize the suitability of these approaches, we develop a framework to organize existing topic modeling applications along the different innovation stages (i.e., idea generation, development, and commercialization) and innovation research in general. By zooming in on the three innovation stages, the authors showcase how topic modeling can spur innovation within each stage and highlight the future potential of the specific approaches. To further assist in capturing the various dynamics in complex unstructured text datasets, we illustratively apply a tailored topic modeling configuration to 1444 Journal of Product Innovation Management articles (1984–2023) to identify emerging, stable, and mature topics, as well as looking at their respective impact. This demonstration could serve as a starting point or blueprint for innovation practitioners and researchers seeking to combine the advantages of several topic modeling approaches. We conclude by offering a future outlook, including a forward-looking research agenda. Taken together, our study offers guidance to and equips innovation practitioners and academics to design distinctive topic modeling procedures to best serve their intended purposes. If deployed appropriately, topic modeling helps users extract a wealth of unique, unprecedented insights from a continuously expanding source of data.

We investigate the interaction design preferences of solution seekers and problem solvers on open innovation (crowdsourcing) platforms. Drawing on social exchange theory (SET), we hypothesize that seekers and solvers have different preferences for the configuration of four central interaction design features of a crowdsourcing platform: communication channels, collaboration options, selection of winning submissions, and feedback mechanisms. Based on a conjoint study with 842 respondents, we show conflicting preferences for the configuration of these features, but also find a surprisingly consistent “best” configuration that can balance the individual preferences of both seekers and solvers. In addition, we identify social trust, risk aversion, and the need for cognition as three personal characteristics of individuals in seeker organizations and solvers that influence their preferred configuration of platform design. Our findings help intermediaries operating a crowdsourcing platform to offer nuanced platform interactions that align how individuals in seeker organizations (e.g., project managers) and individual solvers create and capture value in crowdsourcing. Furthermore, we contribute to the micro-foundations of open innovation by proposing SET as a novel perspective to examine how the expectations and value drivers of all parties involved in a crowdsourcing project can be balanced.

Many launch strategies for new products now aim at building pre-release consumer buzz (PRCB), defined as consumers' collective expressions of anticipation for an upcoming product. While a positive association of PRCB with innovation success has been established, little is known about how, under what conditions, and to what extent PRCB influences consumers' adoption decisions. This research sheds light on these issues by investigating PRCB's contagious nature as one of the concept's defining characteristics. Drawing on herding theory, the authors develop a conceptual framework and provide comprehensive experimental evidence that consumers' exposure to PRCB for a new product triggers distinct psychological mechanisms that influence their own adoption decisions: PRCB-observing consumers exhibit both greater social attraction to the “buzz movement” (group-related evaluation) as well as more curiosity and higher quality expectations about the new product (product-related evaluation). Furthermore, these effects are particularly strong for consumers who are highly susceptible to social influence and for products with low popular appeal. The authors complement their consumer-level analysis with an illustrative market-level what-if analysis that approximates the financial consequences of PRCB's contagious effects. Results suggest that the financial impact of PRCB can be substantial but differs significantly across scenarios, depending on product type and consumer segment. These findings have important implications for the management of innovations before launch.