Journal of Product Innovation Management最新文献

Digital twins (DTs) are virtual representations of real-world entities like production assets, processes, or products. They are updated at a defined fidelity and frequency along the entire life cycle from development and engineering over the production or implementation of a product or process until its usage stage. Interconnected digital twins (IDTs) are DTs shared and connected across organizations with the objective to create holistic simulation and decision models of an entire physical system. In this paper, we investigate how IDTs shape future digital manufacturing scenarios and impact innovation management. We present the results of a real-time Delphi study, analyzing quantitative and qualitative estimates on a set of 24 projections, forecasting the future of digital manufacturing with a projection horizon towards 2030. Using this data and 22 additional use cases of IDTs in manufacturing companies, we present a baseline scenario where our Delphi panel reached a consensus, representing a likely future of digital manufacturing in 2030. By analyzing projections where our expert panels' evaluations vary widely, we identify key design decisions that may impact innovation management along the dimensions of variation, choice, and control in digital manufacturing. We explain how IDTs will impact external knowledge inflows, the emergence and governance of industrial data spaces, and the potential of data-driven and AI-enabled applications for prediction and regulation to drive better decision-making and continuous innovation.

Manufacturing firms that engage in digital transformation develop increasingly smarter versions of their tangible products to reinvigorate growth in shrinking markets. However, they often struggle with translating their investments in digitalization capabilities into actual returns in the form of sales growth. The associated technological advantages often remain unexploited, and digital product innovations frequently fail. Building on the resource-based view of the firm and the demand-side perspective, we theorize that there is a need for complementary capabilities that integrate heterogeneous customer demands, thus, allowing firms to capture more value from smart products. We empirically investigate the mediating role of smart customization capability on the relationship between digitalization capabilities and sales growth. Moreover, we argue that this relationship is further strengthened by integrating information and data across sales and service channels (i.e., channel integration). We test and find support for our hypotheses based on a dataset comprising survey and archival data of 136 smart product manufacturers in Austria, Germany, Switzerland, and the United States. In doing so, we enhance the theoretical understanding of resource and capability configurations needed for digital transformation in general and smart product success in particular. We further update the traditional concept of mass customization by showing how customization with smart products helps manufacturing firms provide personalized solutions at scale.

While raising concerns, the recent proliferation of grand challenges has sparked interest in the role played by innovation in causing them, and in how the attempts made to fix them may cause even greater challenges that present themselves down the line. This article provides an analysis of the bibliographic metadata, published between 2002 and 2020, focusing explicitly on the private-for-profit sector. By identifying common themes from 66 documents, a framework highlighting the shared concerns and research trajectories was derived. Our results are illustrated and discussed along 11 research themes. We contribute theoretically by identifying the innovation efforts of for-profit firms that directly relate to grand challenges, through two cases of carbon capture and storage and deep-sea mining. We conclude that a more holistic understanding of innovation and its many possible consequences needs to be developed. We highlight the limitations of perspectives that do not always take full account of the potential divergence of interests between stakeholders, and, how fuller input by a greater cross-section of stakeholders may help identify any negative effects of innovations at an earlier stage. Informed by recent extensions of social innovation theory, we explore the potential for synthesis around a pragmatic understanding of institutions, stakeholders, and the nature and quality of ties that bind them.

The persistence of grand societal and environmental challenges demands attention from innovation management scholars and practitioners to find effective resolutions. Grand challenges are complex, uncertain, and evaluative and cannot be resolved by individual actors or organizations. Therefore, conventional forms of organizing do not suffice in the face of wicked problems like climate change or global inequality, which require continuous and varied attention and inputs. In this catalyst article, I argue that platform ecosystems—communities and groups of actors in different markets orchestrated through a digital platform and driven by combinations of economic and prosocial incentives—are an organizing form that can help effectively scale solutions for grand societal and environmental problems. This potential is based on three organizational elements of platform ecosystems: (1) coordination structures for orchestrating complementary inputs, (2) instigation and maintenance of collective action, and (3) generativity potential. I illustrate these arguments with practical examples of two platforms with the potential to resolve specific grand challenges: Patient Innovation, which orchestrates a community of innovators seeking to help treatment of chronic and rare diseases, and Excess Materials Exchange, which provides matching solutions to address the challenges associated with industrial material waste. The article concludes with an agenda for future research and practice on platform ecosystems and grand challenges.

Is high employee turnover harmful to innovation? To answer this question, we draw on the knowledge-based view of innovation. Specifically, we theorize that the collective turnover of a firm engenders complex changes in knowledge insourcing needed for generating innovation, which may lead to the attenuating negative effect of turnover on innovation. This study also aims to investigate a contingency that modifies the detrimental effect of collective turnover on innovation. Specifically, we identify knowledge-sharing system (KSS) as a positive knowledge-related contingency that engenders a U-shaped curvilinear relationship between collective turnover and firm innovation. In addition, replenishing human capital by hiring new employees improves knowledge insourcing quality and diversity, thereby constituting the mechanism through which collective turnover affects firm innovation. An analysis of large-scale firm-level data collected from 2259 Korean firms over a 6-year period supports most hypotheses and confirms the positive effect of high turnover on firm innovation through replacement hiring and under favorable firm contingencies, such as a high KSS. This study provides a balanced perspective by revealing the costs and benefits of collective turnover and explains when and how turnover can facilitate firm innovation.

Big data-driven innovation gains momentum in the developing world by tackling grand challenges and making a pronounced and lasting impact. However, research has still not answered the key question regarding the dimensions of big poverty data analytics (BPDA) capabilities for creative service offerings nor their effects on economic and social outcomes. This study fills this gap by conducting a two-phase Delphi study and two rounds of surveys focusing on a globally leading microcredit institute in a developing country. The study conceptualizes management (analytics climate, cross-functional integration), platform (technology and data), and talent (pattern spotting and market ambidexterity) capabilities as the dimensions of BPDA capability. It further investigates creative service offerings (meaningfulness and novelty) as the mediator between BPDA—new service performance (economic outcome from the firm's end) as well as BPDA—quality of life (social outcome from consumer's end). Our research advances, first, the microfoundational view of dynamic capability (DC) theory in the emerging data-driven innovation paradigm by specifically identifying the complementary and co-specialization attributes of six microfoundations (i.e., analytics climate, cross-functional integration, data, technology, pattern spotting, and ambidexterity). In addition to DC, our study extends social innovation literature by establishing the connection between the instrument (BPDA capability) and outcome view (quality of life) of social innovation to tackle grand challenges in the developing world. Finally, our study extends creativity theory by introducing creative service offerings as a mediator and highlighting its role in the novel and meaningful solutions to social problems. Overall, this study is a pioneer in conceptualizing and empirically validating a research model that uncovers the potentially complex nuances between BPDA capability-creative offerings and innovation outcomes to tackle the grand challenge of poverty. Its findings are scholarly significant and executive worthy and constitute a major advancement in extant studies regarding how creative services are developed using BPDA and their impact on the firm's profitability and consumer well-being.

Capturing valuable product/service improvement ideas is helpful for the development of new features. However, the existing methods for capturing such improvement ideas have the disadvantages of high cost, long time lag, information overload, and difficulty in getting a response. We propose an innovative framework based on lead user theory for capturing product/service improvement ideas from user-generated content on social media (henceforth called “chatter”). To identify the chatter containing improvement ideas, we design a machine-learning-based imbalanced classification model. Additionally, we use text summarization technology to get a rough sense of improvement ideas from the selected chatter. We validate the proposed framework by a case study in the automotive industry. The results demonstrate that the ideas extracted by our framework are breakthrough innovative, useful, feasible, and adoptable.

Societies are confronted with grand challenges that require the efforts and coordination of diverse stakeholders. In this context, the role of for-profit organizations has become vital in addressing such challenges. Drawing on the strategy tripod perspective, this study investigated the influence of reverse engineering on frugal product-innovation performance (PIP) through the mediating effect of frugal innovation (i.e., cost innovation, and affordable value innovation). In addition, we examined the moderating impact of the industry environment (i.e., technological turbulence) and institutional context (i.e., legal inefficiency) on this relationship. We tested our hypotheses using time-lagged data from 243 small to medium-sized enterprises (SMEs) in an emerging economy—Ghana. Results from our analyses show that several of our hypotheses are supported which offers important implications for the indirect impact of reverse engineering on frugal product-innovation performance in the context of resource-constrained emerging markets. These findings extend the grand challenges, strategy, and innovation literature.

This paper seeks answers to the question: what are the key factors that enable the scaling of additive manufacturing (AM) from rapid prototyping to high-volume production? Using a longitudinal case study, we collected primary and secondary data to trace the AM scaling journey of AeroCo, a highly innovative aerospace firm. Based on the case findings, we position AM as a whole system technology because it can print components for a wide range of subsystems in a complex final product. Scaling AM requires a significant realignment of existing, and often deeply entrenched, new technology, and product development processes. To achieve this alignment, AeroCo formed institutional alliances with the UK government and universities to establish university technology centers, which facilitated early stage ideation and “catapult” centers, which enabled high-volume testing in factory-like facilities. The case reveals how multiple functions needed to integrate, including research and development, product design, and future programs, to ensure that design changes cascaded from one subsystem to another, and that new technologies were linked to a future product to create a final product pull. These findings inform a managerial framework for additive manufacturing scaling that is generalizable to other digital technologies used in the design and production of complex final products, including artificial intelligence, machine learning, smart factories, and cyber physical production systems. Our framework contributes to innovation thought and practice by explaining how new product development processes and organizational structures change under the effect of digital technologies.