Technovation最新文献

Technology-enabled business model innovations (BMIs) are gaining increasing attention. However, the effects of technology-enabled BMIs on sustainable development have not been fully investigated. Using the implementation of the National Big Data Comprehensive Pilot Zone (NBDCPZ) policy as a quasi-experiment, this paper analyzes the impact of technology-enabled BMIs on the carbon emissions in China. Our results indicate that the NBDCPZ policy significantly increases technology-enabled BMIs and reduces carbon emissions. These results remain consistent after conducting a series of robustness tests. Furthermore, we find that the reduction in carbon emissions is greater in western and northeastern cities or cities with stricter environmental regulations, ample higher education resources, or less reliance on natural resources. An analysis of the mechanisms indicates that the NBDCPZ policy improves energy structure and efficiency, further reducing carbon emissions. This study suggests that companies should be encouraged to adopt technology-enabled BMIs to promote low-carbon development.

Technology spillovers, understood as knowledge flows across technology domains, are an important mechanism for innovation, as progress in one technology area can lead to unexpected benefits in other technology areas. Despite the importance of technology spillovers in theories of recombinant innovation, significant gaps remain in our understanding of how spillovers across knowledge domains occur, what factors enable or affect spillovers, or how much consideration should be given to technology spillovers in research and innovation policy and management. In this paper, we develop and demonstrate novel conceptual and methodological tools to address these knowledge gaps. Specifically, we introduce a conceptual framework that views technology spillovers as a dynamic process of micro-level knowledge transfer across technological domains that drives recombinant innovation in a knowledge-receiving domain. We use this framework to develop a process-tracing methodology for identifying and analyzing individual micro-level technology spillovers. We demonstrate the application of our framework and methodology in a case of three important innovations in crystalline silicon solar photovoltaics by reconstructing a detailed history of how technology spillovers have played a critical role in enabling and driving these three innovations over time. We show how our approach generates important insights relevant to public policy and R&D management that aim to harness spillovers for accelerating innovation.

Building on the literature on the concept-product gap in new product development, we examine how FinTech SMEs are developing Artificial Intelligence (AI)-based innovations and which organisational or project factors best contribute to the acceleration of AI innovation. The empirical evidence collected from interviews with key stakeholders, practitioners’ forums, and public company documents yields two distinct approaches that differ in their potential for accelerating innovation and reducing the concept-product gap. From a contingency perspective, these two approaches are expanded into four distinct development process configurations, contingent on the business development stage, reliance on 3rd party platforms, availability of high volumes of data, investment level, organisational agility, and level of novelty. The resulting process typology could be used as a diagnostic tool for FinTech SMEs interested in effectively leveraging AI innovation. Using contingency theory, we further develop these insights into a new theoretical framework to explain how AI innovation development unfolds in FinTech SMEs and the rationale for different implementations. Our new process typology and theoretical model can help researchers investigate the mechanisms underlying technological innovation processes. We further identify the specific reasons why the potential of AI for creating new services and disrupting incumbents via digital startups has not been fully realised even in contexts with significant investment and support from public and private business development programmes. This field is still rapidly evolving, and thus, new areas for future research are also highlighted.

Relationships between universities and industry are a source of entrepreneurial activity and innovation. Considering the complexity of university-industry (U–I) collaboration though, the underlying activities require formalisation mechanisms to help overcome ambiguity, mismatched objectives, demands and expectations to ensure joint innovation outcomes. Fairness perceptions have also been found to determine the success of inter-organisational relationships. However, to date fairness has not been explored in stakeholder contexts, such as those prevalent during U–I collaboration. This research extends knowledge on U–I collaboration processes by exploring if formalisation mechanisms increase fairness perceptions and joint innovation outcomes. We adopt a survey methodology to explore the experiences of German professors engaging in U–I collaboration. Our findings identify that formalising U–I collaborations, through mechanisms such as clear procedural guidelines and contracts which agree responsibilities and outcome expectations, leads to enhanced perceptions of fairness and trust. Furthermore, both procedural and distributive fairness were found to increase the positive effect of formalisation on joint innovation outcomes. These findings advance U–I collaboration and strategic alliances literature through identifying the importance of perceived fairness, which is often deemed to be as important as material outcomes and will impact upon join innovation outcomes being achieved. Furthermore, this research proves the positive relationship formalisation has on innovation outcomes.

Collaborations between incumbent and startup firms are hailed for enhancing incumbent innovation capability. Yet, these collaborations often face challenges, notably cognitive distance, highlighting the need to balance potential novelty and mutual understanding between partner firms. Drawing on prior research that indicates a non-linear relationship between cognitive distance and innovation outcomes, we focus on collaboration controls—tools and practices that guide and motivate managers responsible for startup collaborations— serving as critical mechanisms for incumbents to exploit cognitive distance. We hypothesize that the use of collaboration controls mediates the non-linear relationship between cognitive distance and realizing enhanced innovation capabilities. Empirical findings from a survey of 239 French incumbents confirm an inverted U-shaped relation between cognitive distance and enhanced innovative capabilities. Notably, we find that use of collaboration controls mediates this relationship, highlighting the importance of controls in exploiting cognitive distance. This research contributes to unveiling the intricacies of successful collaborations between incumbents and startups by uncovering a critical role for collaboration controls in realizing novelty potential.

Researchers often turn to linear mediation models to understand the complex causal processes inherent within innovation and entrepreneurship phenomena. However, these models are not always the most appropriate methods for increasing our understanding of these phenomena. This is because linear models depend on the principle of reductionism – which separates causal processes into their independent components – and overlooks systemwide attributes. To advance research findings that do not adequately address complex causal processes, we advocate using set-theoretic mediation models that offer analytical features better suited for holistically uncovering interdependent and intervening pathways. This method enables investigating complex causal processes associated with the conjunction, equifinality, and asymmetry that can occur with multiple interdependent variables. We provide researchers with practical guidance on constructing and testing set-theoretic mediation models using widely available software while demonstrating these procedures with an illustrative analysis. In doing so, we seek to guide researchers interested in integrating these models into their studies and recommend best practices for implementation. We argue that set-theoretic meditation models can be utilized in various contexts, as they offer new research opportunities for exploring unified necessity and sufficiency relational systems in ways existing methods have yet to address.

Customer involvement plays a critical role in firms' new product development (NPD), especially in the digital context. Prior research has distinguished between two forms of customer involvement: customers as an information source (CIS) and customers as co-developers (CIC). We build on this stream of literature and investigate the impact of CIS and CIC on firms’ NPD performance in the digital context by considering both the benefits and costs of these two forms of customer involvement. Furthermore, we explore the moderating role of NPD stage. Based on survey data collected from 263 informants, we show that whereas CIS has a positive effect on NPD performance, CIC has an inverted U-shaped relationship with NPD performance. More interestingly, the positive relationship between CIS and NPD performance is more pronounced in the ideation stage than in the product development stage, and the inverted U-shaped relationship between CIC and NPD performance is steeper in the product development stage than that in the ideation stage. In our additional analyses, we further divide the NPD stage into four stages and found similar results as our main analyses. Our study enriches the customer involvement literature and offers practical implications regarding whether, how, and when to involve customers in the NPD process.

User engagement is widely recognised as beneficial for the innovation efforts of firms scarce with internal resources but our knowledge about the relevance of user types and engagement strategies is scarce and mostly limited to the front-end phases of the innovation process. To fill this gap, we use data from over 10,000 companies operating in four emerging innovation ecosystems in Central Europe and examine the contribution of users to product development, distribution, market positioning and the commercialisation of innovations. The results show that user engagement through customisation and co-creation promotes the success of the innovation process, with the impact of co-creation being much stronger. Consumers, intermediary users (businesses) and public sector users play different roles at different stages of innovation. These findings provide important insights for firms seeking to optimise their user engagement strategies in product innovation management.

This paper examines corporate angels, small businesses that provide equity capital directly to unquoted ventures and become minority shareholders. Corporate angels have been largely ignored in entrepreneurial finance research. This novel study examines corporate angels' investment practices, goals and challenges by means of a combination of qualitative cases and a survey of 87 corporate angels. From our analysis, we conclude that due to their knowledge and experience of technology and industry, combined with their active involvement in the investments, corporate angels can be an important source of seed and early-stage financing for technology-based start-ups, particularly with regard to technological and market knowledge and experience. Corporate angels’ investment activity also leads to knowledge inflows to the corporate angel firms in terms of organizational and cultural development of the firms. With this paper we shed light on a new and unexplored investor group that is important in the changing landscape of entrepreneurial finance.

With the development of big data analytics, consumers' online reviews are becoming increasingly useful for product innovation with hidden innovative ideas that can be extracted. However, these ideas may be only hidden in a small part of the massive reviews. This study aims to investigate the potential of using anomaly detection technology to identify unique reviews for more effective innovation generation. Three classical anomaly detection approaches (including both machine and deep learning) were explored, namely, isolation forest, density-based cluster analysis, and autoencoder methods. Using the consumer reviews on Dyson Vacuum cleaner from Xiaohongshu (one of the most popular social media platforms in China), we tested and compared the application of these three approaches in detecting innovation-relevant reviews. The results indicate that the two machine learning approaches, aka., density-based cluster analysis and isolation forest are too sensitive to the length of the reviews. The deep learning method, autoencoder, on the contrary, shows good stability and capability to detect the unique reviews from the whole dataset. Furthermore, the experts’ rating also confirms the effectiveness of autoencoder in identifying innovation-relevant reviews. Future studies and implications are then discussed.