Technovation最新文献

Despite extensive research on academic entrepreneurship and entrepreneurial universities, this special issue challenges conventional beliefs by examining intrapreneurship in academia. It aims to investigate how faculty and staff can adopt entrepreneurial behaviors and cultivate an entrepreneurial approach within their roles as researchers and educators, in diverse academic contexts. The 11 papers included in this issue span various domains of intrapreneurial universities, broadening the original concept beyond initial expectations set forth in the call for papers. Exploring a spectrum of intrapreneurial initiatives, this issue seeks to enhance understanding and broaden perspectives on intrapreneurial behaviors within universities through various research approaches and methodologies. Based on the contributions received, we reflect on theoretical and practical implications and delineate future directions for academic intrapreneurship research.

This study conceptualizes a quadrant model characterizing four profiles by contrasting university scientists’ multiple goals: research performance and commercialization. Since literature shows that these goals are conflicting but not mutually exclusive, social capital theory is drawn to test the influence of scientists’ bonding, bridging, and linking social capital on their profile affiliation. Survey data from 1057 German scientists is utilized to estimate a multinomial logistic regression model relating scientists’ profiles to the different forms of social capital. The results show that only 4.16% of the scientists achieve above-average research performance and also commercialize their research results, whereby all three forms of their social capital positively impact the achievement of these goals. Furthermore, bonding social capital positively relates to scientists with above-average research performance but no commercialized research results. Bridging social capital facilitates scientists to commercialize results, albeit with below-average research performance. In addition, an inverted U-shaped relationship between scientists’ bonding social capital and their research performance is identified, suggesting that an excess of this form of social capital may impede scientists’ ability to achieve multiple goals. The results are discussed and policy recommendations are derived.

This study highlights the role of blockchain technology in addressing the trust crisis in supply chain operations and financial activities. While previous research has acknowledged blockchain as a response to trust issues, limited studies have explored its specific contributions and its impact on accelerating cash flow in supply chains. Drawing on governance theory, this study investigates the relationship between blockchain technology and supply chain finance (SCF) through multiple case studies. The central argument is that blockchain technology alone does not automatically generate trust. Instead, trust is established through the configuration of various governance mechanisms. We propose that trust in blockchain applications is derived from two main factors: the requirement of codifiability and verifiability. Through a cross-case analysis, we found that in situations where both the requirement of codifiability and verifiability are high, blockchain governance tends to dominate. When only one factor is high, a configuration of blockchain governance and relational governance emerges. When both factors are low, contractual governance proves to be efficient. This study underscores the need for a nuanced understanding of trust dynamics in blockchain applications and highlights the importance of considering contextual factors in trust research related to blockchain technology.

Many businesses are finding it difficult to strengthen employees' intentions to engage in intrapreneurial new venturing. In this study, we investigate the organizational conditions that are conducive to the formation of such intentions, specifically employees' intentions to initiate the development of new products or services in their host corporation. The results from a survey of 3492 employees of Swedish companies, including small, medium-sized, and large firms, reveal that recognizing and rewarding personal achievements, maintaining a risk-taking organizational culture, and top management support are all associated with stronger employee intentions to engage in intrapreneurial new venturing. There is weaker support for the predicted positive effect of personal independence and the presence of intrapreneurial role models. Time availability appears to have an inverted u-shaped relationship to employees’ intentions to engage in intrapreneurial new venturing. Overall, the findings support the idea that organizations can be purposefully designed for strengthening intrapreneurial new venturing among their employees.

Exploring the connection between basic science and its practical applications is critical to consider the social justification of the substantial governmental investments in science. However, there's a limited understanding of global research patterns and the dynamics of researcher collaboration from a basic-applied perspective because such studies have been mainly focused only on the biomedical field. Here, the main goal is to propose an indicator to quantify the degree of basic-applied research in academic papers. Using the indicator, we uncover how material science has advanced from basic to applied research, based on the international trends of the indicators and the affiliations of the scientists involved. We develop a methodology that indexes levels of advancement from basic research to applied research based on large-scale text data. The continuous scores assigned to each paper are derived from a vector space embedding technical terms from a broad network data. These scores align with experts' views in material science. This methodology enables us to monitor international trends that China has significantly advanced into applied research, as well as Chinese applied scientists increasingly associating with their domestic institutions. As science and technology implication, our methodology extends the boundary of assessing scientific research on its proximity to real-world applications and provides a tangible measure for funding agencies managing to fund or design research environments.

Through a single longitudinal case study of an Italian spinout, this paper develops the concept and processes of technology co-development competence (TCDC). The study adopts the market-as-network perspective for its theoretical framing. TCDC is developed in interaction with customers in a spinout’s network. This competency is possessed by the firm and enacted for the development of its technology within its business network over time - from establishment to embeddedness. The TCDC concept is developed from the literature and case data and is made up of three elements – information and knowledge integration, multi-actor coordination, and interface management. The data collected comprises twenty eight interviews with twelve informants. The study also examines how TCDC evolves in interaction between the focal firm and its business network. It is found to evolve through four processes – technological frictions and tensions management, network-level relationship processes, product features development, and technological co-development enablement. The study outlines a model that presents how TCDC evolves over time in the spinout’s network as it engages with an increasing number of different actors. The model highlights how the elements and processes of TCDC unfold.

Start-ups can make a decisive contribution to the development of innovation. These organizations are designed to experiment with new technologies and business models. In their growth process, learning from failure plays a decisive role. This study enriches our understanding of learning from failure, particularly in start-ups, by analyzing 21 innovative start-ups that have faced experiences of failure. The theories of organizational learning and dynamic capabilities are applied to identify the responses of start-ups to failure and their implications in terms of learning. Six response strategies were identified: external monitoring, internal evaluation, resource acquisition and mobilization, value creation and capture, team-level entrepreneurial, and organizational learning. These response strategies are grouped into the three dynamic capabilities dimensions: sensing, seizing, and transforming. These strategies are relevant for start-ups to overcome difficulties and continue their growth and innovation. They also offer a guideline for start-ups to develop strategies for systematic learning from failures.

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.