Technovation最新文献

Intertwining users' engagement with the development of new products is becoming increasingly important for improving the products' performance. Therefore, a method to model the dynamics of collective user engagement is essential. This paper proposes a method to model the dynamics of collective user engagement in new product development using agent-based simulation. The model integrates an indirect engagement process – in which users exchange information and knowledge, draw from past experiences, and psychologically invest in the product – with a direct engagement process – in which users interact with the product and the firm to contribute. We demonstrate how the model can be calibrated using a consumer survey. Our findings reveal an overall increase in user engagement, characterised by nonlinear growth during the idea and design stages, followed by a relatively steady increase during the test stage. Moreover, our study identifies the significance of various factors, such as social network parameters, past experience, product connection, and match degree, in shaping user engagement in new product development. This research provides a systematic approach to model user engagement within the context of new product development. It also offers practical implications that can guide management decisions in effectively engaging with users during new product development.

Recently, Gen AI has garnered significant attention across various sectors of society, particularly capturing the interest of small business due to its capacity to allow them to reassess their business models with minimal investment. To understand how small and medium-sized firms have utilised Gen AI-based tools to cope with the market's high level of turbulence caused by the COVID-19 pandemic, geopolitical crises, and economic slowdown, researchers have conducted an empirical study. Although Gen AI is receiving more attention, there remains a dearth of empirical studies that investigate how it influences the entrepreneurial orientation of firms and their ability to cultivate entrepreneurial resilience amidst market turbulence. Most of the literature offers anecdotal evidence. To address this research gap, the authors have grounded their theoretical model and research hypotheses in the contingent view of dynamic capability. They tested the research hypotheses using cross-sectional data from a pre-tested survey instrument, which yielded 87 useable responses from small and medium enterprises in France. The authors used variance-based structural equation modelling with the commercial WarpPLS 7.0 software to test the theoretical model. The study's findings suggest that Gen AI and EO have a significant influence on building entrepreneurial resilience as higher-order and lower-order dynamic capabilities. However, market turbulence has a negative moderating effect on the path that joins entrepreneurial orientation and entrepreneurial resilience. The results suggest that the assumption that high market turbulence will have positive effects on dynamic capabilities and competitive advantage is not always true, and the linear assumption does not hold, which is consistent with some scholars' assumptions. The study's results offer significant contributions to the contingent view of dynamic capabilities and open new research avenues that require further investigation into the non-linear relationship of market turbulence.

The literature on stock forecasting using the innovative technique of Artificial Intelligence (AI) has become overwhelming, making it quite challenging for academics and relevant researchers to gain an elaborative, structured, and organised overview of the relevant information. We fill this gap by contributing and conducting a robust bibliometric review on the application of AI innovations for stock market prediction. More specifically, we conducted a bibliometric review by identifying 241 relevant papers related to stock forecasting using AI by taking a quantitative approach. A quantitative approach uses an examination of linked articles to look at the development of research topics and the structure of existing knowledge. We identified five significant themes based on exploratory factor and hierarchical cluster analyses. We posited that successful AI-based models could aid stock traders, brokers, and investors in better decision-making, a task that had previously been fraught with difficulties. Overall, this paper is aimed at benefiting stock traders, brokers, businesses, investors, government, financial institutions, depositories, and banks. This paper concludes with a future research agenda.

Living Labs (LLs) are now well-recognized in the development and commercialization of science—that is taking emerging discoveries from research labs to the industry. Whereas their widespread interest seems encouraging, it has been complicated by some inconsistencies regarding what LLs actually are, what types of activities they (should) support, and under what conditions. We conduct a meta-synthesis of 41 LL application patterns, supplemented by an in-depth empirical case study—the application of a MedTech LL in healthcare—to examine effective practices for organizing collaborative innovation within the science-industry nexus. By studying what worked best and under what conditions, we present a typology of eight design elements that underlie four unique LL models. Our analysis reveals that although these different models do fit under the same umbrella concept, they vary in the roles they serve in the collaborative innovation process. Most notably, we find that organizations need to navigate between these models as the collaboration moves from idea inception into impact. Our results offer relevant insights for understanding new forms of organizations for science-industry relations. We discuss practical implications for LL managers, researchers, and sponsoring institutions and conclude by outlining promising areas for future research.

Numerous enterprises employ Generative AI (GenAI) for a plethora of business operations, which can enhance organizational effectiveness. The adoption might be driven by multiple factors influencing the business landscape. Additionally, numerous ethical considerations could impact the deployment of GenAI. This unique study investigated how organizations adopt GenAI and its effects on their performance. Further, this research utilized institutional theory and ethical guidelines for AI design to develop a research framework examining how organizations adopt GenAI and its impact on their performance. A survey of 384 managers from information technology (IT) and information technology-enabled services (ITeS) companies was conducted. Data analysis was done using PLS-SEM to examine and validate the proposed model. The study outcome reveals that institutional pressures, i.e., coercive, normative and mimetic forces, influence the use of GenAI in organizations. It was also found that fairness, accountability, transparency, accuracy and autonomy influence the use of GenAI. Also, the results divulge that the use of GenAI influences organizational performance and is moderated by organizational innovativeness. This study provides insights to developers of GenAI, senior management of companies, the government and IT policymakers by highlighting the institutional pressures and ethical principles influencing the use of GenAI.

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.