Technovation最新文献

The excitement surrounding Artificial Intelligence (AI) is palpable. It is rapidly gaining prevalence in academia, business, and personal use. In particular, the emergence of generative AI, exemplified by large language models such as ChatGPT, has been marked by substantial media attention, discourse, and hype. Like most, if not all, aspects of business, innovation processes have been impacted. However, little is known about the degree of impact or the benefits that might be gained. To cut through the hype and understand the use of AI in innovation processes in businesses today, a large-scale survey amongst innovation managers in the USA was conducted, followed by interviews. The findings indicate that the use of AI in innovation processes is high and widespread, with AI being used for more than half of the surveyed firms' innovation projects. Furthermore, AI is used more in the development stage of the innovation process than in the idea or commercialization stages, which counters much of the existing discourse, which focuses on the idea stage. We uncover interesting differences by comparing the use and impact of generative AI with that of more traditional AI. Among these is a significant difference in expected benefits in making employees’ jobs more fulfilling — managers believe generative AI is more likely to confer this benefit than traditional AI. This paper offers two valuable contributions. First, it enriches the evolving dialogue at the intersection of AI and innovation management by offering much-needed empirical evidence about practical applications. Second, it provides timely managerial implications by examining relationships between the use of AI and innovation performance and understanding the benefits that AI can confer in the innovation process.

Prior literature has long recognized the substantial economic value that patents hold in the market. Yet, we know much less about the valuation process, i.e., how market audiences estimate (or determine) the value of newly granted patents. Building on behavioral economics, we propose the anchoring effect as an important cognitive mechanism, such that a patent's valuation is anchored on the value that preceding patents have secured. Analyzing financial valuation of U.S. patents between 1991 and 2010, we find broad support to the anchoring effect. The effect is more pronounced when focal patents are of lower novelty, when prior anchors are more consistent, and when focal firms have a higher patenting frequency. Furthermore, our extensional analysis suggests that anchoring acts as an important driver for the divergence between patents' economic value and scientific quality, which deserves attention from firms and policy makers.

This study investigates inter-firm relationships’ distinct impacts on entrepreneurial proactiveness (EP) and exploitative innovation strategy. Although the existing literature on entrepreneurs’ innovation strategies has consistently found a positive relationship between EP and exploration, the mechanisms underlying the inconsistent relationship between EP and exploitative innovation strategy remain unclear. We anticipate that applying brokerage position in network theory would offer further insights into the relationship between EP and exploitative innovation strategy. Additionally, we introduced depth of openness as a moderating variable and examined its effect on the moderated mediation relationship. To test hypotheses, this study utilized linear and logistic regression, bootstrapping, and the Johnson-Neyman technique. The findings of the 2020 Korean Innovation Survey, conducted by the Science and Technology Policy Institute, indicate that data from 2352 Korean manufacturing companies exhibits that the brokerage position mediates EP and exploitative innovation strategy. Moreover, the depth of openness mitigated the positive relationship between the brokerage position and the exploitative innovation strategy and the indirect effect of EP on exploitative innovation strategy through the brokerage position. These findings contribute theoretically to entrepreneurial orientation and underscore the practical significance of aligning company goals with social relationships.

While existing literature acknowledges the role of science fiction in foreseeing technological advancements, a notable gap persists in understanding the underlying factors that drive or hinder individuals from the intention to generate and promote ideas gathered through science fiction. Our research model aims to shed novel light on what factors influence employees’ propensity to generate and promote ideas inspired by science fiction, through the lenses of institutional theory. Purposefully, we frame science fiction methodologies inside the Innovative Work Behavior discourse, as science fiction could be a fruitful tool to generate and promote ideas, and we investigate the interplay of rational and institutional influences on such behaviors. The findings provide valuable insights that can be leveraged to design and implement effective methodologies within organizational settings. Our study, based on data from 480 employees, employs Structural Equation Modeling to reveal the pivotal role of normative influence in idea generation, while idea promotion exhibits a robust association with cultural-cognitive influence, pinpointing the dual phase of science fiction methodologies.

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.