Technovation最新文献

Healthcare professionals' adoption of innovations is crucial for the success and positive impact of innovations. Realizing the important role of knowledge inertia, this research, adopting social exchange perspectives, investigates its impact at the individual level among healthcare professionals and reveals the mechanisms either mitigating or deteriorating its impact on professionals' adoption of innovations. A survey study involving 337 clinical physicians from two specialized and three comprehensive hospitals reveals a negative impact of learning inertia but a positive impact of experience inertia on their intention to adopt innovations. Perceived organizational support mitigates the positive impact of experience inertia and knowledge potential mitigates the impact of both dimensions of knowledge inertia. By focusing on professionals’ knowledge inertia, we have revealed the distinctive effects and the mechanisms that leverage or mitigate these effects, deepening the understanding of professionals and innovation adoption.

The tools for crowdsourcing have been a research focus for quite some time. However, even today, crowdsourcing platforms have remained rather technologically rudimentary as simply idea dropboxes and discussion forums. Our research has indicated that technical modifications to current crowdsourcing platforms are needed for crowds to generate more novel and useful solutions. In this article, we briefly describe basic technology requirements to foster collective co-production. Then, we suggest further digital innovations involving the use of AI technologies that are needed to maximize the potential of crowdsourcing.

In a rapidly evolving technological landscape, firms are strategically diversifying into multiple tech sectors, blurring traditional industry lines through ‘industry convergence’. To comprehend the inception of technology convergence from the roots of knowledge, this study bridges knowledge flow framework with the dynamic capabilities theory, considering the dual roles of firms in knowledge flow and whether they engage in pioneering or follower-type technological innovations. Through this integrated approach, this study classifies technology convergence into four distinct patterns. An analysis of USPTO data spanning 1976–2019 underscores that as knowledge matures, its application in convergence processes intensifies. Pioneers exhibit faster knowledge advancement and maintain focused partnerships, whereas followers diversify across disciplines and pursue extensive collaborations. These results highlight the strategic importance for firms to coordinate their knowledge-based attributes with their technological progression pace. Firms should strategically harmonize their innovation approaches with their market stance to fully leverage the advantages of technology convergence.

This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.

The aim of this paper is dual: a) to elaborate a systemic concept of intrapreneurship in academia which will be reflective of complexities and idiosyncrasies of the University as a multi-mission multi-function organisation; and b) to empirically test this concept in the context of relatively neglected outputs of academic intrapreneurship such as graduate employability and graduate wellbeing. Guided by an intrapreneurial ecosystem framework, our analysis is based on a combination of administrative and survey panel data for 141 UK higher education institutions. We arrive at several contributing findings. These suggest that the intrapreneurial ecosystem within academia is increasingly predisposed towards enhancing the quality of graduate outcomes rather than simply promoting raw measures such as graduate jobs or the number of graduate start-ups. Furthermore, we reveal the critical role of University national stakeholder networks for graduate career satisfaction, start-up generation, and undergraduate employment. Finally, our empirical exercise and its results demonstrate the practical value of the proposed concept of the intrapreneurial ecosystem for University management and practitioners, not least because it systematically identifies areas for an immediate, medium- and long-term action.

Although a number of studies have explored science fiction prototyping as a method for new product development, no study has ever used the method to examine the mobility and transportation technology needs of people with disabilities. The current research created a science fiction prototype, based on expert opinion expressed during an imagination workshop, which the authors then presented to a sample of people with ambulatory disabilities. Through a conjoint analysis, the sample members delineated the elements of the prototype they regarded as most important. The participants considered personal mobility assistive technology (either an automated wheelchair or an exoskeleton) the most important, followed by personal automation (autonomous [driverless] vehicle or personal robot) and thirdly by personal assistance technologies (real-time response versus augmented metaverse planning systems). Outputs to the conjoint analysis were clustered and three categories of individual emerged (i) more innovatively minded people who occupied the first cluster and preferred an exoskeleton, a personal robot and a subscription to the metaverse, (ii) people who appeared to be less technologically inclined and preferred an automated wheelchair, an autonomous vehicle and a subscription to a real-time assistive system, and (iii) a group with members favouring an AV but with few other predilections.

The technological and market containment of emerging economy enterprises (EEEs) from developed countries is becoming a great challenge for EEE development. In this context, how do EEEs respond to international business challenges and achieve continuous organizational innovation? This study proposes a process model of EEEs’ organizational adaptive change to achieve innovation using the case of Hikvision, a leading Chinese artificial intelligence (AI) enterprise. The results show that the adaptive change in EEEs is a dynamic process. The negative impact of international business environment challenges on enterprise development is the starting point for enterprises to adopt change behaviors. Through adaptive change in innovation strategy, innovation architecture and innovation systems, EEEs can break through technology blockades, overcome market crises and further weaken the negative impact of such challenges. This study constructs an adaptive change process model for EEEs and proposes strategic initiatives for EEEs with the help of a national innovation system (NIS), which has policy implications for EEE strategies and industrial policy design in emerging economies.

In this carbon neutrality era, digitalization is booming and emerging as having an essential role in corporate green technology innovation (GTI). However, as corporate digitalization is a systematic project involving various aspects, research into its role and mechanisms in shaping GTI is expected to be enriched. Given the complexity of measuring corporate digitalization, we first provide a measure of firm-level digitalization by applying natural language processing techniques and unsupervised machine learning modelling to management discussion and analysis text. Further, drawing on the resource-based view, we reveal a positive association between corporate digitalization and GTI, and find that corporate digitalization contributes to GTI by improving resource endowments management including resource accumulation, utilization, and allocation. As carbon neutrality and digitalization are in full swing, this study advances research on digitalization and highlights its role in sustainable corporate transformation.

Research on innovation failure has proliferated lately but with little theoretical attention given to the diversity of the concept. Using process theorizing, we present a model and propositions to understand how a firm's anticipation and value toward failure depends on the type of failure (task versus outcome) and the phase (divergent versus convergent) and point (early versus later) ‘within’ the process that the failure occurs. Using the anticipation-value stances, we then present a typology of four modes of innovation failure that can arise ‘from’ task and outcomes failure in the innovation process. The four modes (and associated learning response) are unsolicited failures (prevent-alert-eliminate); hazardous failures (predict-modify-mitigate); fortuitous failures (probe-expose-extrapolate); and excursive failures (facilitate-analyze-harness). To help explain the ideas in our process model and typology, we use the well-known IDEO shopping cart innovation project as an illustrative example. Together, these contributions provide contingency oriented insights on how failure varies and journeys within and from the innovation process, which helps researchers and managers to better understand the related causes, effects and learning responses.

When introducing a new product, firms must decide how to differentiate it from competitors through product positioning. In the low-carbon industry, positioning affective features of the products effectively gives firm a competitive edge. By applying blockchain to online product communities (OPCs), firms can gain insights into consumers' affective requirements during product positioning. In this study, we develop a game-theoretic framework to explore how to optimize affective design of low-carbon products with blockchain application. We find that firms' affective product design depends on two conflicting forces. On one hand, blockchain adoption in OPCs enhances estimation precision, motivating firms to provide mass products that meet mainstream marketplace requirements. On the other hand, blockchain implementation improves estimation similarity, inducing firms to target niche markets. The network effect exacerbates these two conflicting forces, and the trade-off between them determines firms' product design decisions. Additionally, we examine the impacts of blockchain adoption on firms' profits, environmental performance, consumer surplus, and social welfare. We discover that blockchain application in OPCs is always socially beneficial. However, blockchain adoption's effect on economic and environmental performance, as well as consumer surplus, is contingent on the significance of the conflicting forces. We further conduct a survey and in-depth interviews to validate these findings.