Creativity and Innovation Management最新文献

Innovation contests represent a type of open innovation that companies can use for gaining access to innovation ideas from different target groups. These innovation ideas have a measurable creative output and arise in an ideation process that usually takes place unconsciously among the participants, but can be traced back through operationalization with the inventive principles from the Theory of Inventive Problem Solving (TRIZ). We evaluate these two aspects—creative output and ideation process—on the basis of over 600 innovation ideas collected from more than 1200 students during eight identical courses on ‘Innovation Management’ between 2015 and 2022, using a Large Language Model (GPT-4). We identify two important relationships between the two variables. Firstly, our data show that the more inventive principles are used in the ideation process, the higher the creative output of an innovation idea. Secondly, at least in our setting, universal inventive principles outperform technological inventive principles in terms of creative output. This study is the first to use GPT-4 to simultaneously assess both the creative output of more than 600 innovation ideas and the hidden TRIZ inventive principles behind them.

Traditional techniques for evaluating creative outcomes are typically based on evaluations made by human experts. These methods suffer from challenges such as subjectivity, biases, limited availability, ‘crowding’, and high transaction costs. We propose that large language models (LLMs) can be used to overcome these shortcomings. However, there is a dearth of research comparing the performance of LLMs to traditional expert evaluations for evaluating creative outcomes such as ideas. Our study compares the alignment of expert evaluations with evaluations from the LLM GPT-4. Our results reveal that to achieve moderate evaluation alignment with experts, LLMs require using a base framework and a spectrum-based few-shot prompt. We offer six theoretical contributions, shifting the focus from whether LLMs can evaluate to how specific design choices shape their alignment with human judgement. These insights are situated within broader frameworks from cognitive science, creativity theory, and machine learning. Furthermore, we outline six propositions for organizations interested in LLM-supported evaluation methods. Key recommendations include utilizing base frameworks for large-scale idea screening, establishing a database of evaluated ideas to optimize few-shot performance, and leveraging AI–human collaboration for internal and external idea sourcing. Additionally, we highlight the need for privacy considerations when using third-party LLMs for proprietary idea evaluations. This research contributes to innovation management literature by exploring methods for integrating LLM into creative evaluation processes to enhance scalability and efficiency while retaining evaluation quality.

This study draws upon the self-determination theory and the ambidexterity theory to explore the microfoundations of task performance; in particular, it examines the effect of job autonomy on task performance. While prior studies already supported the existence of a relationship between autonomy and task performance, in this research we advance the thesis that such a relationship is influenced by multiple intervening factors. In particular, that innovative work behaviour (IWB) plays a mediating role, while team ambidexterity has a potential moderating role. To substantiate these claims, an empirical investigation was conducted using survey data from 791 employees and their supervisors across diverse industries in Montenegro. The findings lend support to the mediation hypothesis while indicating also that team ambidexterity might strengthen the effect of IWB on task performance. Results highlight the importance of balancing autonomy with structured team dynamics to optimize employee performance, particularly in innovation-driven environments.

Lead user theory has gained traction in both academia and practice, given that involving lead users (LUs) can yield more innovative and commercially successful products. Yet, measurement inconsistencies have hindered consensus on how to reliably assess lead userness. This paper develops and validates a new 30-item scale that conceptualizes lead userness as a hierarchical construct with two second-order dimensions: ahead of trend (AoT) and high benefit expectation (HBE). Each dimension is further subdivided into three first-order subdimensions, capturing facets such as ideas, information and experience (AoT) or problems, needs and gains (HBE). In multiple studies, the scale demonstrated robust reliability and convergent, discriminant and nomological validity, correlating as expected with key antecedents (e.g., divergent thinking, product knowledge, innate innovativeness and risk aversion) and outcomes (e.g., social innovativeness, opinion leadership, innovation experience and innovation intention). Additionally, a separate predictive and explanatory power study comparing the new scale with seven established measures underscored its superior capacity to account for both past and future user-driven innovation activities. Moreover, a market test across smartphones, e-bikes and refrigerators confirmed that product concepts developed by identified LUs commanded higher willingness to pay than those from regular consumers, reinforcing the scale's practical relevance. Finally, a short-scale variant with six items is introduced as a viable alternative when item minimization is crucial. Taken together, these findings provide a comprehensive inventory for measuring lead userness, laying the foundation for deeper theoretical insights into user-driven innovation and more targeted managerial strategies for collaborating with LUs across diverse product categories.

Corporate venture clienting (CV Clienting) has emerged as a distinct form of corporate venturing (CV) in which corporations engage with startups by adopting their innovative solutions without acquiring equity stakes. Unlike traditional corporate venture capital (CV Capital), which served as the dominant CV approach, CV Clienting structures these relationships around supplier–client dynamics, enabling corporations to access pioneering technologies, foster the growth of future suppliers and minimize long-term capital commitments. However, despite its growing relevance, the governance mechanisms specific to CV Clienting remain underexplored. This study investigates the governance mechanisms underpinning CV Clienting initiatives from a stewardship theory perspective. Drawing on an abductive qualitative analysis of three corporations actively implementing CV Clienting, this research identifies three key dimensions of governance (i.e., formal, relational and transactional) that differentiate CV Clienting from CV Capital. This study shows that stewardship theory can provide a grounding model to support corporates in navigating the governance challenges of CV Clienting when interacting with startups.

The originality of new product ideas in the front-end is crucial for new product development (NPD) success. To foster originality, organisations use idea sprints, where distributed and remote NPD teams working in agile develop ideas under fixed time and cost constraints, exchanging outputs virtually. These virtual transfers, via tools like email, include both product-related factors like idea descriptions and person-related factors like emotions. Emotions expressed about idea descriptions can, however, be distorted during virtual transfers, possibly influencing idea elaboration. Despite their frequent occurrence and potential impact, this issue remains unexplored in the literature. Drawing on emotions as social information (EASI) theory, we test if the expression of excitement reduces, while the expression of frustration increases the originality of new product ideas generated in idea sprints. The results of an online experiment with professional participants in the role of either team member (i.e., idea generator or elaborator) or product owner (i.e., evaluator) during a simulated idea sprint unexpectedly show that both emotions, if expressed by the generator in the virtual transfer to the elaborator, reduce the originality of the new product ideas as evaluated by the product owner. These findings enrich our limited understanding of the role of expressed emotions in virtual transfers for ideation activities in the front-end of NPD.

Generative artificial intelligence (GenAI) is transforming the way people approach creative problem-solving (CPS). Whereas previous technologies were only used as creativity tools, GenAI can also be conceived as an actor taking on different roles that adds a collaborative element to individual CPS processes. So far, research on augmenting CPS with GenAI has focused on creative cognition. In this article, we use a metacognitive perspective as a unique lens to conceptualize AI-augmented approaches to CPS and explore the implications of AI-augmented creative cognition. Based on this, we propose that the collaboration with GenAI in CPS shifts a part of human attention away from executing to monitoring and controlling creative cognition. These imposed metacognitive demands have important implications for our theoretical understanding of AI-augmented CPS and how people can better actualize the creative potential of GenAI. Future research directions and empirical approaches for understanding and addressing these metacognitive demands are discussed along with practical implications for applying CPS.