Generative AI (GenAI) offers significant potential to boost innovation performance by accelerating speed and productivity. However, its integration into collaborative, empathy-driven processes like design thinking remains a complex challenge. Drawing on qualitative data from three innovation sprints involving 71 interdisciplinary teams and 18 corporate partners, we investigate how GenAI impacts the core design phases of problem understanding, ideation, and prototyping. Our findings demonstrate that while GenAI effectively expands the problem space and generates diverse solutions, its integration varies significantly across teams. We identify a critical tension: although GenAI can enhance early-stage divergence, its output often lacks the contextual and empathic depth required for user-centered innovation. To address this, we propose a 2×2 framework defined by two dimensions – Level of Integration (High vs. Low) and Purpose of Use (Supportive vs. Directive) – identifying four distinct engagement patterns. This typology illustrates the trade-offs between productivity and team-based empathy and coordination, providing a nuanced perspective on how AI reshapes different stages of the design process. Finally, we offer practical guidance on team orchestration and intervention strategies to ensure GenAI augments, rather than undermines, collaborative creativity.

Even though research has repeatedly shown that non-cash incentives can be effective, cash incentives are the de facto standard in crowdsourcing contests. In this multi-study research, we quantify ideators' preferences for non-cash incentives and investigate how allowing ideators to self-select their preferred incentive -- offering ideators a choice between cash and non-cash incentives -- affects their creative performance. We further explore whether the market context of the organization hosting the contest -- social (non-profit) or monetary (for-profit) -- moderates incentive preferences and their effectiveness. We find that individuals exhibit heterogeneous incentive preferences and often prefer non-cash incentives, even in for-profit contexts. Offering ideators a choice of incentives can enhance creative performance. Market context moderates the effect of incentives, such that ideators who receive non-cash incentives in for-profit contexts tend to exert less effort. We show that heterogeneity of ideators' preferences (and the ability to satisfy diverse preferences with suitably diverse incentive options) is a critical boundary condition to realizing benefits from offering ideators a choice of incentives. We provide managers with guidance to design effective incentives by improving incentive-preference fit for ideators.

Recent developments of generative artificial intelligence (GAI) introduce unprecedented opportunities that are believed to enhance individual skills, particularly creativity, while improving team collaboration. Thus, these novel uses of GAI both on the individual and collective level have the potential to augment the innovation process within teams. Nevertheless, little is understood about how teams leverage GAI to enhance the innovation process. Using affordance theory, this study conducts a field study encompassing 18 teams, with 83 participants to understand the use of GAI during the innovation process. Our findings reveal that profoundly enhance the capacities of teams to innovate by generating, improving, automating, and stimulating sophisticated creative tasks. However, the main benefits of GAI appear to be confined to specific tasks rather than enhancing innovation itself. Our study is expected to contribute to research on the use of GAI at the team level, particularly in the innovation context, and advance affordance theory.

Current approaches for identifying valuable content among the multitude of solutions in crowdsourcing contests are resource-intensive and constrained by human processing capacity. As idea convergence processes usually focus on filtering out single ideas, the potential of solution-related knowledge among the heterogeneous ideas is not exploited in a sustainable manner. Transformer-based language models can process large sets of idea descriptions into digestible structures, with unprecedented capabilities for understanding and manipulating text. This study explores how they can help organizations and decision-makers navigate crowdsourced solution spaces efficiently and comprehensively. Inspired by theoretical concepts around problem-solving and innovation search, we conceptualize three related search practices-direct search, cluster exploration and pattern discovery-and illustrate them on 289 crowdsourced ideas for future mobility and energy services. Direct search can assist in identifying solutions that match pressing needs or subproblems. Cluster exploration enables aggregating semantically similar ideas into clusters to identify relevant needs. Pattern discovery synthesizes themes and interrelations to build a holistic understanding of potential solutions. The study contributes to the application of AI-assisted idea convergence by adding a new perspective beyond filtering out a few promising ideas.

Processing large and heterogeneous numbers of ideas submitted to crowdsourcing contests is a regular challenge for idea evaluators. The aim of this study is to investigate a potential use case for AI-based innovation management and to extend the knowledge of using automated novelty detection in idea evaluation processes. AI-based language models can automatically allocate short texts according to their semantic similarity in an embedded space. We represent the semantic content of crowdsourced ideas with the three contemporary text embeddings - Doc2Vec, SBERT, and GPT-3-based Ada Similarity - and compute their semantic distance to different reference sets using different novelty detection algorithms. We then compare the algorithm-generated scores with human novelty assessments to validate them. While selected novelty scores based on text embeddings correlate with humans, our results show that scores based on SBERT embeddings best match human novelty assessments. We also find that AI-based novelty detection approaches perform better for ideas below the median word count and when compared to a set of existing solutions, suggesting that the chosen language model is not the only factor influencing the applicability of the proposed approach. Furthermore, the study highlights important features and limitations of automatically generated novelty scores that need to be considered when complementing evaluators searching for new ideas in crowdsourcing contests and beyond.

This paper examines the transformative impact of Artificial Intelligence (AI) on innovation management, highlighting the profound organizational shifts necessary to fully leverage its potential. As AI adoption surges, organizations face unprecedented opportunities to accelerate their innovation processes, from early trend identification to product diffusion. However, realizing these benefits requires a comprehensive strategic alignment. This includes rethinking innovation strategy, restructuring organizational set-up, redefining roles, and collaborative practices. By examining these pivotal aspects, this paper identifies critical knowledge gaps and further poses thought-provoking questions designed to stimulate both academic inquiry and managerial innovation. Our goal is to inspire researchers and managers to rethink their approach to innovation management in an AI-driven landscape.

The global demand for new work concepts and the rapid expansion of collaborative information technology (CIT) have fundamentally changed how organizations collaborate and innovate. Based on the literature of innovation management, we aimed to elucidate the pervasiveness of CIT and the global demand for flexible means of innovation and their impacts on facets of innovation processes. Our action research revealed how hybrid work has changed an innovation agency’s innovation process from running primarily in person to being almost fully remote and, in turn, following a hybrid approach. Our findings emphasize that the combination of certain tasks (e.g., novel innovation processes), human resource management (e.g., of team characteristics, leadership, and culture), and appropriate infrastructure (e.g., digital tools) has been essential for the agency to innovate in a hybrid setting. Moreover, rearranging its innovation processes for the new setting allowed the reconfiguration of people and innovation teams. Our results also indicate that building relationships, albeit crucial, presents significant challenges in hybrid settings and that though CIT tools have to be implemented, they do not necessarily foster innovation. Indeed, enhancing innovation capabilities requires understanding how to leverage those tools according to frequency, timing, and purpose. Meanwhile, in hybrid settings, offices can serve as a physical space for inspiration, personal interaction, and (tacit) knowledge exchange. Given those findings, the proper management of innovation tools and methods between remote and in-person work environments will be essential for future innovation managers.

This research investigates how one can predict idea evaluations and investment intentions by individuals' affective reactions to idea pitches. The generation and selection of ideas are fundamental for successful innovation, but companies tend to neglect emotions in the idea evaluation process. Affective Computing, a subfield of computer science that deals with recognizing affective states and emotions, makes it possible to study the affective reaction of subjects to product pitches or prototypes in an economical, low-threshold way. In this study, 60 German and Austrian participants watched 6 product pitches with ideas on how a major German electronics chain can better position itself in the well-being sector. Participants' facial expressions were analyzed to collect affective data on valence and arousal dimensions. Subsequently, participants rated the ideas, indicated their likelihood to invest, and reported their attitude toward the ideas presented. The results show that while valence and arousal alone could not predict idea evaluation, investment intention, or attitude, their interaction could. Furthermore, we demonstrated that idea evaluation and investment intention mediate the effect of affect on attitude. Finally, we showed that emotional expressivity and mood are essential in idea evaluation and investment intentions. This study contributes to a better understanding of how affect influences idea evaluations and attitudes and how Affective Computing may aid in idea selection.

PurposeThis paper aims to examine the key challenges experienced and lessons learned when organizations undergo large-scale agile transformations and seeks to answer the question of how incumbent firms achieve agility at scale.Design/methodology/approachBuilding on a case study of a multinational corporation seeking to scale up agility, the authors combined 36 semistructured interviews with secondary data from the organization to analyze its transformation since the early planning period.FindingsThe results show how incumbent firms develop and successfully integrate agility-enhancing capabilities to sense, seize and transform in times of digital transformation and rapid change. The findings highlight how agility can be established initially at the divisional level, namely with a key accelerator in the form of a center of competence, and later prepared to be scaled up across the organization. Moreover, the authors abstract and organize the findings according to the dynamic capabilities framework and offer propositions of how companies can achieve organizational agility by scaling up agility from a divisional to an organizational level.Practical implicationsAlong with in-depth insights into agile transformations, this article provides practitioners with guidance for developing agility-enhancing capabilities within incumbent organizations and creating, scaling and managing agility across them.Originality/valueExamining the case of a multinational corporation's exceptional, pioneering effort to scale agility, this article addresses the strategic importance of agility and explains how organizational agility can serve incumbent firms in industries characterized by uncertainty and intense competition.

The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations. Our exploratory study among 150 AI-savvy innovation managers reveals four different clusters in terms of how organizations may use and implement AI in their innovation management ranging from (1) AI-Frontrunners, (2) AI-Practitioners, and (3) AI-Occasional innovators to (4) Non-AI innovators. The different groups vary not only in their strategy, organizational structure, and skill-building but also in their perceived potential, understanding of the required changes, encountered challenges, and organizational contexts. Our study contributes to a better understanding of the current state of AI-based innovation management, its impact on future innovation practice, and differences in organizations’ AI ambitions and chosen implementation approaches.

Effective exploration of a landscape full of crowdsourced ideas depends on the right search strategy, as well as the level of granularity in the representation.To categorize similar ideas on different granularity levels modern natural language processing methods and clustering algorithms can be usefully applied.However, the value of machine-based categorizations is dependent on their comprehensibility and coherence with human similarity perceptions.We find that machine-based and human similarity allocations are more likely to converge when comparing ideas across more distant solution clusters than within closely related ones.Our exploratory study contributes to research on the navigability of idea landscapes, by pointing out the impact of granularity on the exploration of crowdsourced knowledge.For practitioners, we provide insights on how to organize the search for the best possible solutions and control the cognitive demand of searchers.

In this research, we explore how crowdsourcing combined with text-mining can help to build a sound understanding of unstructured, complex and ill-defined problems. Therefore, we gathered 101 problem descriptions contributed to a crowdsourcing contest about the impact of COVID-19 on the tourism industry. Based on our findings we propose a five-phase process model for problem understanding consisting of: (1) information gathering, (2) information pre-structuring, (3) problem space mapping, (4) problem space exploration, and (5) problem understanding for solution search. While our study confirms that crowdsourcing and text-mining facilitate fast generation and exploration of problem spaces at limited cost, it also reveals the necessity to follow certain process steps and to deal with challenges such as information loss and human interpretation. For practitioners, our model presents a guideline for how to get a faster grasp on complex and rather unprecedented problems.

In a world typified by uncertainty and unprecedented change, agility is the key theme to innovation and essential for maintaining competitiveness. There is a notable gap between the concepts’ popularity and the understanding of how agile practices influence firms’ innovation processes and output. This study draws on the case of a leading family-owned manufacturing firm in the dental supplier industry transforming their product innovation department by means of agile practices. Building on theoretical concepts of innovation management and agility we study the adoption, use and efficacy of agile practices to facilitate companies’ innovation processes. Our findings highlight the importance of leadership capabilities that encourage individuals’ innovation activities. We found that an organization-specific innovative culture and organizational commitment are vital to facilitating the agile transition. These factors enhance employees’ beliefs and willingness to contribute to the transformation. Additionally, we discuss learnings and insights for managers operating non-software producing areas, searching for approaches to manage complexity and being not naturally firm with agile transitions.

Seeking inspiration from other perspectives is a prominent mechanism to support ideation. AI-based language models can help overcome information processing limits and efficiently structure large solution spaces spanned by prior ideas. However, it remains unclear how the search through a solution space affects the subsequent idea generation. This study explores the influence of different sets of prior idea stimuli pre-structured by an AI-supported clustering on ideation outcomes. The sets varied in quantity and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively interacts with domain-specific knowledge. However, it negatively affects idea feasibility and specificity. These findings encourage innovators seeking particularly novel ideas to complement their current processes with AI-supported clustering tools while taking steps to avoid vagueness.

The boundaries of rational decision-making in managerial conditions of uncertainty and time pressure lead to several studies investigating the importance of the complementary role of intuition in such events. While for example Coget and Keller (2010) or Okoli and Watt (2018) show that in crises intuition is applied to validate decisions and is strongly related to emotions and rational thinking, it is still uncertain how that applies to managers and fundamental crises conditions such as the Covid-19 pandemic. Therefore, we investigated through in-depth qualitative interviews with 17 long-term experienced high-level managers at the beginning of the pandemic how managers decide during the Covid-19 pandemic and what role intuition plays thereby. We found that intuition is applied specifically by providing direction, assessing the situation and information, reviewing and verifying decisions, as well as handling interpersonal topics, and creating possible solutions to problems. Moreover, we propose that managers profit from their gut feeling in crises, however, dependent on their preference and level of responsibility. We provide a specific analysis of the application of intuition in managerial crisis decision-making and thereby a more comprehensive view than the few existing works. Furthermore, we recommend how managers might apply this knowledge to their decision-making practice.

Agile leadership is "in vogue" in today's fast changing and increasingly digitally connected world of work, yet many business leaders struggle to adapt their leadership skills accordingly. Using insights drawn from transformational leadership theory and a case study of the IT subsidiary of Deutsche Bahn, we present a guide for future management skills to create sustainable organizations for a better world. This article argues for the importance of transformational leadership traits, including empowerment, trust, openness, and coaching, in successful agile leadership. Furthermore, affective factors, namely mindfulness and empathy, are important future leadership competencies, especially in a digital work environment. In addition to these specific soft skills derived from transformational leadership theory, managers also need to be equipped with certain hard skills related to technological expertise as well as practical knowledge of agile methods. Finally, the paper addresses the competence to act in network structures as a key success factor for executives and recognize an ongoing paradigm shift from centralized to shared leadership and from the individual to the team perspective.

While crowdsourcing may strengthen a company's innovation performance, it is only rarely embedded in organizations as an innovation practice. Our action research shows that organizations often struggle with crowdsourcing projects as they represent a different form of innovation projects and require additional capabilities and skills e.g., to frame a crowd challenge, deal with IP rights, manage the crowd, or integrate the vast input into innovation projects. To overcome these problems, organizations have to invest in project-led learning to establish easy-to-use templates and routines e.g., to handle offered incentives or the applied winner selection processes. They further need to enable business-led learning as the established innovation structures, processes, and management practices do not cope with crowdsourcing projects and present some rigidities causing high coordination efforts. Organizations that are willing to run a number of consecutive crowdsourcing projects may rapidly improve their capabilities and even come up with scalable crowdsourcing services. Our findings further suggest that crowdsourcing, digital platforms, artificial intelligence, and as-a-service approaches may also add to general project capability building.

Shared beliefs on digital readiness among management and employees are a precursor to successfully guide and implement organizational change. There is, however, little examination of how digital knowledge and skills are distributed among managers and employees, or whether their perceptions of digital readiness systematically differ. The findings of a survey of the banking industry reveal that, while there are similar perceptions of attitude and empowerment toward change, perceptions of individual readiness, competences, and innovation barriers differ significantly. This research advances the framework of change readiness toward digital readiness with theoretical as well as practical implications for digital transformation management.

The wicked problem of plastic pollution is one of the key global challenges. Finding adequate solutions to this complex problem requires cross-cultural and inter-organizational collaboration among diverse sets of stakeholders. In this context, the Ellen Mac Arthur Foundation approaches the problem of plastic pollution not only by involving experts into innovation processes but also by integrating the general public in form of an IT enabled crowdsourcing initiative. In this study, we analyze the outcomes of these actions with the help of automated text mining techniques. Our analysis demonstrates significant differences between the solutions given by experts and the crowd along various criteria. Further, this study provides guidance for practitioners on how to integrate diverse sets of individuals in problem solving processes with the help of information systems technologies. Especially for sustainability issues affecting both, developed and developing regions.