Exploring Machine-based Idea Landscapes – The Impact of Granularity

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

Network Structure and User Roles of a Crowdsourcing Community – The Context of Social Innovations for a Development Project

The principles of crowdsourcing are increasingly applied in social contexts like development projects. In this study we explore a crowdsourcing community, which aims to enhance conditions in low income communities. We investigate the network structures of the community and detect behavioral pattern and user roles based on participation behavior for this specific context. Overall, the observed community shows a high level of collaboration and reciprocal dialogue. On the individual level we located four different user roles distinct in their interaction and contribution behavior. So called “collaborators” are considered as unique user role in an online community within a social context. We contribute to the theory of crowdsourcing by illustrating that context and purpose of crowdsourcing initiatives may influence the behavioral pattern of users. Further we add insights to the junctures between crowdsourcing and social innovation in the context of open development

Cash or Non-Cash? Unveiling Ideators' Incentive Preferences in Crowdsourcing Contests

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.Comment: Journal of Management Information Systems, forthcoming 202

From Concept to Creation: Artificial Intelligence in Innovation Teams

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

Fighting the wicked problem of plastic pollution and its consequences for developing regions with expert and crowd solutions

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

Mobility choices - an instrument for precise automatized travel behavior detection & analysis

Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires

Crowdsourcing strategy: how openness changes strategy work

Strategy development has traditionally been exclusive and secretive. Social software offers new opportunities to harness the collective intelligence of the crowd within organizations and allows more open, participatory modes of strategizing. This paper describes this new phenomenon of open strategy though crowdsourcing and discusses its implications for research and practice. It draws on first examples of crowdsourcing strategy and is further based on observations and theoretical reflections. To understand the phenomenon with its requirements and consequences, a number of questions and challenges are identified which remain to be investigated. These include how the process of opening up needs to be designed, how individuals can be motivated to engage, for which topics and under which conditions crowdsourcing strategy is a suitable approach, how strategies emerge in such initiatives, the appropriate role of management, and how corporate culture affects and is affected by crowdsourcing strategy. Open strategy through crowdsourcing is a newly emerging empirical phenomenon, which seems to fundamentally change the strategist’s work. More open and inclusive ways of strategizing not only offer new opportunities, but also create some challenges for organizations. This paper deepens the insights in this new phenomenon and identifies seven topics critical for research and management practice. Keywords: strategy, crowdsourcing, collective intelligence. JEL Classification: M1

The MOBI-Kids Study Protocol: Challenges in Assessing Childhood and Adolescent Exposure to Electromagnetic Fields from Wireless Telecommunication Technologies and Possible Association with Brain Tumor Risk

The rapid increase in mobile phone use in young people has generated concern about possible health effects of exposure to radiofrequency (RF) and extremely low frequency (ELF) electromagnetic fields (EMF). MOBI-Kids, a multinational case-control study, investigates the potential effects of childhood and adolescent exposure to EMF from mobile communications technologies on brain tumor risk in 14 countries. The study, which aims to include approximately 1,000 brain tumor cases aged 10-24 years and two individually matched controls for each case, follows a common protocol and builds upon the methodological experience of the INTERPHONE study. The design and conduct of a study on EMF exposure and brain tumor risk in young people in a large number of countries is complex and poses methodological challenges. This manuscript discusses the design of MOBI-Kids and describes the challenges and approaches chosen to address them, including: (1) the choice of controls operated for suspected appendicitis, to reduce potential selection bias related to low response rates among population controls; (2) investigating a young study population spanning a relatively wide age range; (3) conducting a large, multinational epidemiological study, while adhering to increasingly stricter ethics requirements; (4) investigating a rare and potentially fatal disease; and (5) assessing exposure to EMF from communication technologies. Our experience in thus far developing and implementing the study protocol indicates that MOBI-Kids is feasible and will generate results that will contribute to the understanding of potential brain tumor risks associated with use of mobile phones and other wireless communications technologies among young people