User generated brands and their contribution to the diffusion of user innovations

It has been argued that users can create innovations and also diffuse them peer-to-peer independent of support or involvement by producers: that “user-only” innovation systems can exist. It is known that users can be incented to innovate via benefits from in-house use. But users’ incentives to invest in diffusion are much less clear: benefits that others might obtain from their innovation can be largely or entirely an externality for user innovators. Of course, effective distribution of information products can be done near-costlessly via posting downloadable content – for example, software – on the Internet. However, potential adopters must still learn about the product and trust its qualities. In producer systems, this aspect of diffusion is heavily supported via the creation of trusted brands. It has been shown that brands help to increase awareness, to communicate a product's benefits, and to reduce perceived risks of adoption. The development of brands by producers is traditionally seen as a very costly exercise – unlikely to be thought of as worthwhile by users who expect little or no benefits from the diffusion of their innovations to others. In this paper, we explore the creation of a strong and trusted brand by the Apache software community – and find it was created costlessly, as a side effect of normal community functioning. We think the costless creation of strong brands is an option that is generally available to user innovation communities. It supports, we propose, the existence of robust, user-only innovation systems by helping to solve the problem of low-cost diffusion of trusted user-developed innovations

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

The More the Merrier? The Effects of Community Feedback on Idea Quality in Innovation Contests

Innovation contests represent a novel and popular approach for organizations to leverage the creativity of the crowd for organizational innovations. In this approach, ideators present their initial ideas to a global community of potential users, and solicit their feedback for idea improvement or refinement. However, it is not clear which types of feedback lead to the development of better ideas and which contingent factors moderate these relationships. In this study, we examine the role of community feedback on idea development in online innovation contests, by using feedback intervention theory to develop a set of hypotheses relating community feedback and idea quality, and then testing those hypotheses using data from ZEISS VR ONE innovation contest. Our analysis suggest that task information feedback does lead to improvement in idea quality, while task learning and task motivation feedback does not, and the number of users providing feedback moderate the relationship between feedback and idea quality. Implications of our findings for theory and practice are discussed

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

How Text Mining Algorithms for Crowdsourcing Can Help Us to Identify Today's Pressing Societal Issues

Crowdsourcing is increasingly applied in the area of open development with the goal to find solutions for today’s pressing societal issues. To solve such wicked problems, manifold solutions need to be found and applied. In contrast to this, most recent research in crowdsourcing focuses on the few winning ideas, ignoring the sheer amount of content created by the community. In this study we address this issue by applying an automated text mining technique to analyze the ideas contributed by the crowd in an initiative tackling plastic pollution. We show that automated text mining approaches reveal numerous possibilities to make use of the so far unused content of IT enabled collaboration projects. We further add insights into how our findings can help researchers and practitioners to accelerate the solution process for today’s pressing societal issues

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

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