Knowledge that Transforms

Recently, large companies have been experimenting with corporate prediction markets run among their employees. In the present study, we develop an analytical model to analyze the effects of information precision and social interactions on prediction market performance. We find that increased precision of public information is not always beneficial to the prediction market accuracy because of the “hidden profiles” effect: the information-aggregation mechanism places a larger-than-efficient weight on existing public information. We show that a socially embedded prediction market with information sharing among participants may help correct such inefficiency and improve prediction market performance. We also identify conditions under which increased precision of public information is detrimental in a nonnetworked prediction market and in a socially embedded prediction market. These results should be of interest to practitioners as the managerial implications highlight the detrimental effect of public information and the role of social networking among employees in a corporate prediction market.

Prediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a belief–decision framework as the event probability estimator. This belief–decision framework models bettors’ beliefs with mixed beta distributions and bettors’ decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold.

In this paper, we argue that the use of technology is structured not only by users, technology, and social context, but also by onlookers (i.e., actors for whom the use is visible, but who are not directly involved in the activities of use themselves). Building on the “technology-in-practice” lens and insights of an ethnographic study in operating rooms where nurses used mobile technology for various work-related and recreational purposes, we show how onlookers contribute to structuring collective patterns of technology use. We concep-tualize their role as the onlooker effect, which means that onlookers’ inferences, judgments, and reactions trigger users to reflect on consequences and adjust the use in front of others, a phenomenon which is activated by the cues unintentionally given off when using technology. By identifying the role of onlookers in technology use, this study goes beyond user-centric and feature-centric perspectives on information technology use, illustrating that it does not happen in a physical vacuum, but often draws in unintended audiences. The onlooker effect provides a more in-depth explanation for unexpected patterns of technology use emerging in the workplace.

Despite the recognition of how important expertise coordination is to the performance of software development teams, understanding of how expertise is coordinated in practice is limited. We adopt a configural approach to develop a theoretical model of expertise coordination that differentiates between design collaboration and technical collaboration. We propose that neither a strictly centralized, top-down model nor a largely decentralized approach is superior. Our model is tested in a field study of 71 software development teams. We conclude that because design work addresses ill-structured problems with diverse potential solutions, decentralization of design collaboration can lead to greater coordination success and reduced team conflict. Conversely, technical work benefits from centralized collaboration. We find that task knowledge tacitness strengthens these relationships between collaboration configuration and coordination outcomes and that team conflict mediates the relationships. Our findings underline the need to differentiate between technical and design collaboration and point to the importance of certain configurations in reducing team conflict and increasing coordination success in software development teams. This paper opens up new research avenues to explore the collaborative mechanisms underlying knowledge team performance.

Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community’s design knowledge available for reuse for customization—or further extension of the metamodels, a kind of reuse for innovation.

Grounded theory methodology (GTM), with its espoused goal of theory development of novel phenomena, has found broad application in Information Systems (IS) research. To investigate how GTM is applied in IS research and how the research contributions are contingent on those applications, we review 43 GTM-based articles in major IS and related journals. Ten of the articles develop theory. The other 33 articles use GTM to develop models and rich descriptions of new phenomena as their theoretical contribution. We show that each of the three forms is valuable to the IS community. For example, studies that develop theories and models are highly cited in the IS literature. We identify nine GTM procedures that are applied in various combinations to develop the three forms of research contribution. Treating GTM as a portfolio of the nine procedures, we examine the implications for the research contribution of adopting the core GTM procedures compared with a partial portfolio of those procedures.

Digital ventures, start-ups growing by drawing on and adding to digital infrastructures, can scale their business at an unprecedented pace. We view such rapid scaling as a generative process by which a venture’s user base increases significantly between two points in time through digital innovation. We studied WeCash, a Chinese digital venture, nearly doubling its user base monthly, to learn more about this generative process. We trace three contingent mechanisms underpinning rapid scaling: data-driven operation, instant release, and swift transformation. We explain these mechanisms and how they interact in the rapid scaling of digital ventures. The research offers an agency perspective on scaling of digital ventures that speaks to the digital innovation literature.

Rapid and pervasive digitization of innovation processes and outcomes has upended extant theories on innovation management by calling into question fundamental assumptions about the definitional boundaries for innovation, agency for innovation, and the relationship between innovation processes and outcomes. There is a critical need for novel theorizing on digital innovation management that does not rely on such assumptions and draws on the rich and rapidly emerging research on digital technologies. We offer suggestions for such theorizing in the form of four new theorizing logics, or elements, that are likely to be valuable in constructing more accurate explanations of innovation processes and outcomes in an increasingly digital world. These logics can open new avenues for researchers to contribute to this important area. Our suggestions in this paper, coupled with the six research notes included in the special issue on digital innovation management, seek to offer a broader foundation for reinventing innovation management research in a digital world.