In today's dynamic business environment, companies are under tremendous pressure to become more innovative and maintain a steady stream of ideas that can lead to new and improved products and services. Companies have begun to explore the possibility of capturing consumers' "collective intelligence" by establishing firm-sponsored online brainstorming sites where individuals can share their ideas and offer comments on the ideas contributed by others. We term these sites "Company-Sponsored Online Co-Creation Brainstorming" (COCB). The value of this open and voluntary co-creation depends largely on members' contribution levels, the quality of the contributions, and sustained participation. In this paper, utilizing Zwass's taxonomy of co-creation value as a base, we structure a taxonomic framework of COCBs and an accompanying basic model of COCBs. We then present a series of hypotheses concerning the relationships between the model's various factors and specific COCB activities. We validate the model using empirical data collected over two and a half years, starting from the initiation of a pioneering company-sponsored online brainstorming site. Our analyses demonstrate that the level of peer feedback and the responsiveness (speed) of sponsor company feedback have significant influences on both members' contribution levels and duration of active participation. The sponsoring company's feedback, however, seems to influence only the quality of member's contribution level. On the practical side, the outcomes suggest that sponsoring companies should develop efficient processes for reviewing and responding to submitted ideas. Regarding theory, our findings provide an initial piece of contextualized research that offers implications for theory building in the COCB context, most notably the identification of key relationships between feedback (both peer and company) and participant activity levels and duration of participation.
In the work presented here, we develop and apply preference markets in evaluating early stage technology. Partnering with a Fortune 5 company, we developed and implemented two internal preference markets (field experiments). In both cases, nonmonetary (play money) incentives were utilized, but one market provided additional nonmonetary (play money) incentives. Working with the partner company, our investigation started with seven emerging technologies and expanded to a total of 17 emerging technologies. Our results suggest that even a simple form of additional nonmonetary play money incentive yielded greater price convergence, increased spread across final market prices, and greater consistency with a costly expert panel that was set up by the partner company. Based on the outcomes of our analyses, the partner company is investing in developing extended applications of preference markets as a potentially scalable approach for dealing with its ongoing and expanding strategic identification of promising emerging technologies.
Advances in online technologies and bandwidth availability have opened new vistas for online distribution of digital goods, but potential benefits for consumers are juxtaposed against challenges for retailers. Here, we investigate one type of digital experience good--music--whose market environment includes the very real presence of online piracy. Although arguments abound for and against online distribution of such digital goods, little research exists in this area. We develop a model of consumer search for such an experience good, and study different emerging market environments for retailers, where consumers can pirate music online. Retailer cost to publishers is modeled using a variety of licensing schemas. Survey results, together with data from online sharing networks, are utilized to validate a key assumption. Finally, computational analysis is used to develop insights that cannot be obtained analytically. Our results indicate that decreasing piracy is not necessarily equivalent to increasing profit, and online selling strategies can provide additional profits for a traditional retailer even in the presence of piracy. We show that leading strategies for business in such goods should include pricing options, provision of efficient search tools, and new licensing structures.
The need for timely information in the e-business world provides the impetus to develop a flexible database system with the capability to adapt and maintain performance levels under changing queries and changing business environments. Recognizing the importance of providing fast access to a variety of read-only applications in today's e-business world, we introduce the systems architecture for developing and implementing a flexible database system to achieve considerable gains in processing times of read queries. The key component of a flexible database system is query mining, the concept of determining relationships among query properties, alternative database structures, and query processing times. We validate the flexible database system concept through extensive laboratory experiments, where we embed learning tools to demonstrate the implementation of query mining.
Insider trading and asymmetric information have been the subject of a significant body of research since the 1960s. Yet little work has been directed at analyzing the impact of different market regulations. Along with difficulties in correctly identifying trades made on inside information, empirical field study methods have not been capable of analyzing the impact of different market regulations. The authors develop a controllable networked market trading environment that incorporates accurate identification of information possessed by each trader studied and that provides the flexibility necessary to analyze market impacts of different regulatory schemes to limit trading on inside information. The authors illustrate their methods through a series of controlled induced-value laboratory experiments using human subjects. Subject rewards are performance-based, with cash incentives tied to the outcomes of each market transaction. Experimental results indicate that markets with inside, privately informed traders led to greater trading volumes than markets with traders having access to private information only. In addition to reporting the results of initial sets of the experiments, the authors use these outcomes to frame future research issues involving the use of IT systems in surveillance and links between trading patterns and insider activity.