Using longitudinal data of IT professionals' activities in the SAP Community Network, and the career histories of these professionals obtained from LinkedIn, we investigate the relationship between an individual's participation in Internet-enabled open knowledge communities and a major event of his/her career development: job-hopping. We measure individual participation in open knowledge communities by two dimensions of related activities: contribution and learning. We provide empirical evidence that contribution to knowledge communities leads to a higher likelihood of job-hopping, yet a greater amount of learning is associated with a higher probability of retention. We argue that the effect of contribution can be attributed to job market signaling and the effect of learning is primarily driven by enhanced job performance and career advancement within the current organization. A series of robustness tests were conducted to address the self-selection bias and to rule out some possible alternative explanations to these mechanisms. Our work contributes to the existing body of literature on networks of practice and provides supporting evidence that participation in these networks indeed leads to career benefits and status advancements. Additionally, our study takes the first step to fill the gap in the current literature on voluntary employee turnover that has so far ignored the impacts of employee participation in external knowledge communities, thus providing both theoretical and practical insights in the area of organizational research.
The existence and persistence of price dispersion for identical products in online markets have been welldocumented in the literature. Possible explanations of this price dispersion, derived mainly using hedonic price models, have seen only modest success. In this paper, we propose a competitive model based on online retailers' differentiation mainly in service provided and recognition enjoyed to explain price dispersion. Our exploratory empirical analyses, using cross-sectional data, demonstrate that the competitive model provides a better explanation of the association between prices and online retailers' service and recognition levels. In addition, our competitive model is able to explain observations that are seemingly inconsistent with the hedonic model such as the negative association between service and price. This paper contributes to the literature on price dispersion by offering a differentiation model that provides a good fit with data and by proposing a theory that explains previous counterintuitive observations of prices. Our model also helps an e-tailer to choose a desirable position in the competitive market.
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.
Despite much hype about electronic payments systems (EPSs), a 2004 survey establishes that close to 80% of between-business payments are still made using paper-based formats. We present a finite mixture logit model to predict likelihood of EPS adoption in business-to-business (B2B) settings. Our model simultaneously classifies firms into homogeneous segments based on firm-specific characteristics and estimates the model's coefficients relating predictor variables to EPS adoption decisions for each respective segment. While such models are increasingly making their presence felt in the marketing literature, we demonstrate their applicability to traditional information systems (IS) problems such as technology adoption. Using the finite mixture approach, we predict the likelihood of EPS adoption using a unique data set from a Fortune 100 company. We compare the finite mixture model with a variety of traditional approaches. We find that the finite mixture model fits the data better, controlling for the number of parameters estimated; that our explicit model-based segmentation leads to a better delineation of segments; and that it significantly improves the predictive accuracy in holdout samples. Practically, the proposed methodology can help business managers develop actionable segment-specific strategies for increasing EPS adoption by their business partners. We discuss how the methodology is potentially applicable to a wide variety of IS research.
To cut costs, companies have chosen to deliver a variety of service offerings online. However, the digital systems providing such services (e-service) have always been complemented with or supported by humanbased service (h-service). Whereas h-service has total costs that increase with the demand for services, e-service mainly requires a fixed investment upfront, which can be amortized over the totality of customers served. Considering the different nature of the costs of h-service and e-service and the heterogeneity of customer preferences for services, we derive the optimal mix of h-service and e-service for a service-providing company vis-à-vis its competitor. Our theoretical analysis finds the subgame-perfect Nash equilibria that determines the optimal positions in a duopoly setting. We further study the competitive dynamics of the system to examine how firms stay on the equilibrium paths. Using simulation, we investigate the effects of starting positions, small adjustments in h-service and/or e-service, and monotonic expansions of e-service on the final positioning and profits of the firms. Our results demonstrate that when firms follow a local best-reply strategy, they may end up in a position of low profitability, and when only monotonic expansions of e-service are allowed, both firms may end up overinvesting in e-service.
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.