Research summary: Innovation requires inventors to have both new knowledge and the ability to combine and configure knowledge (i.e., combinatory knowledge), and such knowledge may flow through networks. We argue that both combinatory knowledge and new knowledge are accessed through collaboration networks, but that inventors' abilities to access such knowledge depends on its location in the network. Combinatory knowledge transfers from direct contacts, but not easily from indirect contacts. In contrast, new knowledge transfers from both direct and indirect contacts, but is far more likely to be new and useful when it comes from indirect contacts. Exploring knowledge flows in 69,476 patents and 89,930 unique inventors reveals evidence that combinatory knowledge from direct contacts and new knowledge from indirect contacts significantly affects innovative performance.Managerial summary: Inventors often combine ideas to create innovations. To do this, they need ideas to combine and they need the ability to combine those ideas. Inventors can get ideas to combine as well as the ability to combine ideas through prior co-workers. Prior co-workers can share ideas that may be relevant for the inventor's project and can tell the inventor about other things that other people are working on, especially people the inventor may not know. This can help inventors easily learn about ideas from friends-of-friends. The ability to combine ideas, however, is much harder to pass on. Prior co-workers must carefully work with the inventor to teach him or her the complex processes of combining ideas. This means that it is very hard to learn how to combine knowledge from a friend-of-a-friend, but it may be possible to learn from prior co-workers. We explore this phenomenon in the social relationships of software inventors.

The theory of fuzzy logic is based on the notion of relative graded membership, as inspired by the processes of human perception and cognition. Lotfi A. Zadeh published his first famous research paper on fuzzy sets in 1965. Fuzzy logic can deal with information arising from computational perception and cognition, that is, uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic allows for the inclusion of vague human assessments in computing problems. Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. New computing methods based on fuzzy logic can be used in the development of intelligent systems for decision making, identification, pattern recognition, optimization, and control.Fuzzy logic is extremely useful for many people involved in research and development including engineers

E xternal financing is critical to ventures that do not have a revenue source but need to recruit employees, develop products, pay suppliers, and market their products/services. There is an increasing belief among entrepreneurs that electronic word-of-mouth (eWOM), specifically blog coverage, can aid in achieving venture capital financing. Conflicting findings reported by past studies examining eWOM make it unclear what to make of such beliefs of entrepreneurs. Even if there were generally agreed-upon results, a stream of literature indicates that because of the differences in traits between the prior investigated contexts and venture capital financing, the findings from the prior studies cannot be generalized to venture capital financing. Extant studies also fall short in examining the role of time and the status of entities generating eWOM in determining the influence of eWOM on decision making. To address this dearth of literature in a context that attracts billions of dollars every year, we investigate the effect of eWOM on venture capital financing. This study entails the challenging task of gathering data from hundreds of ventures along with other sources including VentureXpert, surveys, Google Blogsearch, Lexis-Nexis, and Archive.org.The key findings of our econometric analysis are that the impact of negative eWOM is greater than is the impact of positive eWOM and that the effect of eWOM on financing decreases with the progress through the financing stages. We also find that the eWOM of popular bloggers helps ventures in getting higher funding amounts and valuations. The empirical model used in this work accounts for inherent selection biases of entrepreneurs and venture capitalists, and we conduct numerous robustness checks for potential issues of endogeneity, selection bias, nonlinearities, and popularity cutoff for blogs. The findings have important implications for entrepreneurs and suggest ways by which entrepreneurs can take advantage of eWOM.

No abstract

No abstract

In this work, we followed the sentiment analysis literature, and used supervised learning methods, which take manually classified data (corpus) as input and automatically extract features (combination of words and parts of speech of words) for sentiment analysis (Dave et al. 2003; Ghose and Ipeirotis 2011; Pang et al. 2002; Shanahan et al. 2006). These supervised methods do not rely on manually or semi-manually constructed discriminant-word lexicons. Prior research has shown that supervised methods perform better than lexicon-based approaches for sentiment analysis (Chaovalit and Zhou 2005; Pang et al. 2002).

No abstract

For organizations to achieve the benefits of new IT systems their users must adopt and then actually use these new systems. Recent models help to articulate the potentially different explanations for why some users will adopt and then continue using new technologies, but these models have not explicitly incorporated IT-knowledge. This is particularly important in contexts where the user base may be non-IT professionals-i.e. the users may vary substantially in their basic IT-knowledge. We draw upon psychology to argue that that in situations where there is wide variance in actual IT-knowledge there will often exist U relationship between actual and self-perceived IT-knowledge such that the least knowledgeable believe themselves to be highly knowledgeable. We then draw upon individual level adoption theories to argue that users with high self-perceived IT-knowledge will be more likely to adopt new technologies and do so faster. We also draw upon individual level continuance theories to argue that users with low actual IT-knowledge will be more likely to discontinue using new technologies and do so faster. We test our expectations using a proprietary data set of 225 sales professionals in a large Indian pharmaceutical company that is testing a new CRM system. We find strong support for our hypotheses.