No abstract

Abstract. Natural Language Processing techniques for text-mining and information retrieval are finding application in the analysis of many kinds of documentation, from technical documentation to World Wide Web. Particularly, Functional Analysis techniques are based on the extraction of the interactions between the entities described in the document: these interactions are expressed as Subject-Action-Object (SAO) triples (obtainable using a suitable syntactic parser) which represent a concept in its most synthesizing form. In this work, the techniques developed for a functional analysis of patents and their implementation in the PAT-Analyzer tool are presented. The same technique has been properly tailored and applied to the analysis of software requirements documents. Current work in the direction of the development of a SAO-based Content Analysis of technical documentation is presented.

No abstract

No abstract

It is known that wherever there is human interaction, there is social influence. Here, we refer to more influential individuals as “influencers”, who drive team processes for better or worst. Social influence gives rise to social learning, the propensity of humans to mimic the most influential individuals. As individual learning is affected by the presence of an influencer, so is an individual's idea generation . Examining this phenomenon through a series of human studies would require an enormous amount of time to study both individual and team behaviors that affect design outcomes. Hence, this paper provides an agent-based approach to study the effect of influencers during idea generation. This model is supported by the results of two empirical experiments which validate the assumptions and sustain the logic implemented in the model. The results of the model simulation make it possible to examine the impact of influencers on design outcomes, assessed in the form of exploration of design solution space and quality of the solution. The results show that teams with a few prominent influencers generate solutions with limited diversity. Moreover, during idea generation, the behavior of the teams with uniform distribution of influence is regulated by their team members' self-efficacy.