02104nas a2200205   4500008003900000022001400039245005800053210005800111260004200169300001000211490000600221520149300227653002301720653002401743653002801767653002501795653002101820100002101841856003601862       2012                          d  a1927-032100aPredicting Ecosystem Alliances Using Landscape Theory0 aPredicting Ecosystem Alliances Using Landscape Theory  aOttawabTalent First Networkc08/2012  a31-380 v23 aPrevious articles in the TIM Review have covered various aspects of the concept of business ecosystems, from the types of ecosystems to keystone strategy, to different member roles and value co-creation. While there is no dearth of suggested best practices that organizations should follow as ecosystem members, it can be difficult to apply these insights into actionable steps for them to take. This is especially true when the ecosystem members already have a prior history of cooperation or competition with each other, as opposed to where a new ecosystem is created.

Landscape theory, a political science approach to predicting coalition formation and strategic alliances, can be a useful complement to ecosystems studies by providing a tool to evaluate the best possible alliance options for an organization, given information about itself and the other companies in the system. As shown in the case study of mobile device manufacturers choosing platform providers in the mobile ecosystem, this tool is highly flexible and customizable, with more data providing a more accurate view of the alliances in the ecosystem. At the same time, with even basic parameters, companies can glean significant information about which coalitions will best serve their interest and overall standing within the ecosystem. 

This article shows the synergies between landscape theory and an ecosystems approach and offers a practical, actionable way in which to analyze individual member benefits.10aalliance formation10abusiness ecosystems10acoalition forming games10acoalition prediction10alandscape theory1 aSatsangi, Shruti  uhttp://timreview.ca/article/59701671nas a2200145   4500008004100000022001400041245007200055210006900127260004200196520117900238100002501417700002601442700002101468856003601489       2010                        eng d  a1913-610200aQ&A. How is social network analysis used in studies of open source?0 aQA How is social network analysis used in studies of open source  aOttawabTalent First Networkc02/20113 aSocial network analysis (SNA) can be used to study online communities, including free/libre open source software (F/LOSS) developer teams. SNA techniques provide insight into these communities and enable researchers to make predictions based on these insights. They can be used to model the nature and patterns of interactions that can be used as a predictor of group behaviour, trust, knowledge generation, and information diffusion (Crowston et al., 2010). SNA can also be used make predictions about other kinds of networks other than pure social networks, such as networks based on relationships between code artifacts.

In this article, we answer the question of how SNA has been used to study open source. We begin by describing social networks and how they can be deconstructed to examine the relationships between entities within them. Next, we discuss social networks within F/LOSS communities and describe how SNA gives insights into the various actors and groups acting within networks. Finally, we provide an overview of common SNA measures used to study open source, including examples of how they have been used to provide insights about F/LOSS communities. 1 aAilapperuma, Chulaka1 aMukunda, Senthilkumar1 aSatsangi, Shruti  uhttp://timreview.ca/article/420