Integrating social media tools in a web portal

Benefitting from Web 2.0 features, Social Media allows organisations to be where the users are, creating proximity, talking to them, and knowing what they want. Going viral and word-of-mouth become easier, as these platforms allow us to share, to like, and to use multimedia and convergence – as they can interact with each other, communicating on a large scale. Given that online portals provide for a highly competitive environment, players strive to get more visits, better search rankings, and even aspire to be the homepage for the Web universe. We discuss the integration of Social Media tools in a Web Portal, and explore how using these together may improve the competitiveness of a Web Portal. A large Web Portal was selected to develop this case study. We found that, although for this particular Web Portal conditions were created to accommodate and integrate the chosen Social Media platforms, this was done in an organic and fluid way, with great focus on community construction and less focus on absorptive capacity. Based on the findings of this case study, we propose a dynamic cycle of benefits for integrating Social Media tools in a Web Portal.

Team performance according to ball possession characteristics : a social networks approach

Over the last few years, football entered in a period of accelerated access to large amount of match analysis data. Social networks have been adopted to reveal the structure and organization of the web of interactions, such as the players passing distribution tendencies. In this study we investigated the influence of ball possession characteristics in the competitive success of Spanish La Liga teams. The sample was composed by OPTA passing distribution raw data (n=269,055 passes) obtained from 380 matches involving all the 20 teams of the 2012/2013 season. Then, we generated 760 adjacency matrixes and their corresponding social networks using Node XL software. For each network we calculated three team performance measures to evaluate ball possession tendencies: graph density, average clustering and passing intensity. Three levels of competitive success were determined using two-step cluster analysis based on two input variables: the total points scored by each team and the scored per conceded goals ratio. Our analyses revealed significant differences between competitive performances on all the three team performance measures (p < .001). Bottom-ranked teams had less number of connected players (graph density) and triangulations (average clustering) than intermediate and top-ranked teams. However, all the three clusters diverged in terms of passing intensity, with top-ranked teams having higher number of passes per possession time, than intermediate and bottom-ranked teams. Finally, similarities and dissimilarities in team signatures of play between the 20 teams were displayed using Cohen’s effect size. In sum, findings suggest the competitive performance was influenced by the density and connectivity of the teams, mainly due to the way teams use their possession time to give intensity to their game.