Working with or next to each other? Boundary crossing in the field of information visualisation

Abstract

Due to the need to present information in a fast and attractive way, organizations are eager to use information visualisations. This study explores the collision between the different experts involved in the production of these visualisations using the model of trading zones supplemented with the learning mechanisms found in the boundary crossing literature. Results show that that there is not one single good solution to effective interdisciplinary cooperation in the field of information visualisation. Rather, all four types of cooperation that we distinguish -enforced, dominated, fractionated, and attuned -might work well, as long as they are adapted to the situation and the participants accept the constraints of the specific cooperation type they are engaged in. In any case the involved experts and initiators have to understand and incorporate approaches that enhance the cocreative, iterative nature of the production process. In surveying the different forms of collaboration we detect two major forms of trading zones: the one that encompasses the collaboration between an external client and a designer (external trading zone) and the trading zones within an organization between content producer and designer (internal trading zone). Both mechanisms of identifying each other's expertise and coordinating the different tasks in the production process seem beneficial for the production process.

Open-Source Trading Zones and Boundary Objects: Examining GitHub as a Space for Collaborating on “News”

“…That key finding underscores the challenge of uniting disparate groups around a boundary object (Smit et al, 2014;Star & Griesemer, 1989). For example, Lewis and Usher (2014) found that limited organizational support and a lack of sustained and enduring exchanges made it hard for Hacks/Hackers groups to maintain momentum.…”

“…Not all collaboration is useful, though. Indeed, some trading zones may prove unproductive as more time is spent negotiating meanings and frictions than advancing ideas (Smit et al, 2014).…”

“…This opens up opportunities for trade around the boundary object of 'news,' which can reshape and reify key material objects, normative processes, and professional discourses (Carlson, 2015;Lewis & Usher, 2016;Weber & Kosterich, 2018). Those efforts are most fruitful when the trade cohort is heterogeneous and active, though there are multiple structural obstacles to the success of trade and the sustainability of its outputs (Lindén, 2017;Smit et al, 2014;Weber & Kosterich, 2018). In light of this work, the following research questions are posed: RQ1: Who are the primary actors gathering around the object of news on GitHub?…”

Staying with the trouble of networks

“…For instance, taking the clustering of accounts not as a definitive classification of communicative user behavior and political stances, but leaving space for interpretative ambiguities and doubt in this encoding and analytical process constituted an area of conflict. Frictions in the collaboration derived in part from the separation of our analytical work and the journalists' investigative process (data analysis and content production did not go hand in hand, as discussed by Smit et al, 2014). The fact that the project started off with certain reporting hypotheses-i.e., the idea that This collection of tweets was not limited to retweets, but we broadened our sampling scope and included all kinds of tweets containing media references (hyperlinks).…”

The effect of interest and attitude on public comprehension of news with data visualization

“…The press no longer has the exclusivity of the news, but other media take center stage, which is why some authors are beginning to study the importance of combining data visualization with news with interaction, to attract more audiences (Freixa et al, 2021). Following this idea, these techniques have the ability to attract readers by integrating large amounts of data into the news (Schroeder, 2004;Smit et al, 2014), which makes it possible to understand complex news topics (Bakker et al, 2013).…”