Aleksi Aaltonen

The ambivalent ontology of digital artifacts

Digital artifacts are embedded in wider and constantly shifting ecosystems such that they become increasingly editable, interactive, reprogrammable, and distributable. This state of flux and constant transfiguration renders the value and utility of these artifacts contingent on shifting webs of functional relations with other artifacts across specific contexts and organizations. By the same token, it apportions control over the development and use of these artifacts over a range of dispersed stakeholders and makes their management a complex technical and social undertaking. These ideas are illustrated with reference to (1) provenance and authenticity of digital documents within the overall context of archiving and social memory and (2) the content dynamics occasioned by the findability of content mediated by Internet search engines. We conclude that the steady change and transfiguration of digital artifacts signal a shift of epochal dimensions that calls for rethinking some of the inherited wisdom in IS research and practice.

Everything counts in large amounts: a critical realist case study on data-based production

Contemporary digital ecosystems produce vast amounts of data every day. The data are often no more than microscopic log entries generated by the elements of an information infrastructure or system. Although such records may represent a variety of things outside the system, their powers go beyond the capacity to carry semantic content. In this article, we harness critical realism to explain how such data come to matter in specific business operations. We analyse the production of an advertising audience from data tokens extracted from a telecommunications network. The research is based on an intensive case study of a mobile network operator that tries to turn its subscribers into an advertising audience. We identify three mechanisms that shape data-based production and three properties that characterize the underlying pool of data. The findings advance the understanding of many organizational settings that are centred on data processing.

Building Governance Capability in Online Social Production: Insights from Wikipedia

This article investigates a form of governance that makes online social production possible. Drawing on the concepts of capability and routine, we develop a dynamic, process-oriented view that departs from past research focused on static comparative analysis. We theorize that online social production systems develop a collective governance capability to steer the process of integrating distributed knowledge resources to the production of value. Governance mechanisms emerge from individual and collective learning that is made possible by new technology, and they evolve over time, as routines are developed to respond to new problems faced by a growing production system. Using Wikipedia as a paradigmatic example of online social production, we characterize governance as an evolving, enabling and embedded process and discuss implications for a dynamic theory of governance.

Coordination and Learning in Wikipedia: Revisiting the Dynamics of Exploitation and Exploration

The evolution of Wikipedia betrays an increasing reliance on policies and guidelines, signalling certain stabilisation in the knowledge making processes underlying the encyclopaedia. We interpret such a state of affairs as reflecting the need to provide a few principles and guidelines of coordination, in a context that has otherwise been marked by vast diversity, high membership turnover and the lack of traditional exploitative structures. Rather than reflecting bureaucratisation and a shift away from its constitutive principles, the consolidation of these coordinative mechanisms further embeds the distinctive profile of knowledge making processes characteristic of the online encyclopaedia. They reinforce the diversity of the collective (rather than individual capabilities and skills) as the primary source of knowledge and render the mechanisms of harvesting that diversity and assembling it to a reasonable knowledge output key means of social learning.

(How) Does Data-based Music Discovery Work?

This paper analyses a new type of business operations that mediate the production and consumption of music. Online environment has largely abolished constraints on the variety of music that can be economically distributed, but, at the same time, it reveals another problem. How do people learn what music items do they want to listen to? In the music industry, the product space consists of thousands of artists, songs and albums, and is expanding rapidly. More effective forms of music discovery could therefore create considerable new value by allowing people to listen to music that better matches their taste. We analyse data from Last.fm music discovery service that deploys a collaborative filtering recommender system and social media features to aid music discovery. The analysis finds evidence that the new form of music discovery is valuable to consumers, yet it is relatively less important than an opportunity to listen to music for free. The findings lead us to discuss how the nature of analytical problem and product space, consumer taste, and social media features shape the potential value of created by big data.