Computational Approaches for Analyzing Latent Social Structures in Open Source Organizing

Open source software represents a novel form of organizing that leaves digital trace data for organizational researchers to analyze using computational methods. Computational social science has emerged as an important approach to understanding patterns that represent latent social structures in sociological, organizational, and technical phenomena. Within the context of open and digitalized collaboration the clearest manifestation of computational social science has been social network analysis. While social network analysis is a powerful approach for understanding social phenomena in terms of their latent relational social structure, the network lens does not capture the entirety of social structures. Procedural social structures undergirding recurrent patterns of action form another important element of latent social structure. Analyzing such structures requires alternative methods able to deal with history-dependent patterning of activities. Therefore, we investigate the concepts of latent relational and procedural structures, and discuss computational approaches for analyzing patterns and interdependencies among such structures

GROUNDED COMPUTATIONAL ANALYSIS: A HANDS-ON APPROACH TO ANALYSING DIGITAL INNOVATION

As socio-technical processes related to digital innovation are increasingly connected and distributed across geographical, organisational, and temporal boundaries, the methods we use to study them must be adapted to accommodate the greater detail and scope of the phenomenon. Specifically, there is a need to operationalise methods for generating inductive theory of distributed digital innovation from digital trace data. An emerging stream of IS research on computationally intensive inductive theorising lays the groundwork for such methods. This paper builds on this foundation to develop a hands-on approach to operationalising grounded theorizing in computational analysis of digital trace data. The paper first conceptualises trace data of digital innovation as a new research context before articulating an approach to operationalising grounded theory in computational analysis of digital innovation. The application of the grounded computational analysis approach is then briefly illustrated in the context of digital trace data from an online social network before possible directions for further research are laid out

Developing Theory Through Integrating Human and Machine Pattern Recognition

New forms of digital trace data are becoming ubiquitous. Traditional methods of qualitative research that aim at developing theory, however, are often overwhelmed by the sheer volume of such data. To remedy this situation, qualitative researchers can engage not only with digital traces, but also with computational tools that are increasingly able to model digital trace data in ways that support the process of developing theory. To facilitate such research, this paper crafts a research design framework based on the philosophical tradition of pragmatism, which provides intellectual tools for dealing with multifaceted digital trace data, and offers an abductive analysis approach suitable for leveraging both human and machine pattern recognition. This framework provides opportunities for researchers to engage with digital traces and computational tools in a way that is sensitive to qualitative researchers’ concerns about theory development. The paper concludes by showing how this framework puts human imaginative capacities at the center of the push for qualitative researchers to engage with computational tools and digital trace