Search results

This chapter critically discusses implications of working with ‘big data’ from the perspective of qualitative research and methodology. A critique is developed of the analytic troubles that come with integrating qualitative methodologies with ‘big data’ analyses and, moreover, the ways in which qualitative traditions themselves offer a challenge, as well as contributions, to computational social science.

The chapter draws on Interactionist understandings of social organisation as an ongoing production, tied to and accomplished in the actual practices of actual people. This is a matter of analytic priority but also points to a distinctiveness of sociological work which may be undermined in moving from the study of such actualities, suggesting an alternative coming crisis of empirical sociology.

A cautionary tale is offered regarding the contribution and character of sociological analysis within the ‘digital turn’. It is suggested that ‘big data’ analyses of traces abstracted from actual people and their practices not only miss and distort the relation of social practice to social product but, consequentially, can take on an ideological character.

The chapter offers an original contribution to current discussions and debates surrounding ‘big data’ by developing enduring critiques of sociological methodology and analysis. It concludes by pointing to contributions and interventions that such an empirical programme of qualitative research might make in the context of the ‘digital turn’ and is of value to those working at the interface of traditional and digital(ised) inquiries and methods.

The chapter will review significant changes in information technology (IT) affecting research over the 30-year history of Communication, Information Technology, and Media Sociology. It compares broad overviews of computers and the social sciences published shortly after the beginning of the section (1989 and 1990) with a contemporary overview of online research methods from 2017. It also draws on my own experiences from 1981 to the present as both an academic and a software entrepreneur. The author will discuss how changes in the section parallel developments in social science computing over this period, identifying some of the significant ways IT has transformed both the methods of research and the substantive foci of research. Finally, the author extrapolates into the future to consider how continuing changes in the Internet, big data, artificial intelligence, and natural language understanding may change how sociological research is conducted in the foreseeable future.

A century ago, the ancestors of modern computers were largely devoted to analysis of social data, but sociology and computer science diverged, and today they need to be reunited. This conceptual chapter argues for the development of an integrated social-information science, in order to understand and develop socio-technical information systems, to explore and extend recommender and reputation systems, to establish the cultural basis for flourishing virtual worlds, and to deal with revolutionary issues concerning intellectual property rights. It suggests that three forms of human–machine collaboration will become increasingly important: (1) partnerships between humans and information technology, (2) cultures jointly created by the human mind and information technology, and (3) environments where humans and machines cooperate.

The authors discuss the microfoundations of institutional theory, specifically as microfoundations are manifested in this volume of Research in the Sociology of Organizations. The authors argue that the main interest seems to be in better understanding macrofoundations: top-down forces from institutions to actors. Furthermore, throughout the volume institutions themselves are definitionally layered – in problematic ways – with a large array of other macroconstructs, including fields, logics, practices, habitus, situations, routines, and so forth. The authors argue that there is an opportunity to more carefully delineate microfoundations for institutional theory, by focusing on lower-level heterogeneity, agency, as well as the aggregate and emergent social processes that animate microfoundational explanation.

Research on organizational teamwork is increasingly highlighting the patterned nature of the relational processes (e.g., communication, backup behavior) and psychological states (e.g., trust, shared cognition) that underlie team effectiveness. However, studies of teams often rely on methodologies that do not explicitly assess the underlying patterns of relational processes and states. Social network approaches offer an appealing alternative to the typical methodologies used in team research given that network approaches provide both the theory and methodology necessary to conceptualize and investigate patterns of interactions among group members. Despite the advantages of social network approaches, many team researchers are unfamiliar with the network paradigm and its associated methodologies. The purpose of this chapter is to clarify how networks can be leveraged to answer key research questions related to the study of team functioning and effectiveness. We begin by discussing the evolution and eventual convergence of team research and network approaches. Then, we examine the current state of the literature at the intersection of teams and networks in order to identify key takeaways and remaining questions. We conclude by highlighting opportunities for the future of team network science.

The aim of this paper is to offer agent‐based modelling (ABM) as an alternative approach to advance research in entrepreneurship. It argues that ABM allows entrepreneurship researchers (i.e. the designers) to find better ways in generating entrepreneurial outcomes by understanding alternative histories and examining a plausible future.

This paper begins with an overview of ABM, and discusses the shared conceptual foundations of entrepreneurship and ABM as the motives for the adoption of ABM as an appropriate methodology to study entrepreneurship. It offers a roadmap in using ABM approach for entrepreneurship research and illustrates this using a contemporary research question in entrepreneurship: the study of success/failure in business venturing.

This paper suggests the shared foundations between ABM and entrepreneurship as the basis for bringing the methodology and research domain closer. It offers a roadmap for advancing entrepreneurship research using agent‐based simulation approach and explains the contribution of ABM to further advance entrepreneurship research.

This paper addresses the methodological gap in entrepreneurship research and develops the argument for a wider adoption of ABM simulation approach to study entrepreneurship. It bridges the gap by examining the possibility of formalizing entrepreneurship processes by grounding an agent‐based model on empirical facts and generally‐accepted foundations of entrepreneurship. It offers a contribution to the literature by showing that ABM is a useful and appropriate methodological approach for entrepreneurship research in addition to the conventional variance and process approach.

The purpose of this paper is to use agent-based modeling to simulate the negotiation and cooperation between agents working on tasks in an organization and to study the effects that gender differences might have on the outcome of the process.

The model used herein allows for idiosyncratic differences in terms of the propensity to negotiate/cooperate. The model also allows for multi-round negotiations/cooperation and incorporates subjective value into the negotiation process. The model is implemented in NetLogo.

The results clearly show that it is always beneficial to negotiate, even when backlash might result from the request. The study then extends this analysis by allowing for gender differences in both negotiation and cooperation. The results provide strong support for the hypothesis that agentic characteristics are beneficial for negotiators, while communal characteristics can be detrimental.

Like all models, the model used herein made some simplifying assumptions about the negotiation and cooperation processes. In addition, the utilized model assumes that agents work individually on tasks and that negotiation takes place between two individual agents, even though negotiation can be a team-based endeavor in many cases.

The results of this study indicate that individuals need to adopt characteristics that are more agentic; this finding is particularly true for females who aim to be on a level playing field with their male counterparts. The results also indicate that negotiation is beneficial whether there is an abundance of resources or not, while cooperation is only beneficial when resources are abundant.

While past negotiation research has used simple choice games, laboratory studies and field studies, this study provides computational support for the hypothesis that higher levels of negotiation are beneficial to individuals. Additionally, unlike recent agent-based studies that have studied negotiation as a taken-for-granted automated computational process that is done by software on behalf of individuals, the present study simulates agents that have yet to decide whether they will engage in negotiation or not.