Collecting experimental network data from interventions on critical links in workplace networks

Highlights

• We illustrate the use of a dyadic intervention method within the framework of social network data collection.

• We report on challenges of getting access and designing participatory network data collection.

• We discuss opportunities of network alteration methods for identifying causality in social network research.

• We propose recommendations for gathering data from social network interventions in business settings.

Abstract

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.

Introduction

Almost every day at work, we organize and are subjected to network interventions. Teams are formed, organizations are merged and restructured, staff members are relocated, meetings are called to introduce staff members to each other, and office spaces are redesigned. We are expected to participate in drinks after work and team building events (Coburn, 2016). These activities are conducted with the explicit or implicit aim of manipulating our networks to improve workplace effectiveness, efficiency, collaboration, and cohesion. Network alteration preoccupies management (even if it is not usually thought about in those terms), and is a part of our everyday work experience.

In contrast to everyday practice, deliberate network alteration is still rarely a part of social network data collection (Valente, 2012). Although the idea of using sociometry for action research “in situ” goes back at least to Moreno (1951), apart from studies of online communication and experiments in virtual settings, academic social network researchers have still relatively little experience with collecting real-world interventionist data. Long after Moreno’s days, deliberate network alternation has been taken up mainly by non-academic consultancy companies promising to improve organisational efficacy and collaboration and to unblock bottlenecks in information flows. The academic community of social network researchers seems to have less knowhow and practice to get access to and maintain trust of partner organizations and, importantly, their employees. While there is no shortage of consultancies that market organizational network services, few network interventions are recorded with scientific rigor. There seem to be no well-documented repeatable protocols and rigorous procedures on how to gather interventionist network data of offline interactions in an ethical and reciprocal way so that the researchers are not the only ones benefitting from the exercise, and the data validly reflects issues that really matter to the participants. To get access and make a valued practical contribution, we need more awareness and understanding on how to conduct real-world network interventions in a style that makes the participants feel like respected professionals whose choices and wellbeing are valued, not like they are being manipulated for someone else’s data needs or motives of the management.

Because we are ill equipped for collecting network data in complex professional settings while the observed networks are being deliberately changed, we do not understand the specific effects of professional networking, team-building, and organizational restructuring activities. While fragmented networks at work can cause distress and wasted productivity, measures taken to reshape workplace social networks are often not backed by research evidence. We do not know whether some well-intentioned initiatives could be counterproductive because we seldom collect data on how networks respond to interventions. We even lack evidence on whether the desired social network structures would lead to desired outcomes. Although managers may implement policies to centralize or decentralize networks in organizations, it is hard to say which structures are more effective without evidence, or with evidence provided only from observational studies. More of networking activities is not always better.

From an academic viewpoint, interventionist network data collection is a means to explore causal network mechanisms that can be only partially inferred from observational studies. Changing certain aspects of networks and observing the change in network outcomes helps us understand network consequences. To test network theories and to provide practical managerial implications, we need data capturing causal processes on networks. However, causality, in its strict sense, is difficult to test in research of real-world social networks without exogenously intervening in their specific aspects. In an even stricter sense, causal effects of exogenous interventions are impossible to establish without controlled trials over a large number of networks (randomized at the level of networks), because interactive and emergent properties mean that the entire network should be considered as “treated” by an intervention to any of its components. To this date, feasible tools and protocols for interventionist network data collection remain largely underexplored and undocumented.

The goal of this article is to enhance the toolbox of experimental social network data gathering methods through examination of what worked and what did not work during a social network intervention aimed at altering specific dyads in a workplace context. To strengthen critical relationships in expressive communication networks in a large organization in Australia, we applied a well-described, well-tested, and repeatable procedure that had been shown to increase interpersonal closeness in studies in other fields; a facilitated personal self-disclosure technique (Aron et al., 1997). The practical aim of this article is to explore the challenges associated with implementation of such method in combination with network research and, based on these lessons, propose recommendations for conducting general and dyad-targeting social network interventions for experimental network data gathering in business settings.