PhD candidate in Industrial Engineering & Management Sciences, Northwestern University
The role of social media in both enhancing and inhibiting the efficacy of organizational work teams.
Today’s digital world has shaken the foundation of organizational structure, shifting the traditional functional hierarchical structure of organizations to one that is implemented around networks of teams. According to a Deloitte 2016 Human Capital Trends report, the number one issue on leaders’ minds is redesigning the way we work in teams to enable these networks of teams to coordinate their activities, share information and work together effectively. Meanwhile, the rapid proliferation of social media technologies is offering unprecedented opportunities for people to post, reach, and transmit information with diverse groups of people in a visible manner that persists over time. These advances in digital tools offer new ways for people to communicate and collaborate within and across teams that can facilitate ease of coordination, information sharing and teamwork. Yet at the same time, this persistent visibility and awareness of content and activity may lead to attention allocation problems in the workplace. Due to the potential for information overload, team members may develop strategies to manage their hyper awareness and selective unawareness to social media. Thus, an important question that one might ask is how should team members allocate their attention so that they are able to balance their abundant awareness with selective unawareness?
The availability of server-side and digital trace data of people’s interactions and communication with each other provide a novel way to address this important question. In my current research, I leverage a unique data set from a multi-city online real estate company that uses Slack, a widely used team-based communication platform, to organize their internal communications within the firm. Using a multi-method approach that involves social network analysis, survey methods, and regression, I first examine how team members strategically regulate information flow through the use of different heuristics. Then, I examine the relationship between each of these strategies and productivity, to determine how team members can optimally configure their memberships in team networks to promote more effective teamwork and performance outcomes.
Ultimately, my goal is to seek an improved understanding of how organizations are structuring their work around networks of teams, and how team members can take advantage of the widespread availability of social media technologies to accomplish their work effectively.
Abstract: Research from a variety of fields including psychology and linguistics have found correlations and patterns in personal attributes and behavior, but efforts to understand the broader heterogeneity in human behavior have not yet integrated these approaches and perspectives with a cohesive methodology. Here we extract patterns in behavior and relate those patterns together in a high-dimensional picture. We use dimension reduction to analyze word usage in text data from the online discussion platform Reddit. We find that pronouns can be used to characterize the space of the two most prominent dimensions that capture the greatest differences in word usage, even though pronouns were not included in the determination of those dimensions. These patterns overlap with patterns of topics of discussion to reveal relationships between pronouns and topics that can describe the user population. This analysis corroborates findings from past research that have identified word use differences across populations and synthesizes them relative to one another. We believe this is a step toward understanding how differences between people are related to each other.
Pub.: 09 Aug '17, Pinned: 31 Aug '17
Abstract: No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect the outcomes of other units, a phenomenon known as interference. This thesis develops a causal framework and inference methodology for experiments where interference takes place on a network of influence (i.e. network interference). In this framework, the network potential outcomes serve as the key quantity and flexible building blocks for causal estimands that represent a variety of primary, peer, and total treatment effects. These causal estimands are estimated via principled Bayesian imputation of missing outcomes. The theory on the unconfoundedness assumptions leading to simplified imputation highlights the importance of including relevant network covariates in the potential outcome model. Additionally, experimental designs that result in balanced covariates and sizes across treatment exposure groups further improve the causal estimate, especially by mitigating potential outcome model mis-specification. The true potential outcome model is not typically known in real-world experiments, so the best practice is to account for interference and confounding network covariates through both balanced designs and model-based imputation. A full factorial simulated experiment is formulated to demonstrate this principle by comparing performance across different randomization schemes during the design phase and estimators during the analysis phase, under varying network topology and true potential outcome models. Overall, this thesis asserts that interference is not just a nuisance for analysis but rather an opportunity for quantifying and leveraging peer effects in real-world experiments.
Pub.: 28 Aug '17, Pinned: 31 Aug '17
Abstract: Despite rigorous empirical research exploring the changes in innovation dynamics triggered by Social Media Networks (SMNs), the benefits coming from the use of these digital platforms for knowledge search in innovative activities for small to medium enterprises (SMEs) are still unexplored. Customers become the new trailblazers. Thus, by adopting a customer led innovation perspective, this paper seeks to measure the effect on return on investment (ROI) of the use of SMNs as external drivers for supporting internal innovation search processes. On the basis of the extant literature on information system and social network analysis, the research describes and evaluates the multidimensional activities interwoven into the open innovation process, driven by integrating the five constructs of structural dimension, relational behaviour, cognitive dimension, knowledge transfer, and legitimization into our hypothesised conceptual model.
Pub.: 30 Mar '17, Pinned: 31 Aug '17
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