Uncovering Latent Archetypes from Digital Trace Sequences: An Analytical Method and Empirical Example

The widespread availability of digital trace data provides new opportunities for researchers to understand human behaviors at a large scale. Sequences of behavior, captured when individuals interface with an information system, can be analyzed to uncover behavioral trends and tendencies. Rather than assume homogeneity among actors, in this study we introduce a method for identifying subsets of the population which demonstrate similar behavioral trends. The objective of this analysis would be to identify a finite set of behavioral archetypes, which we define as distinct patterns of action displayed by unique subsets of a population. This study makes a contribution to the literature by introducing a novel methodology for analyzing sequences of digital traces. We apply our technique to data from a lab experiment featuring thirty twenty-person teams communicating over Skype

A Dynamic Sequence Model of Information Sharing Processes in Virtual Teams

Sharing information is a critical component of virtual team functioning. While prior research has identified the motivations for and the structure of information sharing, there has been little emphasis on the dynamic patterning of sharing behavior. In this study, we focus on the process of information sharing, namely the sequence and timing of individual decisions during a virtual team task. Further, we argue that sharing behaviors can be categorized into a finite number of approaches. We propose a temporal, event-based model to uncover the behavioral and cognitive factors that influence information sharing. With a sample of 600 participants organized into thirty ad hoc virtual teams, we demonstrate significant heterogeneity in sharing propensities. Our study makes two contributions to the extant literature. First, we extend theories regarding the motivation and structure of information sharing. Second, we make a broader methodological contribution with the application of a latent-class relational event model

Robust Optimization for Inference on Machine Learning Generated Variables

Leveraging supervised machine learning (SML) algorithms to operationalize constructs from unstructured data like text or images is becoming common in practice and research. As a result, variables generated through SML are used in regression models to make inferences and test theories. However, variables produced by SML will have measurement errors compared to the underlying construct. We propose using robust optimization to reduce the negative impact of these errors and produce less biased coefficient estimates while conducting more accurate hypothesis testing. To extend the burgeoning literature on this issue, our proposed method focuses on the generalized research setting where a flexible number of dependent and independent variables are measured by SML algorithms. We combine recent robust optimization techniques to fit a linear regression model in the presence of uncertain measurement error. We theoretically demonstrate the consistency and efficiency of the robust approach. Through simulations, we demonstrate the effectiveness of our approach

The Fluidity of an Individual’s Core-Periphery Position in Digital Knowledge Fields

The literature on digital knowledge fields suggests that knowledge coproducers are embedded in a core-periphery social structure. This structure engenders an individual-level tension: be in the core where there is support for successful knowledge integration, or be in the periphery where one can work outside of peer pressure. In this paper, we focus on the fluidity of core-periphery structures in digital knowledge fields. We study the case of nanoHUB, a digital, interdisciplinary knowledge field of nanoscience and engineering. We analyze 17,821 contributions made by 251 knowledge producers who coproduce 609 scientific simulation tools over a nearly ten-year period, encompassing over six million lines of code. We find that knowledge producers seek to resolve the core-periphery tension by moving towards and then away from the temporal core. Additionally, we find that proximity to the temporal core at the point of the knowledge production has a curvilinear relationship with code produced

Models as Social Actors in the Diffusion of AI Innovations: A Multilayer, Heterogeneous, Dynamic Network Perspective

Artificial Intelligence (AI) has emerged as a crucial facet of contemporary technological innovation, influencing diverse domains. Consequently, understanding the diffusion and evolution of AI innovations is vital. Scholarly publications have commonly served as proxies for studying these AI innovations. However, previous studies on publication diffusion have largely overlooked the role of models, which is particularly integral for AI innovations as they bridge upstream datasets and downstream applications. Moreover, models form an interdependent network due to their combinational evolution. This paper addresses this gap, examining how the location, movement, and speed of model movement in that model network affect the dissemination of AI research. Using a four-layer network—author collaborations, paper citations, model dependencies, and keyword co-occurrences—we examine 345,383 AI papers from 2000 to 2022. This research aims to contribute to the diffusion of innovation literature and dynamic network analysis, offering several novel insights and advancements

Preregistration of Information Systems Research

In this paper, we introduce the preregistration concept for experiments in the information systems (IS) discipline. Preregistration constitutes a way to commit to analytic steps before collecting or observing data and, thus, mitigate any biases authors may have (consciously or not) towards reporting significant findings. We explain why preregistration matters, how to preregister a study, the benefits of preregistration, and common arguments against preregistration. We also offer a call to action for authors to conduct more preregistered work in IS

Emotions in Microblogs and Information Diffusion: Evidence of a Curvilinear Relationship

How is emotional content shared on microblogging platforms? Prior work has proposed that emotionally charged content is diffused more than emotionally neutral content because it can evoke physiological arousal in platform users. Drawing on recent research in IS, we argue that the real relationship between emotions and Information Diffusion is an inverse U-shaped relationship; moderately strong emotions lead to optimal diffusion. We further theorize that this relationship is moderated by discourse context and valence of emotions. We test these hypotheses by testing a Twitter dataset that includes tweets collected from multiple conversation contexts. Results show broad support for our hypotheses and extend prior work on emotional content in microblogging

Vero: A Method for Remotely Studying Human-AI Collaboration

Despite the recognized need in the IS community to prepare for a future of human-AI collaboration, the technical skills necessary to develop and deploy AI systems are considerable, making such research difficult to perform without specialized knowledge. To make human-AI collaboration research more accessible, we developed a novel experimental method that combines a video conferencing platform, controlled content, and Wizard of Oz methods to simulate a group interaction with an AI teammate. Through a case study, we demonstrate the flexibility and ease of deployment of this approach. We also provide evidence that the method creates a highly believable experience of interacting with an AI agent. By detailing this method, we hope that multidisciplinary researchers can replicate it to more easily answer questions that will inform the design and development of future human-AI collaboration technologies

OPEN COMMUNITY HEALTH: WORKSHOP REPORT

This report summarizes key outcomes from a workshop on open community health conducted at the University of Nebraska at Omaha in April 2018. Workshop members represented research and practice communities across Citizen Science, Open Source, and Wikipedia. The outcomes from the workshop include (1) comparisons among these communities, (2) how a shared understanding and assessment of open community health can be developed, and (3) a taxonomical comparison to begin a conversation between these communities that have developed disparate languages