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

User Generated Multi-Dimensional Classification in an Adaptive Network Library Interface

Classification can be thought of as defining subject matter classes, and assigning information bearing items (IBEs) to those classes as a way to support organization and retrieval of those IBEs. This corresponds to a Platonic view in which subjects reside in a world of abstractions, and real world IBEs are mapped to them (many-te-many) as accurately as possible

The Web Science Observatory

To understand and enable the evolution of the Web and to help address grand societal challenges, the Web must be observable at scale across space and time. That requires a globally distributed and collaborative Web Observatory

Theoretical frameworks for the study of structuring processes in group decision support systems

Most theoretical perspectives used to explain the use and effects of communication and decision support technologies assume someform of technological a&rminism. lnwnsisten&s in the research jindings have prompted theorists to reject the assumptions of technological determinism in favor of an emergent perspective. To date, only adaptive structuration theo y CAST) offers the promise of satisfying two requirements for exphnation based on an emergent perspective: recursivify and unique effects. The current article reviews the application of AST to the study of a relatively recent technology in the workplace--group decision support systems (GDSS). Next it discusses AST's chal- lenge to capture, dynamically and precisely, GDSS processes and outcomes. In response to these concerns, self-organizing systems theory (SOST) is reviewed and applied to problematic areas in GDSS research with the aim of advancing AST

Modeling 21st century project teams: docking workflow and knowledge network computational models

This paper reports on an attempt to integrate and extend two established computational organizational models\u2014SimVision\uae and Blanche\u2014to examine the co-evolution of workflow and knowledge networks in 21st century project teams. Traditionally, workflow in project teams has been modeled as sets of sequential and/or parallel activities each assigned to a responsible participant, organized in a fixed structure. In the spirit of Jay Galbraith\u2019s (1973) information processing view of organizations, exceptions\u2014situations in which participants lack the required knowledge to complete a task\u2014are referred up the hierarchy for resolution. However, recent developments in digital technologies have created the possibility to design project teams that are more flexible, self-organizing structures, in which exceptions can be resolved much more flexibly through knowledge networks that extend beyond the project or even the company boundaries. In addition to seeking resolution to exceptions up the hierarchy, members of project teams may be motivated to retrieve the necessary expertise from other knowledgeable members in the project team. Further, they may also retrieve information from non-human agents, such as knowledge repositories or databases, available to the project team. Theories, such as Transactive Memory, Public Goods, Social Exchange and Proximity may guide their choice of retrieving information from a specific project team member or database. This paper reports on a \u201cdocked\u201d computational model that can be used to generate and test hypotheses about the co-evolution of workflow and knowledge networks of these 21st century project teams in terms of their knowledge distribution and performance. The two computational models being docked are SimVision (Jin & Levitt, 1999) which has sophisticated processes to model organizations executing project-oriented workflows, and Blanche (Hyatt, Contractor, & Jones, 1997), a multiagent computational network environment, which models multitheoretical mechanisms for the retrieval and allocation of information in knowledge networks involving human and non-human agents. This paper was supported in part by a grant from the U.S. National Science Foundation for the project \u201cCo-Evolution of Knowledge Networks and 21st Century Organizational Forms (IIS- 9980109)

Expert recommendation based on social drivers, social network analysis, and semantic data representation

ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration

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