Communication and Performance across Time Zones: A Laboratory Experiment

We often hear that global knowledge work teams are affected by time zone differences, but most research in geographically dispersed collaboration has focused on the effects of distance and has treated time zones as a secondary factor. The experimental study we describe here is part of a larger research program aimed at understanding how technical teams coordinate their work across time zones and which factors influence performance. In this study we investigate how time zone differences affect team performance in a laboratory setting. The study is composed of three phases corresponding to different task type manipulations – simple, complex and equivocal task. In each of the study phases, dyadic teams were randomly assigned into 4 time zone (i.e., work time overlap) conditions: full overlap, 2/3 overlap, 1/3 overlap and no overlap. Teams performed a map drawing task simulating the assembly of software components. We completed data collection for 131 dyad teams. In each phase we collected the following data: team performance (speed and accuracy), exit survey, and chat log capture. In this paper we describe our research design, briefly discuss our preliminary results from analysis of data from Phase 1, and describe our expectations and next steps for the full study. In Phase 1 we found that time separation has a negative effect on accuracy. We also found that a small amount of time separation has a negative effect on production speed but, surprisingly, speed actually increases with further increases in time separation – a “U” shaped curve. Our chat log text analysis also revealed differences in communication patterns across time zone conditions, which helps explain the unanticipated results. To evaluate if the simplicity of our task influenced our results in Phase 1 we manipulated the task in Phase 2 (added complexity) and Phase 3 (more equivocal). Expected results and implications from these subsequent phases are discussed at the end

Team Knowledge Networks, Task Dependencies and Coordination: Preliminary Findings from Software Teams

Today’s work increasingly involves teams with fluid boundaries, and members working on multiple projects at a time. To understand how work is effectively coordinated in such complex organizations, we focus on the role of a company’s task dependency network. We integrate three research streams – coordination, team knowledge and social networks to conceptualize multiteam work as a large collaboration with members in multiple functional roles and areas of expertise, with complex task dependency relationships, operating as a coherent and well-coordinated knowledge network. Through this integration and empirical test of associated hypotheses with data from a European software company, our study illustrates how to represent multiple relationships in one complex multiplex network. This extends our understanding of how the various knowledge relationships and individual attribute differences influence the effective coordination in collaborative software development work. We address the concepts of awareness and shared familiarity and how they affect coordination, while keeping our focus on illustrating the power of network analytics to gain nuanced insights into the drivers of effective coordination

Understanding Shared Familiarity and Team Performance through Network Analytics

In this article, we propose a network approach to understanding team knowledge with archival data, offering conceptual and methodological advantages. Often, the degree to which team members’ possess shared knowledge has been conceptualized and measured as an aggregate property of a team as a whole. Rather than an aggregate property, however, we argue that shared team knowledge is more appropriately conceptualized as a network of knowledge overlaps or linkages between sets of team members. We created shared knowledge networks for a sample of 1,942 software teams based on members’ prior experiences working with one another on different tasks and teams. We included metrics representing topological features of team shared knowledge networks within predictive models of team performance. Our results suggest that network patterning provides additional predictive power for explaining software development team performance over and above the effects of average level of knowledge similarity within a team

The Main and Interaction Effects of Process Rigor, Process Standardization, and Process Agility on System Performance in Distributed IS Development: An Ambidexterity Perspective

Information systems (IS) development is becoming increasingly more geographically dispersed. Although process rigor, process standardization, and process agility are generally believed to have a positive impact on software development, it has not been well understood how these process capabilities affect distributed IS development. More important, no prior research has investigated their interaction effects. Drawing upon prior literature on organizational ambidexterity, we hypothesize: positive main effects of process rigor, process standardization, and process agility; a positive interaction effect of process rigor and process agility; and a positive interaction effect of process standardization and process agility on system performance in distributed development. Our data analysis results support a positive main effect of the three process capabilities. We find a positive interaction effect of process rigor and process agility suggesting positive process ambidexterity of rigor and agility. Surprisingly, we find a negative interaction effect of process agility and process standardization suggesting negative process ambidexterity of agility and standardizatio

Big Data and Analytics: Issues and Challenges for the Past and Next Ten Years

In this paper we continue the minitrack series of papers recognizing issues and challenges identified in the field of Big Data and Analytics, from the past and going forward. As this field has evolved, it has begun to encompass other analytical regimes, notably AI/ML systems. In this paper we focus on two areas: continuing main issues for which some progress has been made and new and emerging issues which we believe form the basis for near-term and future research in Big Data and Analytics. The Bottom Line: Big Data and Analytics is healthy, is growing in scope and evolving in capability, and is finding applicability in more problem domains than ever before

Big Data Redux: New Issues and Challenges Moving Forward

As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics