The Role of Task Uncertainty in IT Project Team Advice Networks

An organizational digital footprint for interruption management: a data-driven approach

Purpose

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

Design/methodology/approach

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

Findings

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

Originality/value

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.

Team learning in interdisciplinary research teams: antecedents and consequences

Purpose

Team learning is critical to interdisciplinary research teams (IDR teams) to use heterogeneous knowledge effectively. Nevertheless, team learning is rarely addressed in the IDR team literature. Also, few studies investigate the antecedents and consequences of team learning in IDR teams, leading to a lack of guidance for management practices. This study aims to investigate how team learning can be developed and how team learning influences team outcomes in IDR teams.

Design/methodology/approach

A questionnaire survey on 304 members of 37 IDR teams in a research university in China is conducted. Data are analyzed using a partial least square structural equation modeling.

Findings

The results support most hypotheses in general. For the antecedent variables, task interdependence, trust and constructive conflict positively affect team learning. For the outcome variables, team learning improves shared mental models, coordination quality and team performance significantly. Additionally, task uncertainty positively moderates the team learning-coordination quality relation and team learning-team performance relation. However, this paper does not find support for the moderating role of task uncertainty on the team learning-shared mental models relation.

Originality/value

To the best of the knowledge, this is the first study investigating the antecedents and consequences of team learning in IDR teams. A multidimensional measurement of team learning for the IDR team context is developed. This study investigates how team behavioral factors influence team learning and the effect of team learning on shared mental models, coordination quality and team performance. This study also explores the contingency role of task uncertainty in the effects of team learning.