Global Virtual teams performance data
Description
All the teams in the dataset follow the same support process of a similar business IT application. As the team are part of the same company, many of variables such as management support, organizational maturity, training standards and overall company culture are the same. The company is part large corporation (200 000+ employees) and is focused on aviation industry, overall the processes in the department are mature and long running, hence the influence of learning curve towards work contents should be largely exhausted (teams running in the same setting for +5 years). Therefore, the main differentiating variable is the level of virtuality and possible learning curve with adoption of virtual tools during forced virtuality. The scope of teamwork includes complete care of the Enterprise Resource Planning (ERP) system. The nature of work includes the full service of the company information system, covering the resolution of any reported issues or defects found by users working with the system. A usual roadmap of such support ticket includes initial triage of criticality and complexity, analysis, code development, internal testing, user acceptance testing, deployment and post-deployment monitoring. Research data were extracted from the main company ticketing system using the default reporting system of ServiceNow software. Extracted data include necessary identification details that measure over 150 selected variables and KPIs of each ticket (work item). Also, the technical nature of this extraction ensures high data quality and a unified measurement process for all examined tickets. Tickets with outlier values such as resolution time over one month were removed. Also, the work items that were not entirely up to the selected teams were removed (extensive interaction with a third party). In total 59 267 individual work items from 58 teams consisting of 1 548 members were extracted and analysed to create the research database. The research data were gathered from an extensive period of January 2018 to December 2020 to ensure that the dataset covers high exposure periods of the ERP system (year-end closing of financial ledgers and monthly/quarterly production planning). The company’s internal ticketing system ensures the quality of the dataset; therefore, it depicts fully verified metrics. Resolution time Mean time duration of work items for the team over quarter Communication ambiguity Mean number of updates per work items for the team over quarter Quality reworks Mean number of reworks per items for the team per quarter