Comparing two approaches to team building: a performance measurement evaluation

Purpose – This paper reports a study that compares two different team-building approaches. The first one is the traditional top-down management approach. The second one is a hybrid bottom-up approach based on the consensus model. The aim of this paper is to determine which of the two approaches is the most effective across a number of performance measurements. Design/methodology/approach – A case study is presented using data collected and analysed from a small family-owned vehicle maintenance firm. A mixed methods approach to data collection is utilized, including participant observation, focus groups, survey questionnaires and organizational performance reports. A convenience sample of eight routine maintenance jobs was selected for team performance comparison purposes. The measures used for comparing the two team approaches were lead time, customer satisfaction ratings and employees’ satisfaction ratings. Findings – The teams assembled using the consensus approach performed better than those selected using the traditional top-down approach across all three performance measures, i.e. the jobs were completed faster and both customers and employees were more satisfied. Research limitations/implications – This is an exploratory case study limited to one small familyowned business and, as such, findings may not be generalizable. Practical implications – As an alternative to manager selected work teams, managers should involve and empower employees to select their own teams. This has the potential to offer benefits for both customers and employees of faster delivery times and increase satisfaction, as well as increase productivity for the firm. Originality/value – This is the first field study to compare the performance of a hybrid, bottom-up approach to team building with the performance of a more traditionalmanagement, top-down approach to teambuilding. It paves the way for a wider study to be conducted in the future to test the findings’ generalizability.