The structural validity of the innovative work behaviour questionnaire: Comparing competing factorial models
The Southern African Journal of Entrepreneurship and Small Business Management
Innovation is about central organisational sustainability and is fundamentally centred in individuals.Objectives: Understanding and building theory on innovative work behaviour (IWB), as well as the parallel measurement thereof, is a prerequisite to the development of models for enhancing IWB. Most theorists propose IWB as a sequential process involving steps such as exploration, generativity, investigation, championing and application. These steps are also reflected in the design of IWB
... esign of IWB measurements. In this study, the theorised step-structure of IWB, as proposed by Kleysen and Street in 2001, is tested – relying on general descriptive statistics and applying exploratory and confirmatory factor analyses, with five different factorial structures tested.Methods: Complete records for more than 3000 respondents on the IWB measure were available. The results revealed that exploration and generativity occur more often than investigation, championing and application, alerting theorists to the dwindling effect of creative ideas and also to the hierarchical nature of the steps embedded in IWB. With regard to structure, the results revealed that the IWB steps were correlated, not orthogonal, and unlikely to be sequential as theorised. The initial steps of IWB (exploration and generativity) are therefore linked to the latter steps (investigation, championing and application), implying that employees are cognisant of the latter steps when engaging in the former.Results: The results of this study suggest reconsidering the segmented stepwise thinking regarding IWB. It also has important practical implications for stimulating IWB: Enabling individuals to manage the latter 'steps' of the IWB may well encourage the creativity and curiosity associated with the former 'steps'.Conclusion: The research provides important insights into the nature of IWB, informing theoretical models using data-driven information.