An initial analysis of software engineers’ attitudes towards organizational change
2016 (English)In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, 1-27 p.Article in journal (Refereed) Epub ahead of print
Employees’ attitudes towards organizational change are a critical determinant in the change process. Researchers have therefore tried to determine what underlying concepts that affect them. These extensive efforts have resulted in the identification of several antecedents. However, no studies have been conducted in a software engineering context and the research has provided little information on the relative impact and importance of the identified concepts. In this study, we have combined results from previous social science research with results from software engineering research, and thereby identified three underlying concepts with an expected significant impact on software engineers’ attitudes towards organizational change, i.e. their knowledge about the intended change outcome, their understanding of the need for change, and their feelings of participation in the change process. The result of two separate multiple regression analysis, where we used industrial questionnaire data (N=56), showed that the attitude concept openness to change is predicted by all three concepts, while the attitude concept readiness for change is predicted by need for change and participation. Our research provides an empirical baseline to an important area of software engineering and the result can be a starting-point for future organizational change research. In addition, the proposed model prescribes practical directions for software engineering organizations to adopt in improving employees’ responses to change and, thus, increase the probability of a successful change.
Place, publisher, year, edition, pages
Springer, 2016. 1-27 p.
Attitudes, Behavioral software engineering, Human aspects, Openness to change, Organizational change, Readiness for change, Social psychology, Software engineering, Systematic literature review, Regression analysis, Social sciences computing
IdentifiersURN: urn:nbn:se:bth-13675DOI: 10.1007/s10664-016-9482-0ScopusID: 2-s2.0-85006172824OAI: oai:DiVA.org:bth-13675DiVA: diva2:1060798