Change Management and Technology: A Case of GenAI in Higher Education

Abstract

In the digital age, with the evolution of GenAI, change has become more rapid and multi-dimensional. Promoting the success of technology-related change initiatives has become an area to explore. Change management has been management-oriented, lacking holistic, interdisciplinary research and an understanding of technology and data, which has prevented it from being successfully applied to complex AI-related changes in organizations.

The purpose of this doctoral research was to explore methods to enhance the success of technology-related change. To answer the central research question, how do methods enhance technology-related change success, this research conducted a literature review of traditional change management frameworks, as well as an analysis of the complex inter-relations among (external) environment, organization, and technology, and links among various components, such as goal setting vs. technology. The researcher also conducted twenty interviews with executives, managers, and staff who worked in change and IT/IM capacities at a Canadian university. The findings from the interviews generated three themes: change success and failure factors, change challenges across different roles, and changes in education regarding ChatGPT. These findings helped fill gaps in the literature, link to Social Construction of Technology (SCOT) and Sensemaking theories, answer research questions, identify reasons for change failure, develop strategies to target ChatGPT in higher education, and enhance the success of ChatGPT change management implementation. The methods to enhance technology-related change success focused on understanding the new change phenomenon that GenAI brings and sensemaking, focusing on the people and understanding components. Future research should aim to rethink the relations between academic research and industrial practices to enhance a timely, holistic, and renewed understanding of GenAI and changes in an interdisciplinary and holistic manner. Graphs, concept maps, and tables have been developed to visualize the complexity of change, success and failure factors, and the gaps in understanding GenAI-induced changes.