Assessing Individuals' Resistance Prior to IT Implementation in the AEC Industry

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Conference Proceeding

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Ever increasing technological capabilities exist in the architecture/engineering/construction (AEC) industry. Email, project specific websites, Computer Aided Drafting (CAD), animations, and Building Information Modeling (BIM) are but a few information technologies adopted in recent years within the industry. The change methods used in the adoptions suggest a focus on technology, yet the technology itself is seen as a primary barrier to successful implementation.

In general, the AEC industry is extremely slow to embrace available information technology. Companies often have difficulty with technology implementations because technology is the driver of change, rather than an enabler of change. Resistance of people is the primary reason for failure of any organizational change, including an information technology change. Technological changes will be more successful when researchers develop a fundamental understanding of how people change. Studying individuals and their change processes is essential to improving implementation of technology change, yet change management theories present processes and guidelines for changing organizations and tasks with limited emphasis on individuals involved in change. This research uses a people centered paradigm for developing technology implementation models, placing technology in a change enabling position rather than being a driver of change.

This research investigates individuals’ resistance to change brought about by new information technology implementation in the AEC industry. Resistance to change is a combination of three factors: cause of resistance, level of resistance, and manifestation of resistance. Previous work investigated the importance of specific behavioral characteristics indicative of resistance to change and correlated these characteristics to the level of resistance in individuals. This paper discusses methodology continuing this work, which aims to confirm the previous work, as well as to develop and validate new predictive tools to identify potential resisters prior to an information technology change implementation. The results from analysis of preliminary data are also discussed.

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