addressed is whether different change management factors
present on AEC projects have an effect on the frequency of
resistance that is encountered on the individual project level.
And if so, this paper seeks to identify which change
management factors correspond to greater and lesser resistance,
which has practical application implications for change
practitioners within the AEC industry.
Hypothesis testing was conducted via one-way ANOVA and
Tukey post-hoc testing to determine whether resistance to
change was impacted based upon different change management
factors present on individual AEC projects. The hypotheses
were tested to determine whether the frequency of resistance
encountered was different based upon project characteristics
(scope, size, duration), project personnel characteristics (position
level, career stage), the organization's expectations
(change implementation speed, organizational shift required),
and implementation approach (change message delivery,
establishment of formal change agents, and level of change
agent involvement in project-level change implementation).
Ten hypotheses are defined in Table 3. The following sections
will cover the data sample demographics, method of data
collection and analysis, results, and findings, and discuss
lessons for change practitioners along with conclusions and
recommendations for future research.