QUALITATIVE INVESTIGATION OF TAM
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aspects of CRM implementations or underemphasize the negative aspects of those
implementations.
There are also some limitations concerning the temporal context. Throughout the course
of this research, significant advancements have been made in the nature of the technology,
particularly through the inclusion of artificial intelligence capabilities that are being added to
CRM technologies at a rapid rate. This could lead to potential new challenges or opportunities
for CRM adoption that could impact the relevance of these findings in the future. The qualitative
nature of the data also places limits on the ability to quantify relationships or establish greater
causation between variables. When binary or other limited choices were available, this research
explored those possibilities. However, a multi-method design that also examines quantitative
measures could offer a more complete understanding of CRM use cases, adoption patterns, and
TAM principles.
Future Research
Given the delimitations and limitations, there are significant opportunities for future
research. First, a longitudinal study to better examine changes in CRM adoption patterns could
potentially capture more accurately the evolving challenges faced by the CES. This is
particularly true given the prevalence of artificial intelligence technologies being added to
modern CRM systems. Additionally, future research could focus on comparative studies on a
state-by-state basis to better contextualize the adoption of CRM in individual states aligned with
strategic organizational needs. Throughout this research, participants identified various perceived
purposes of adopting CRM technologies within those states, which may have made it challenging
to understand CRM in a broader system-wide context. Future research could also consider an
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