Graduate School of Business


Doctor of Business Administration (DBA)


Gayle R. Jesse


Advanced Analytics, Machine Learning, Natural Gas, Data Warehouse, Training, Resources


Business | Computer Sciences


This qualitative single case study describes the effects of an advanced analytic and machine learning system (AAML) has on the transportation of natural gas pipelines and the causes for failure to fully utilize the advanced analytic and machine learning system. This study's guiding theory was the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Transformation Leadership. The factors for failure to fully utilize AAML systems were studied, and the factors that made the AAML system successful were also examined. Data were collected through participant interviews. This study indicates that the primary factors for failure to fully utilize AAML systems are training and resource allocation. The AAML system successfully increased the participants' productivity and analytical abilities by eliminating the many manual steps involved in producing reports and analyzing business conditions. The AAML system also allowed the organization to gather and analyze real-time data in a volume and manner that would have been impossible before the AAML system was installed. The leadership team brought about the AAML system's success through transformation leadership by encouraging creativity, spurring innovation while providing the proper funding, time, and personnel to support the AAML system.