Abstract:
In view of the increasingly competitive business world, prudent spending and cost recovery have become the driving
force for the optimal performance of large public organizations. This study, therefore, examined the cost-effectiveness
of a Large Energy Utility (LEU) in a Southern African country by exploring the relationship between extraction of
transactional customer data (that is, data on the servicing and repairing energy faults) and the Utility’s recurrent
expenditure (especially its technicians’ overtime bill). Using data mining, a large corpus of the LEU Area Centre (AC)
data was extracted to establish the relationship between transactional customer data extraction including capture and
the financial cost of the LEU (e.g., recurrent expenditure on overtime bill). Results indicate that incorrect extraction
and capturing of transactional customer service data has contributed significantly to the LEU’s escalating overtime
wage bill. The data also demonstrate that the correct extraction and capturing of transactional customer service data can
positively reduce the financial costs of this LEU. The paper demonstrates one of the few attempts to examine the
effects of correct data extraction and capture on the financial resources of struggling large public energy utility. Using
Resource Based Theory, the study also demonstrates how technicians’ feedback on incorrect transactions enhances the
measurement of inaccurate transactional data albeit a burgeoning overtime wage bill incentives.
