Effects of the Internal Control Environment on the Operational Performance of the 4 and 5-Star Hotels in Thailand

Abstract

The objectives of this research were 1) to study the important levels of the internal control environment of 4 and 5-star hotels in Thailand, 2) to study the operational performance of the 4 and 5-star hotels in Thailand, and 3) to study the effects of the internal control environment on the operational performance of the 4 and 5-star hotels in Thailand by using a quantitative research methodology. The samples were 400 hotel business administrators in Thailand. The research instrument was a questionnaire. The statistics used were frequency, percentage, mean, standard deviation, and multiple regression analysis.

            The research findings were as follows.

1. The hotel business administrators in Thailand paid attention to the importance of the effects of the internal control environment. The mean score of each aspect was organized from the highest to the lowest: honesty and morality, knowledge, skills and ability, policies and human resource administration methods, philosophies and administrators’ working styles, authority assignment and responsibility, organizational management structures, and participation of administration and examination committees. The opinions on all of the aspects were at a high level.
2. The operational performance of the hotel businesses was at a high level. The mean score of each aspect was organized from the highest to the lowest: the customer view, the learning and organizational growth view, the international process view, and the financial view.
3. The prediction of the operational performance of the hotel businesses by using the internal control environment showed that four variables influenced the operational performance of the hotel businesses. The four variables were policies and human resource administration methods, authority assignment and responsibility, knowledge, skills, and ability, and organizational management structures. They could be used to predict the operational performance by 23%.